SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, X; Gao, H; Sharp, G

2015-06-15

Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to eachmore » chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)« less

Adaptive optics using a MEMS deformable mirror for a segmented mirror telescope

NASA Astrophysics Data System (ADS)

Miyamura, Norihide

2017-09-01

For small satellite remote sensing missions, a large aperture telescope more than 400mm is required to realize less than 1m GSD observations. However, it is difficult or expensive to realize the large aperture telescope using a monolithic primary mirror with high surface accuracy. A segmented mirror telescope should be studied especially for small satellite missions. Generally, not only high accuracy of optical surface but also high accuracy of optical alignment is required for large aperture telescopes. For segmented mirror telescopes, the alignment is more difficult and more important. For conventional systems, the optical alignment is adjusted before launch to achieve desired imaging performance. However, it is difficult to adjust the alignment for large sized optics in high accuracy. Furthermore, thermal environment in orbit and vibration in a launch vehicle cause the misalignments of the optics. We are developing an adaptive optics system using a MEMS deformable mirror for an earth observing remote sensing sensor. An image based adaptive optics system compensates the misalignments and wavefront aberrations of optical elements using the deformable mirror by feedback of observed images. We propose the control algorithm of the deformable mirror for a segmented mirror telescope by using of observed image. The numerical simulation results and experimental results show that misalignment and wavefront aberration of the segmented mirror telescope are corrected and image quality is improved.

The whole mesh deformation model: a fast image segmentation method suitable for effective parallelization

NASA Astrophysics Data System (ADS)

Lenkiewicz, Przemyslaw; Pereira, Manuela; Freire, Mário M.; Fernandes, José

2013-12-01

In this article, we propose a novel image segmentation method called the whole mesh deformation (WMD) model, which aims at addressing the problems of modern medical imaging. Such problems have raised from the combination of several factors: (1) significant growth of medical image volumes sizes due to increasing capabilities of medical acquisition devices; (2) the will to increase the complexity of image processing algorithms in order to explore new functionality; (3) change in processor development and turn towards multi processing units instead of growing bus speeds and the number of operations per second of a single processing unit. Our solution is based on the concept of deformable models and is characterized by a very effective and precise segmentation capability. The proposed WMD model uses a volumetric mesh instead of a contour or a surface to represent the segmented shapes of interest, which allows exploiting more information in the image and obtaining results in shorter times, independently of image contents. The model also offers a good ability for topology changes and allows effective parallelization of workflow, which makes it a very good choice for large datasets. We present a precise model description, followed by experiments on artificial images and real medical data.

A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

NASA Astrophysics Data System (ADS)

Botter Martins, Samuel; Vallin Spina, Thiago; Yasuda, Clarissa; Falcão, Alexandre X.

2017-02-01

Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence tomography

NASA Astrophysics Data System (ADS)

Hidalgo-Aguirre, Maribel; Gitelman, Julian; Lesk, Mark Richard; Costantino, Santiago

2015-11-01

Optical coherence tomography (OCT) imaging has become a standard diagnostic tool in ophthalmology, providing essential information associated with various eye diseases. In order to investigate the dynamics of the ocular fundus, we present a simple and accurate automated algorithm to segment the inner limiting membrane in video-rate optic nerve head spectral domain (SD) OCT images. The method is based on morphological operations including a two-step contrast enhancement technique, proving to be very robust when dealing with low signal-to-noise ratio images and pathological eyes. An analysis algorithm was also developed to measure neuroretinal tissue deformation from the segmented retinal profiles. The performance of the algorithm is demonstrated, and deformation results are presented for healthy and glaucomatous eyes.

Primal/dual linear programming and statistical atlases for cartilage segmentation.

PubMed

Glocker, Ben; Komodakis, Nikos; Paragios, Nikos; Glaser, Christian; Tziritas, Georgios; Navab, Nassir

2007-01-01

In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively.

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

NASA Astrophysics Data System (ADS)

Das, Bipul; Saha, Punam K.; Wolf, Ronald; Song, Hee Kwon; Wright, Alexander C.; Wehrli, Felix W.

2005-04-01

Atherosclerotic cerebrovascular disease leads to formation of lipid-laden plaques that can form emboli when ruptured causing blockage to cerebral vessels. The clinical manifestation of this event sequence is stroke; a leading cause of disability and death. In vivo MR imaging provides detailed image of vascular architecture for the carotid artery making it suitable for analysis of morphological features. Assessing the status of carotid arteries that supplies blood to the brain is of primary interest to such investigations. Reproducible quantification of carotid artery dimensions in MR images is essential for plaque analysis. Manual segmentation being the only method presently makes it time consuming and sensitive to inter and intra observer variability. This paper presents a deformable model for lumen and vessel wall segmentation of carotid artery from MR images. The major challenges of carotid artery segmentation are (a) low signal-to-noise ratio, (b) background intensity inhomogeneity and (c) indistinct inner and/or outer vessel wall. We propose a new, effective object-class uncertainty based deformable model with additional features tailored toward this specific application. Object-class uncertainty optimally utilizes MR intensity characteristics of various anatomic entities that enable the snake to avert leakage through fuzzy boundaries. To strengthen the deformable model for this application, some other properties are attributed to it in the form of (1) fully arc-based deformation using a Gaussian model to maximally exploit vessel wall smoothness, (2) construction of a forbidden region for outer-wall segmentation to reduce interferences by prominent lumen features and (3) arc-based landmark for efficient user interaction. The algorithm has been tested upon T1- and PD- weighted images. Measures of lumen area and vessel wall area are computed from segmented data of 10 patient MR images and their accuracy and reproducibility are examined. These results correspond exceptionally well with manual segmentation completed by radiology experts. Reproducibility of the proposed method is estimated for both intra- and inter-operator studies.

Medial-based deformable models in nonconvex shape-spaces for medical image segmentation.

PubMed

McIntosh, Chris; Hamarneh, Ghassan

2012-01-01

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

Biomechanical Model for Computing Deformations for Whole-Body Image Registration: A Meshless Approach

PubMed Central

Li, Mao; Miller, Karol; Joldes, Grand Roman; Kikinis, Ron; Wittek, Adam

2016-01-01

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2-D models and computing single organ deformations. In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. PMID:26791945

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images

NASA Astrophysics Data System (ADS)

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K.; Yashar, Catheryn M.; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-01

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based ‘thin-plate-spline robust point matching’ algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images.

PubMed

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K; Yashar, Catheryn M; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-07

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based 'thin-plate-spline robust point matching' algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

A model-based approach for estimation of changes in lumbar segmental kinematics associated with alterations in trunk muscle forces.

PubMed

Shojaei, Iman; Arjmand, Navid; Meakin, Judith R; Bazrgari, Babak

2018-03-21

The kinematics information from imaging, if combined with optimization-based biomechanical models, may provide a unique platform for personalized assessment of trunk muscle forces (TMFs). Such a method, however, is feasible only if differences in lumbar spine kinematics due to differences in TMFs can be captured by the current imaging techniques. A finite element model of the spine within an optimization procedure was used to estimate segmental kinematics of lumbar spine associated with five different sets of TMFs. Each set of TMFs was associated with a hypothetical trunk neuromuscular strategy that optimized one aspect of lower back biomechanics. For each set of TMFs, the segmental kinematics of lumbar spine was estimated for a single static trunk flexed posture involving, respectively, 40° and 10° of thoracic and pelvic rotations. Minimum changes in the angular and translational deformations of a motion segment with alterations in TMFs ranged from 0° to 0.7° and 0 mm to 0.04 mm, respectively. Maximum changes in the angular and translational deformations of a motion segment with alterations in TMFs ranged from 2.4° to 7.6° and 0.11 mm to 0.39 mm, respectively. The differences in kinematics of lumbar segments between each combination of two sets of TMFs in 97% of cases for angular deformation and 55% of cases for translational deformation were within the reported accuracy of current imaging techniques. Therefore, it might be possible to use image-based kinematics of lumbar segments along with computational modeling for personalized assessment of TMFs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Atlas ranking and selection for automatic segmentation of the esophagus from CT scans

NASA Astrophysics Data System (ADS)

Yang, Jinzhong; Haas, Benjamin; Fang, Raymond; Beadle, Beth M.; Garden, Adam S.; Liao, Zhongxing; Zhang, Lifei; Balter, Peter; Court, Laurence

2017-12-01

In radiation treatment planning, the esophagus is an important organ-at-risk that should be spared in patients with head and neck cancer or thoracic cancer who undergo intensity-modulated radiation therapy. However, automatic segmentation of the esophagus from CT scans is extremely challenging because of the structure’s inconsistent intensity, low contrast against the surrounding tissues, complex and variable shape and location, and random air bubbles. The goal of this study is to develop an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation to the delineate esophagus automatically. We performed atlas selection in two phases. In the first phase, we used the correlation coefficient of the image content in a cubic region between each atlas and the new image to evaluate their similarity and to rank the atlases in an atlas pool. A subset of atlases based on this ranking was selected, and deformable image registration was performed to generate deformed contours and deformed images in the new image space. In the second phase of atlas selection, we used Kullback-Leibler divergence to measure the similarity of local-intensity histograms between the new image and each of the deformed images, and the measurements were used to rank the previously selected atlases. Deformed contours were overlapped sequentially, from the most to the least similar, and the overlap ratio was examined. We further identified a subset of optimal atlases by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach was validated with promising results using both internal data sets (21 head and neck cancer patients and 15 thoracic cancer patients) and external data sets (30 thoracic patients).

Probabilistic atlas and geometric variability estimation to drive tissue segmentation.

PubMed

Xu, Hao; Thirion, Bertrand; Allassonnière, Stéphanie

2014-09-10

Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, which presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modeled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas-based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets. Copyright © 2014 John Wiley & Sons, Ltd.

3D deformable image matching: a hierarchical approach over nested subspaces

NASA Astrophysics Data System (ADS)

Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

2000-06-01

This paper presents a fast hierarchical method to perform dense deformable inter-subject matching of 3D MR Images of the brain. To recover the complex morphological variations in neuroanatomy, a hierarchy of 3D deformations fields is estimated, by minimizing a global energy function over a sequence of nested subspaces. The nested subspaces, generated from a single scaling function, consist of deformation fields constrained at different scales. The highly non linear energy function, describing the interactions between the target and the source images, is minimized using a coarse-to-fine continuation strategy over this hierarchy. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR Images from different individuals. The method has shown efficient in putting into correspondence the principle anatomical structures of the brain. An application to atlas-based MRI segmentation, by transporting a labeled segmentation map on patient data, is also presented.

Biomechanical model for computing deformations for whole-body image registration: A meshless approach.

PubMed

Li, Mao; Miller, Karol; Joldes, Grand Roman; Kikinis, Ron; Wittek, Adam

2016-12-01

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time-consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2D models and computing single organ deformations. In this study, 3D comprehensive patient-specific nonlinear biomechanical models implemented using meshless Total Lagrangian explicit dynamics algorithms are applied to predict a 3D deformation field for whole-body image registration. Unlike a conventional approach that requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the fuzzy c-means algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle.

PubMed

Popuri, Karteek; Cobzas, Dana; Esfandiari, Nina; Baracos, Vickie; Jägersand, Martin

2016-02-01

The proportions of muscle and fat tissues in the human body, referred to as body composition is a vital measurement for cancer patients. Body composition has been recently linked to patient survival and the onset/recurrence of several types of cancers in numerous cancer research studies. This paper introduces a fully automatic framework for the segmentation of muscle and fat tissues from CT images to estimate body composition. We developed a novel finite element method (FEM) deformable model that incorporates a priori shape information via a statistical deformation model (SDM) within the template-based segmentation framework. The proposed method was validated on 1000 abdominal and 530 thoracic CT images and we obtained very good segmentation results with Jaccard scores in excess of 90% for both the muscle and fat regions.

Segmentation propagation for the automated quantification of ventricle volume from serial MRI

NASA Astrophysics Data System (ADS)

Linguraru, Marius George; Butman, John A.

2009-02-01

Accurate ventricle volume estimates could potentially improve the understanding and diagnosis of communicating hydrocephalus. Postoperative communicating hydrocephalus has been recognized in patients with brain tumors where the changes in ventricle volume can be difficult to identify, particularly over short time intervals. Because of the complex alterations of brain morphology in these patients, the segmentation of brain ventricles is challenging. Our method evaluates ventricle size from serial brain MRI examinations; we (i) combined serial images to increase SNR, (ii) automatically segmented this image to generate a ventricle template using fast marching methods and geodesic active contours, and (iii) propagated the segmentation using deformable registration of the original MRI datasets. By applying this deformation to the ventricle template, serial volume estimates were obtained in a robust manner from routine clinical images (0.93 overlap) and their variation analyzed.

Fractal analysis of INSAR and correlation with graph-cut based image registration for coastline deformation analysis: post seismic hazard assessment of the 2011 Tohoku earthquake region

NASA Astrophysics Data System (ADS)

Dutta, P. K.; Mishra, O. P.

2012-04-01

Satellite imagery for 2011 earthquake off the Pacific coast of Tohoku has provided an opportunity to conduct image transformation analyses by employing multi-temporal images retrieval techniques. In this study, we used a new image segmentation algorithm to image coastline deformation by adopting graph cut energy minimization framework. Comprehensive analysis of available INSAR images using coastline deformation analysis helped extract disaster information of the affected region of the 2011 Tohoku tsunamigenic earthquake source zone. We attempted to correlate fractal analysis of seismic clustering behavior with image processing analogies and our observations suggest that increase in fractal dimension distribution is associated with clustering of events that may determine the level of devastation of the region. The implementation of graph cut based image registration technique helps us to detect the devastation across the coastline of Tohoku through change of intensity of pixels that carries out regional segmentation for the change in coastal boundary after the tsunami. The study applies transformation parameters on remotely sensed images by manually segmenting the image to recovering translation parameter from two images that differ by rotation. Based on the satellite image analysis through image segmentation, it is found that the area of 0.997 sq km for the Honshu region was a maximum damage zone localized in the coastal belt of NE Japan forearc region. The analysis helps infer using matlab that the proposed graph cut algorithm is robust and more accurate than other image registration methods. The analysis shows that the method can give a realistic estimate for recovered deformation fields in pixels corresponding to coastline change which may help formulate the strategy for assessment during post disaster need assessment scenario for the coastal belts associated with damages due to strong shaking and tsunamis in the world under disaster risk mitigation programs.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

PubMed Central

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images. PMID:24989402

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

PubMed

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-07-01

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

Computer aided segmentation of kidneys using locally shape constrained deformable models on CT images

NASA Astrophysics Data System (ADS)

Erdt, Marius; Sakas, Georgios

2010-03-01

This work presents a novel approach for model based segmentation of the kidney in images acquired by Computed Tomography (CT). The developed computer aided segmentation system is expected to support computer aided diagnosis and operation planning. We have developed a deformable model based approach based on local shape constraints that prevents the model from deforming into neighboring structures while allowing the global shape to adapt freely to the data. Those local constraints are derived from the anatomical structure of the kidney and the presence and appearance of neighboring organs. The adaptation process is guided by a rule-based deformation logic in order to improve the robustness of the segmentation in areas of diffuse organ boundaries. Our work flow consists of two steps: 1.) a user guided positioning and 2.) an automatic model adaptation using affine and free form deformation in order to robustly extract the kidney. In cases which show pronounced pathologies, the system also offers real time mesh editing tools for a quick refinement of the segmentation result. Evaluation results based on 30 clinical cases using CT data sets show an average dice correlation coefficient of 93% compared to the ground truth. The results are therefore in most cases comparable to manual delineation. Computation times of the automatic adaptation step are lower than 6 seconds which makes the proposed system suitable for an application in clinical practice.

MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhen, X; Chen, H; Zhou, L

2014-06-15

Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the randommore » walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no 81301940)« less

SU-F-J-97: A Joint Registration and Segmentation Approach for Large Bladder Deformations in Adaptive Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Derksen, A; Koenig, L; Heldmann, S

Purpose: To improve results of deformable image registration (DIR) in adaptive radiotherapy for large bladder deformations in CT/CBCT pelvis imaging. Methods: A variational multi-modal DIR algorithm is incorporated in a joint iterative scheme, alternating between segmentation based bladder matching and registration. Using an initial DIR to propagate the bladder contour to the CBCT, in a segmentation step the contour is improved by discrete image gradient sampling along all surface normals and adapting the delineation to match the location of each maximum (with a search range of +−5/2mm at the superior/inferior bladder side and step size of 0.5mm). An additional graph-cutmore » based constraint limits the maximum difference between neighboring points. This improved contour is utilized in a subsequent DIR with a surface matching constraint. By calculating an euclidean distance map of the improved contour surface, the new constraint enforces the DIR to map each point of the original contour onto the improved contour. The resulting deformation is then used as a starting guess to compute a deformation update, which can again be used for the next segmentation step. The result is a dense deformation, able to capture much larger bladder deformations. The new method is evaluated on ten CT/CBCT male pelvis datasets, calculating Dice similarity coefficients (DSC) between the final propagated bladder contour and a manually delineated gold standard on the CBCT image. Results: Over all ten cases, an average DSC of 0.93±0.03 is achieved on the bladder. Compared with the initial DIR (0.88±0.05), the DSC is equal (2 cases) or improved (8 cases). Additionally, DSC accuracy of femoral bones (0.94±0.02) was not affected. Conclusion: The new approach shows that using the presented alternating segmentation/registration approach, the results of bladder DIR in the pelvis region can be greatly improved, especially for cases with large variations in bladder volume. Fraunhofer MEVIS received funding from a research grant by Varian Medical Systems.« less

Dynamic deformable models for 3D MRI heart segmentation

NASA Astrophysics Data System (ADS)

Zhukov, Leonid; Bao, Zhaosheng; Gusikov, Igor; Wood, John; Breen, David E.

2002-05-01

Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clincally-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.

Self-correcting multi-atlas segmentation

NASA Astrophysics Data System (ADS)

Gao, Yi; Wilford, Andrew; Guo, Liang

2016-03-01

In multi-atlas segmentation, one typically registers several atlases to the new image, and their respective segmented label images are transformed and fused to form the final segmentation. After each registration, the quality of the registration is reflected by the single global value: the final registration cost. Ideally, if the quality of the registration can be evaluated at each point, independent of the registration process, which also provides a direction in which the deformation can further be improved, the overall segmentation performance can be improved. We propose such a self-correcting multi-atlas segmentation method. The method is applied on hippocampus segmentation from brain images and statistically significantly improvement is observed.

Segmentation of the Aortic Valve Apparatus in 3D Echocardiographic Images: Deformable Modeling of a Branching Medial Structure

PubMed Central

Pouch, Alison M.; Tian, Sijie; Takabe, Manabu; Wang, Hongzhi; Yuan, Jiefu; Cheung, Albert T.; Jackson, Benjamin M.; Gorman, Joseph H.; Gorman, Robert C.; Yushkevich, Paul A.

2015-01-01

3D echocardiographic (3DE) imaging is a useful tool for assessing the complex geometry of the aortic valve apparatus. Segmentation of this structure in 3DE images is a challenging task that benefits from shape-guided deformable modeling methods, which enable inter-subject statistical shape comparison. Prior work demonstrates the efficacy of using continuous medial representation (cm-rep) as a shape descriptor for valve leaflets. However, its application to the entire aortic valve apparatus is limited since the structure has a branching medial geometry that cannot be explicitly parameterized in the original cm-rep framework. In this work, we show that the aortic valve apparatus can be accurately segmented using a new branching medial modeling paradigm. The segmentation method achieves a mean boundary displacement of 0.6 ± 0.1 mm (approximately one voxel) relative to manual segmentation on 11 3DE images of normal open aortic valves. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology. PMID:26247062

Interactive surface correction for 3D shape based segmentation

NASA Astrophysics Data System (ADS)

Schwarz, Tobias; Heimann, Tobias; Tetzlaff, Ralf; Rau, Anne-Mareike; Wolf, Ivo; Meinzer, Hans-Peter

2008-03-01

Statistical shape models have become a fast and robust method for segmentation of anatomical structures in medical image volumes. In clinical practice, however, pathological cases and image artifacts can lead to local deviations of the detected contour from the true object boundary. These deviations have to be corrected manually. We present an intuitively applicable solution for surface interaction based on Gaussian deformation kernels. The method is evaluated by two radiological experts on segmentations of the liver in contrast-enhanced CT images and of the left heart ventricle (LV) in MRI data. For both applications, five datasets are segmented automatically using deformable shape models, and the resulting surfaces are corrected manually. The interactive correction step improves the average surface distance against ground truth from 2.43mm to 2.17mm for the liver, and from 2.71mm to 1.34mm for the LV. We expect this method to raise the acceptance of automatic segmentation methods in clinical application.

Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach.

PubMed

Qazi, Arish A; Pekar, Vladimir; Kim, John; Xie, Jason; Breen, Stephen L; Jaffray, David A

2011-11-01

Intensity modulated radiation therapy (IMRT) allows greater control over dose distribution, which leads to a decrease in radiation related toxicity. IMRT, however, requires precise and accurate delineation of the organs at risk and target volumes. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. State of the art auto-segmentation methods are either atlas-based, model-based or hybrid however, robust fully automated segmentation is often difficult due to the insufficient discriminative information provided by standard medical imaging modalities for certain tissue types. In this paper, the authors present a fully automated hybrid approach which combines deformable registration with the model-based approach to accurately segment normal and target tissues from head and neck CT images. The segmentation process starts by using an average atlas to reliably identify salient landmarks in the patient image. The relationship between these landmarks and the reference dataset serves to guide a deformable registration algorithm, which allows for a close initialization of a set of organ-specific deformable models in the patient image, ensuring their robust adaptation to the boundaries of the structures. Finally, the models are automatically fine adjusted by our boundary refinement approach which attempts to model the uncertainty in model adaptation using a probabilistic mask. This uncertainty is subsequently resolved by voxel classification based on local low-level organ-specific features. To quantitatively evaluate the method, they auto-segment several organs at risk and target tissues from 10 head and neck CT images. They compare the segmentations to the manual delineations outlined by the expert. The evaluation is carried out by estimating two common quantitative measures on 10 datasets: volume overlap fraction or the Dice similarity coefficient (DSC), and a geometrical metric, the median symmetric Hausdorff distance (HD), which is evaluated slice-wise. They achieve an average overlap of 93% for the mandible, 91% for the brainstem, 83% for the parotids, 83% for the submandibular glands, and 74% for the lymph node levels. Our automated segmentation framework is able to segment anatomy in the head and neck region with high accuracy within a clinically-acceptable segmentation time.

Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Li; Gao, Yaozong; Shi, Feng

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segmentmore » CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.« less

Extra-dimensional Demons: a method for incorporating missing tissue in deformable image registration.

PubMed

Nithiananthan, Sajendra; Schafer, Sebastian; Mirota, Daniel J; Stayman, J Webster; Zbijewski, Wojciech; Reh, Douglas D; Gallia, Gary L; Siewerdsen, Jeffrey H

2012-09-01

A deformable registration method capable of accounting for missing tissue (e.g., excision) is reported for application in cone-beam CT (CBCT)-guided surgical procedures. Excisions are identified by a segmentation step performed simultaneous to the registration process. Tissue excision is explicitly modeled by increasing the dimensionality of the deformation field to allow motion beyond the dimensionality of the image. The accuracy of the model is tested in phantom, simulations, and cadaver models. A variant of the Demons deformable registration algorithm is modified to include excision segmentation and modeling. Segmentation is performed iteratively during the registration process, with initial implementation using a threshold-based approach to identify voxels corresponding to "tissue" in the moving image and "air" in the fixed image. With each iteration of the Demons process, every voxel is assigned a probability of excision. Excisions are modeled explicitly during registration by increasing the dimensionality of the deformation field so that both deformations and excisions can be accounted for by in- and out-of-volume deformations, respectively. The out-of-volume (i.e., fourth) component of the deformation field at each voxel carries a magnitude proportional to the excision probability computed in the excision segmentation step. The registration accuracy of the proposed "extra-dimensional" Demons (XDD) and conventional Demons methods was tested in the presence of missing tissue in phantom models, simulations investigating the effect of excision size on registration accuracy, and cadaver studies emulating realistic deformations and tissue excisions imparted in CBCT-guided endoscopic skull base surgery. Phantom experiments showed the normalized mutual information (NMI) in regions local to the excision to improve from 1.10 for the conventional Demons approach to 1.16 for XDD, and qualitative examination of the resulting images revealed major differences: the conventional Demons approach imparted unrealistic distortions in areas around tissue excision, whereas XDD provided accurate "ejection" of voxels within the excision site and maintained the registration accuracy throughout the rest of the image. Registration accuracy in areas far from the excision site (e.g., > ∼5 mm) was identical for the two approaches. Quantitation of the effect was consistent in analysis of NMI, normalized cross-correlation (NCC), target registration error (TRE), and accuracy of voxels ejected from the volume (true-positive and false-positive analysis). The registration accuracy for conventional Demons was found to degrade steeply as a function of excision size, whereas XDD was robust in this regard. Cadaver studies involving realistic excision of the clivus, vidian canal, and ethmoid sinuses demonstrated similar results, with unrealistic distortion of anatomy imparted by conventional Demons and accurate ejection and deformation for XDD. Adaptation of the Demons deformable registration process to include segmentation (i.e., identification of excised tissue) and an extra dimension in the deformation field provided a means to accurately accommodate missing tissue between image acquisitions. The extra-dimensional approach yielded accurate "ejection" of voxels local to the excision site while preserving the registration accuracy (typically subvoxel) of the conventional Demons approach throughout the rest of the image. The ability to accommodate missing tissue volumes is important to application of CBCT for surgical guidance (e.g., skull base drillout) and may have application in other areas of CBCT guidance.

Extra-dimensional Demons: A method for incorporating missing tissue in deformable image registration

PubMed Central

Nithiananthan, Sajendra; Schafer, Sebastian; Mirota, Daniel J.; Stayman, J. Webster; Zbijewski, Wojciech; Reh, Douglas D.; Gallia, Gary L.; Siewerdsen, Jeffrey H.

2012-01-01

Purpose: A deformable registration method capable of accounting for missing tissue (e.g., excision) is reported for application in cone-beam CT (CBCT)-guided surgical procedures. Excisions are identified by a segmentation step performed simultaneous to the registration process. Tissue excision is explicitly modeled by increasing the dimensionality of the deformation field to allow motion beyond the dimensionality of the image. The accuracy of the model is tested in phantom, simulations, and cadaver models. Methods: A variant of the Demons deformable registration algorithm is modified to include excision segmentation and modeling. Segmentation is performed iteratively during the registration process, with initial implementation using a threshold-based approach to identify voxels corresponding to “tissue” in the moving image and “air” in the fixed image. With each iteration of the Demons process, every voxel is assigned a probability of excision. Excisions are modeled explicitly during registration by increasing the dimensionality of the deformation field so that both deformations and excisions can be accounted for by in- and out-of-volume deformations, respectively. The out-of-volume (i.e., fourth) component of the deformation field at each voxel carries a magnitude proportional to the excision probability computed in the excision segmentation step. The registration accuracy of the proposed “extra-dimensional” Demons (XDD) and conventional Demons methods was tested in the presence of missing tissue in phantom models, simulations investigating the effect of excision size on registration accuracy, and cadaver studies emulating realistic deformations and tissue excisions imparted in CBCT-guided endoscopic skull base surgery. Results: Phantom experiments showed the normalized mutual information (NMI) in regions local to the excision to improve from 1.10 for the conventional Demons approach to 1.16 for XDD, and qualitative examination of the resulting images revealed major differences: the conventional Demons approach imparted unrealistic distortions in areas around tissue excision, whereas XDD provided accurate “ejection” of voxels within the excision site and maintained the registration accuracy throughout the rest of the image. Registration accuracy in areas far from the excision site (e.g., > ∼5 mm) was identical for the two approaches. Quantitation of the effect was consistent in analysis of NMI, normalized cross-correlation (NCC), target registration error (TRE), and accuracy of voxels ejected from the volume (true-positive and false-positive analysis). The registration accuracy for conventional Demons was found to degrade steeply as a function of excision size, whereas XDD was robust in this regard. Cadaver studies involving realistic excision of the clivus, vidian canal, and ethmoid sinuses demonstrated similar results, with unrealistic distortion of anatomy imparted by conventional Demons and accurate ejection and deformation for XDD. Conclusions: Adaptation of the Demons deformable registration process to include segmentation (i.e., identification of excised tissue) and an extra dimension in the deformation field provided a means to accurately accommodate missing tissue between image acquisitions. The extra-dimensional approach yielded accurate “ejection” of voxels local to the excision site while preserving the registration accuracy (typically subvoxel) of the conventional Demons approach throughout the rest of the image. The ability to accommodate missing tissue volumes is important to application of CBCT for surgical guidance (e.g., skull base drillout) and may have application in other areas of CBCT guidance. PMID:22957637

Contour-Driven Atlas-Based Segmentation

PubMed Central

Wachinger, Christian; Fritscher, Karl; Sharp, Greg; Golland, Polina

2016-01-01

We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images. PMID:26068202

Automatic segmentation of the liver using multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images

NASA Astrophysics Data System (ADS)

Jang, Yujin; Hong, Helen; Chung, Jin Wook; Yoon, Young Ho

2012-02-01

We propose an effective technique for the extraction of liver boundary based on multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images. Our method is composed of four main steps. First, for extracting an optimal volume circumscribing a liver, lower and side boundaries are defined by positional information of pelvis and rib. An upper boundary is defined by separating the lungs and heart from CT images. Second, for extracting an initial liver volume, optimal liver volume is smoothed by anisotropic diffusion filtering and is segmented using adaptively selected threshold value. Third, for removing neighbor organs from initial liver volume, morphological opening and connected component labeling are applied to multiple planes. Finally, for refining the liver boundaries, deformable surface model is applied to a posterior liver surface and missing left robe in previous step. Then, probability summation map is generated by calculating regional information of the segmented liver in coronal plane, which is used for restoring the inaccurate liver boundaries. Experimental results show that our segmentation method can accurately extract liver boundaries without leakage to neighbor organs in spite of various liver shape and ambiguous boundary.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Yanrong; Shao, Yeqin; Gao, Yaozong

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integratemore » the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.« less

Automated segmentation of dental CBCT image with prior-guided sequential random forests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Li; Gao, Yaozong; Shi, Feng

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimatemore » the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method for CBCT segmentation.« less

A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou Jinghao; Kim, Sung; Jabbour, Salma

2010-03-15

Purpose: In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. Methods: The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CTmore » (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. Results: The ACRASM segmentation algorithm was compared to the original active shape model (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to 6.54 mm for ASM. The volume overlap ratio ranged from 79% to 91% for ACRASM and from 44% to 80% for ASM. These data demonstrated that the segmentation results of ACRASM were in better agreement with the corresponding benchmarks than those of ASM. The developed registration algorithm was quantitatively evaluated by comparing the registered target volumes from the pCT to the benchmarks on the CBCT. The mean distance and the root mean square error ranged from 0.38 to 2.2 mm and from 0.45 to 2.36 mm, respectively, between the CBCT images and the registered pCT. The mean overlap ratio of the prostate volumes ranged from 85.2% to 95% after registration. The average time of the ACRASM-based segmentation was under 1 min. The average time of the global transformation was from 2 to 4 min on two 3D volumes and the average time of the local transformation was from 20 to 34 s on two deformable superquadrics mesh models. Conclusions: A novel and fast segmentation and deformable registration method was developed to capture the transformation between the planning and treatment images for external beam radiotherapy of prostate cancers. This method increases the computational efficiency and may provide foundation to achieve real time adaptive radiotherapy.« less

Watermarked cardiac CT image segmentation using deformable models and the Hermite transform

NASA Astrophysics Data System (ADS)

Gomez-Coronel, Sandra L.; Moya-Albor, Ernesto; Escalante-Ramírez, Boris; Brieva, Jorge

2015-01-01

Medical image watermarking is an open area for research and is a solution for the protection of copyright and intellectual property. One of the main challenges of this problem is that the marked images should not differ perceptually from the original images allowing a correct diagnosis and authentication. Furthermore, we also aim at obtaining watermarked images with very little numerical distortion so that computer vision tasks such as segmentation of important anatomical structures do not be impaired or affected. We propose a preliminary watermarking application in cardiac CT images based on a perceptive approach that includes a brightness model to generate a perceptive mask and identify the image regions where the watermark detection becomes a difficult task for the human eye. We propose a normalization scheme of the image in order to improve robustness against geometric attacks. We follow a spread spectrum technique to insert an alphanumeric code, such as patient's information, within the watermark. The watermark scheme is based on the Hermite transform as a bio-inspired image representation model. In order to evaluate the numerical integrity of the image data after watermarking, we perform a segmentation task based on deformable models. The segmentation technique is based on a vector-value level sets method such that, given a curve in a specific image, and subject to some constraints, the curve can evolve in order to detect objects. In order to stimulate the curve evolution we introduce simultaneously some image features like the gray level and the steered Hermite coefficients as texture descriptors. Segmentation performance was assessed by means of the Dice index and the Hausdorff distance. We tested different mark sizes and different insertion schemes on images that were later segmented either automatic or manual by physicians.

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

PubMed Central

Guo, Yanrong; Gao, Yaozong

2016-01-01

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods. PMID:26685226

Mapping of the surface rupture induced by the M 7.3 Kumamoto Earthquake along the Eastern segment of Futagawa fault using image correlation techniques

NASA Astrophysics Data System (ADS)

Ekhtari, N.; Glennie, C. L.; Fielding, E. J.; Liang, C.

2016-12-01

Near field surface deformation is vital to understanding the shallow fault physics of earthquakes but near-field deformation measurements are often sparse or not reliable. In this study, we use the Co-seismic Image Correlation (COSI-Corr) technique to map the near-field surface deformation caused by the M 7.3 April 16, 2016 Kumamoto Earthquake, Kyushu, Japan. The surface rupture around the Eastern segment of Futagawa fault is mapped using a pair of panchromatic 1.5 meter resolution SPOT 7 images. These images were acquired on January 16 and April 29, 2016 (3 months before and 13 days after the earthquake respectively) with close to nadir (less than 1.5 degree off nadir) viewing angle. The two images are ortho-rectified using SRTM Digital Elevation Model and further co-registered using tie points far away from the rupture field. Then the COSI-Corr technique is utilized to produce an estimated surface displacement map, and a horizontal displacement vector field is calculated which supplies a seamless estimate of near field displacement measurements along the Eastern segment of the Futagawa fault. The COSI-Corr estimated displacements are then compared to other existing displacement observations from InSAR, GPS and field observations.

Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

PubMed

Rebouças Filho, Pedro Pedrosa; Cortez, Paulo César; da Silva Barros, Antônio C; C Albuquerque, Victor Hugo; R S Tavares, João Manuel

2017-01-01

The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images. Copyright © 2016 Elsevier B.V. All rights reserved.

Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Morais, Pedro; Queirós, Sandro; Heyde, Brecht; Engvall, Jan; 'hooge, Jan D.; Vilaça, João L.

2017-09-01

Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35  ±  1.21 mm and 2.27  ±  1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.

Evaluation of a deformable registration algorithm for subsequent lung computed tomography imaging during radiochemotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stützer, Kristin; Haase, Robert; Exner, Florian

2016-09-15

Purpose: Rating both a lung segmentation algorithm and a deformable image registration (DIR) algorithm for subsequent lung computed tomography (CT) images by different evaluation techniques. Furthermore, investigating the relative performance and the correlation of the different evaluation techniques to address their potential value in a clinical setting. Methods: Two to seven subsequent CT images (69 in total) of 15 lung cancer patients were acquired prior, during, and after radiochemotherapy. Automated lung segmentations were compared to manually adapted contours. DIR between the first and all following CT images was performed with a fast algorithm specialized for lung tissue registration, requiring themore » lung segmentation as input. DIR results were evaluated based on landmark distances, lung contour metrics, and vector field inconsistencies in different subvolumes defined by eroding the lung contour. Correlations between the results from the three methods were evaluated. Results: Automated lung contour segmentation was satisfactory in 18 cases (26%), failed in 6 cases (9%), and required manual correction in 45 cases (66%). Initial and corrected contours had large overlap but showed strong local deviations. Landmark-based DIR evaluation revealed high accuracy compared to CT resolution with an average error of 2.9 mm. Contour metrics of deformed contours were largely satisfactory. The median vector length of inconsistency vector fields was 0.9 mm in the lung volume and slightly smaller for the eroded volumes. There was no clear correlation between the three evaluation approaches. Conclusions: Automatic lung segmentation remains challenging but can assist the manual delineation process. Proven by three techniques, the inspected DIR algorithm delivers reliable results for the lung CT data sets acquired at different time points. Clinical application of DIR demands a fast DIR evaluation to identify unacceptable results, for instance, by combining different automated DIR evaluation methods.« less

SU-E-J-220: Evaluation of Atlas-Based Auto-Segmentation (ABAS) in Head-And-Neck Adaptive Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Q; Yan, D

2014-06-01

Purpose: Evaluate the accuracy of atlas-based auto segmentation of organs at risk (OARs) on both helical CT (HCT) and cone beam CT (CBCT) images in head and neck (HN) cancer adaptive radiotherapy (ART). Methods: Six HN patients treated in the ART process were included in this study. For each patient, three images were selected: pretreatment planning CT (PreTx-HCT), in treatment CT for replanning (InTx-HCT) and a CBCT acquired in the same day of the InTx-HCT. Three clinical procedures of auto segmentation and deformable registration performed in the ART process were evaluated: a) auto segmentation on PreTx-HCT using multi-subject atlases, b)more » intra-patient propagation of OARs from PreTx-HCT to InTx-HCT using deformable HCT-to-HCT image registration, and c) intra-patient propagation of OARs from PreTx-HCT to CBCT using deformable CBCT-to-HCT image registration. Seven OARs (brainstem, cord, L/R parotid, L/R submandibular gland and mandible) were manually contoured on PreTx-HCT and InTx-HCT for comparison. In addition, manual contours on InTx-CT were copied on the same day CBCT, and a local region rigid body registration was performed accordingly for each individual OAR. For procedures a) and b), auto contours were compared to manual contours, and for c) auto contours were compared to those rigidly transferred contours on CBCT. Dice similarity coefficients (DSC) and mean surface distances of agreement (MSDA) were calculated for evaluation. Results: For procedure a), the mean DSC/MSDA of most OARs are >80%/±2mm. For intra-patient HCT-to-HCT propagation, the Resultimproved to >85%/±1.5mm. Compared to HCT-to-HCT, the mean DSC for HCT-to-CBCT propagation drops ∼2–3% and MSDA increases ∼0.2mm. This Resultindicates that the inferior imaging quality of CBCT seems only degrade auto propagation performance slightly. Conclusion: Auto segmentation and deformable propagation can generate OAR structures on HCT and CBCT images with clinically acceptable accuracy. Therefore, they can be reliably implemented in the clinical HN ART process.« less

Deformable M-Reps for 3D Medical Image Segmentation.

PubMed

Pizer, Stephen M; Fletcher, P Thomas; Joshi, Sarang; Thall, Andrew; Chen, James Z; Fridman, Yonatan; Fritsch, Daniel S; Gash, Graham; Glotzer, John M; Jiroutek, Michael R; Lu, Conglin; Muller, Keith E; Tracton, Gregg; Yushkevich, Paul; Chaney, Edward L

2003-11-01

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models , which define objects at coarse scale by a hierarchy of figures - each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps ), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.

Deformable M-Reps for 3D Medical Image Segmentation

PubMed Central

Pizer, Stephen M.; Fletcher, P. Thomas; Joshi, Sarang; Thall, Andrew; Chen, James Z.; Fridman, Yonatan; Fritsch, Daniel S.; Gash, Graham; Glotzer, John M.; Jiroutek, Michael R.; Lu, Conglin; Muller, Keith E.; Tracton, Gregg; Yushkevich, Paul; Chaney, Edward L.

2013-01-01

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures – each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure. A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects. The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported. PMID:23825898

TU-AB-202-05: GPU-Based 4D Deformable Image Registration Using Adaptive Tetrahedral Mesh Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, Z; Zhuang, L; Gu, X

Purpose: Deformable image registration (DIR) has been employed today as an automated and effective segmentation method to transfer tumor or organ contours from the planning image to daily images, instead of manual segmentation. However, the computational time and accuracy of current DIR approaches are still insufficient for online adaptive radiation therapy (ART), which requires real-time and high-quality image segmentation, especially in a large datasets of 4D-CT images. The objective of this work is to propose a new DIR algorithm, with fast computational speed and high accuracy, by using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step ismore » to generate the adaptive tetrahedral mesh based on the image features of a reference phase of 4D-CT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. Subsequently, the deformation vector fields (DVF) and other phases of 4D-CT can be obtained by matching each phase of the target 4D-CT images with the corresponding deformed reference phase. The proposed 4D DIR method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its parallel computing ability. Results: A 4D NCAT digital phantom was used to test the efficiency and accuracy of our method. Both the image and DVF results show that the fine structures and shapes of lung are well preserved, and the tumor position is well captured, i.e., 3D distance error is 1.14 mm. Compared to the previous voxel-based CPU implementation of DIR, such as demons, the proposed method is about 160x faster for registering a 10-phase 4D-CT with a phase dimension of 256×256×150. Conclusion: The proposed 4D DIR method uses feature-based mesh and GPU-based parallelism, which demonstrates the capability to compute both high-quality image and motion results, with significant improvement on the computational speed.« less

Video-based noncooperative iris image segmentation.

PubMed

Du, Yingzi; Arslanturk, Emrah; Zhou, Zhi; Belcher, Craig

2011-02-01

In this paper, we propose a video-based noncooperative iris image segmentation scheme that incorporates a quality filter to quickly eliminate images without an eye, employs a coarse-to-fine segmentation scheme to improve the overall efficiency, uses a direct least squares fitting of ellipses method to model the deformed pupil and limbic boundaries, and develops a window gradient-based method to remove noise in the iris region. A remote iris acquisition system is set up to collect noncooperative iris video images. An objective method is used to quantitatively evaluate the accuracy of the segmentation results. The experimental results demonstrate the effectiveness of this method. The proposed method would make noncooperative iris recognition or iris surveillance possible.

Open-source software platform for medical image segmentation applications

NASA Astrophysics Data System (ADS)

Namías, R.; D'Amato, J. P.; del Fresno, M.

2017-11-01

Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We present the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.

Adapting Active Shape Models for 3D segmentation of tubular structures in medical images.

PubMed

de Bruijne, Marleen; van Ginneken, Bram; Viergever, Max A; Niessen, Wiro J

2003-07-01

Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited. This paper describes modifications to both the shape and the appearance model of the original ASM formulation. Shape model flexibility is increased, for tubular objects, by modeling the axis deformation independent of the cross-sectional deformation, and by adding supplementary cylindrical deformation modes. Furthermore, a novel appearance modeling scheme that effectively deals with a highly varying background is developed. In contrast with the conventional ASM approach, the new appearance model is trained on both boundary and non-boundary points, and the probability that a given point belongs to the boundary is estimated non-parametrically. The methods are evaluated on the complex task of segmenting thrombus in abdominal aortic aneurysms (AAA). Shape approximation errors were successfully reduced using the two shape model extensions. Segmentation using the new appearance model significantly outperformed the original ASM scheme; average volume errors are 5.1% and 45% respectively.

Lymph node segmentation on CT images by a shape model guided deformable surface methodh

NASA Astrophysics Data System (ADS)

Maleike, Daniel; Fabel, Michael; Tetzlaff, Ralf; von Tengg-Kobligk, Hendrik; Heimann, Tobias; Meinzer, Hans-Peter; Wolf, Ivo

2008-03-01

With many tumor entities, quantitative assessment of lymph node growth over time is important to make therapy choices or to evaluate new therapies. The clinical standard is to document diameters on transversal slices, which is not the best measure for a volume. We present a new algorithm to segment (metastatic) lymph nodes and evaluate the algorithm with 29 lymph nodes in clinical CT images. The algorithm is based on a deformable surface search, which uses statistical shape models to restrict free deformation. To model lymph nodes, we construct an ellipsoid shape model, which strives for a surface with strong gradients and user-defined gray values. The algorithm is integrated into an application, which also allows interactive correction of the segmentation results. The evaluation shows that the algorithm gives good results in the majority of cases and is comparable to time-consuming manual segmentation. The median volume error was 10.1% of the reference volume before and 6.1% after manual correction. Integrated into an application, it is possible to perform lymph node volumetry for a whole patient within the 10 to 15 minutes time limit imposed by clinical routine.

SU-E-J-108: Solving the Chinese Postman Problem for Effective Contour Deformation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, J; Zhang, L; Balter, P

2015-06-15

Purpose: To develop a practical approach for accurate contour deformation when deformable image registration (DIR) is used for atlas-based segmentation or contour propagation in image-guided radiotherapy. Methods: A contour deformation approach was developed on the basis of 3D mesh operations. The 2D contours represented by a series of points in each slice were first converted to a 3D triangular mesh, which was deformed by the deformation vectors resulting from DIR. A set of parallel 2D planes then cut through the deformed 3D mesh, generating unordered points and line segments, which should be reorganized into a set of 2D contour points.more » It was realized that the reorganization problem was equivalent to solving the Chinese Postman Problem (CPP) by traversing a graph built from the unordered points with the least cost. Alternatively, deformation could be applied to a binary mask converted from the original contours. The deformed binary mask was then converted back into contours at the CT slice locations. We performed a qualitative comparison to validate the mesh-based approach against the image-based approach. Results: The DIR could considerably change the 3D mesh, making complicated 2D contour representations after deformation. CPP was able to effectively reorganize the points in 2D planes no matter how complicated the 2D contours were. The mesh-based approach did not require a post-processing of the contour, thus accurately showing the actual deformation in DIR. The mesh-based approach could keep some fine details and resulted in smoother contours than the image-based approach did, especially for the lung structure. Image-based approach appeared to over-process contours and suffered from image resolution limits. The mesh-based approach was integrated into in-house DIR software for use in routine clinic and research. Conclusion: We developed a practical approach for accurate contour deformation. The efficiency of this approach was demonstrated in both clinic and research applications. This work was partially supported by Cancer Prevention & Research Institute of Texas (CPRIT) RP110562.« less

SU-C-BRA-03: An Automated and Quick Contour Errordetection for Auto Segmentation in Online Adaptive Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, J; Ates, O; Li, X

Purpose: To develop a tool that can quickly and automatically assess contour quality generated from auto segmentation during online adaptive replanning. Methods: Due to the strict time requirement of online replanning and lack of ‘ground truth’ contours in daily images, our method starts with assessing image registration accuracy focusing on the surface of the organ in question. Several metrics tightly related to registration accuracy including Jacobian maps, contours shell deformation, and voxel-based root mean square (RMS) analysis were computed. To identify correct contours, additional metrics and an adaptive decision tree are introduced. To approve in principle, tests were performed withmore » CT sets, planned and daily CTs acquired using a CT-on-rails during routine CT-guided RT delivery for 20 prostate cancer patients. The contours generated on daily CTs using an auto-segmentation tool (ADMIRE, Elekta, MIM) based on deformable image registration of the planning CT and daily CT were tested. Results: The deformed contours of 20 patients with total of 60 structures were manually checked as baselines. The incorrect rate of total contours is 49%. To evaluate the quality of local deformation, the Jacobian determinant (1.047±0.045) on contours has been analyzed. In an analysis of rectum contour shell deformed, the higher rate (0.41) of error contours detection was obtained compared to 0.32 with manual check. All automated detections took less than 5 seconds. Conclusion: The proposed method can effectively detect contour errors in micro and macro scope by evaluating multiple deformable registration metrics in a parallel computing process. Future work will focus on improving practicability and optimizing calculation algorithms and metric selection.« less

The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration

NASA Astrophysics Data System (ADS)

Saenz, Daniel L.; Kim, Hojin; Chen, Josephine; Stathakis, Sotirios; Kirby, Neil

2016-09-01

The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu’s method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying.

PubMed

Nguyen, Hoai Nam; Paveau, Vincent; Cauchois, Cyril; Kervrann, Charles

2018-04-19

Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. We developed a novel de-arraying approach for TMA analysis. By combining wavelet-based detection, active contour segmentation, and thin-plate spline interpolation, our approach is able to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background and non-linear deformation of TMA grid. In addition, the deformation estimation produces quantitative information to asset the manufacturing quality of TMAs.

Correction tool for Active Shape Model based lumbar muscle segmentation.

PubMed

Valenzuela, Waldo; Ferguson, Stephen J; Ignasiak, Dominika; Diserens, Gaelle; Vermathen, Peter; Boesch, Chris; Reyes, Mauricio

2015-08-01

In the clinical environment, accuracy and speed of the image segmentation process plays a key role in the analysis of pathological regions. Despite advances in anatomic image segmentation, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a low number of interactions, and a user-independent solution. In this work we present a new interactive correction method for correcting the image segmentation. Given an initial segmentation and the original image, our tool provides a 2D/3D environment, that enables 3D shape correction through simple 2D interactions. Our scheme is based on direct manipulation of free form deformation adapted to a 2D environment. This approach enables an intuitive and natural correction of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle segmentation from Magnetic Resonance Images. Experimental results show that full segmentation correction could be performed within an average correction time of 6±4 minutes and an average of 68±37 number of interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.03.

Deformation field correction for spatial normalization of PET images

PubMed Central

Bilgel, Murat; Carass, Aaron; Resnick, Susan M.; Wong, Dean F.; Prince, Jerry L.

2015-01-01

Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet the current state of the art in PET-to-PET registration is limited to the application of conventional deformable registration methods that were developed for structural images. A method is presented for the spatial normalization of PET images that improves their anatomical alignment over the state of the art. The approach works by correcting the deformable registration result using a model that is learned from training data having both PET and structural images. In particular, viewing the structural registration of training data as ground truth, correction factors are learned by using a generalized ridge regression at each voxel given the PET intensities and voxel locations in a population-based PET template. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation evaluation on 79 subjects shows that the proposed method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations. PMID:26142272

Complete grain boundaries from incomplete EBSD maps: the influence of segmentation on grain size determinations

NASA Astrophysics Data System (ADS)

Heilbronner, Renée; Kilian, Ruediger

2017-04-01

Grain size analyses are carried out for a number of reasons, for example, the dynamically recrystallized grain size of quartz is used to assess the flow stresses during deformation. Typically a thin section or polished surface is used. If the expected grain size is large enough (10 µm or larger), the images can be obtained on a light microscope, if the grain size is smaller, the SEM is used. The grain boundaries are traced (the process is called segmentation and can be done manually or via image processing) and the size of the cross sectional areas (segments) is determined. From the resulting size distributions, 'the grain size' or 'average grain size', usually a mean diameter or similar, is derived. When carrying out such grain size analyses, a number of aspects are critical for the reproducibility of the result: the resolution of the imaging equipment (light microscope or SEM), the type of images that are used for segmentation (cross polarized, partial or full orientation images, CIP versus EBSD), the segmentation procedure (algorithm) itself, the quality of the segmentation and the mathematical definition and calculation of 'the average grain size'. The quality of the segmentation depends very strongly on the criteria that are used for identifying grain boundaries (for example, angles of misorientation versus shape considerations), on pre- and post-processing (filtering) and on the quality of the recorded images (most notably on the indexing ratio). In this contribution, we consider experimentally deformed Black Hills quartzite with dynamically re-crystallized grain sizes in the range of 2 - 15 µm. We compare two basic methods of segmentations of EBSD maps (orientation based versus shape based) and explore how the choice of methods influences the result of the grain size analysis. We also compare different measures for grain size (mean versus mode versus RMS, and 2D versus 3D) in order to determine which of the definitions of 'average grain size yields the most stable results.

Alternative radiation-free registration technique for image-guided pedicle screw placement in deformed cervico-thoracic segments.

PubMed

Kantelhardt, Sven R; Neulen, Axel; Keric, Naureen; Gutenberg, Angelika; Conrad, Jens; Giese, Alf

2017-10-01

Image-guided pedicle screw placement in the cervico-thoracic region is a commonly applied technique. In some patients with deformed cervico-thoracic segments, conventional or 3D fluoroscopy based registration of image-guidance might be difficult or impossible because of the anatomic/pathological conditions. Landmark based registration has been used as an alternative, mostly using separate registration of each vertebra. We here investigated a routine for landmark based registration of rigid spinal segments as single objects, using cranial image-guidance software. Landmark based registration of image-guidance was performed using cranial navigation software. After surgical exposure of the spinous processes, lamina and facet joints and fixation of a reference marker array, up to 26 predefined landmarks were acquired using a pointer. All pedicle screws were implanted using image guidance alone. Following image-guided screw placement all patients underwent postoperative CT scanning. Screw positions as well as intraoperative and clinical parameters were retrospectively analyzed. Thirteen patients received 73 pedicle screws at levels C6 to Th8. Registration of spinal segments, using the cranial image-guidance succeeded in all cases. Pedicle perforations were observed in 11.0%, severe perforations of >2 mm occurred in 5.4%. One patient developed a transient C8 syndrome and had to be revised for deviation of the C7 pedicle screw. No other pedicle screw-related complications were observed. In selected patients suffering from pathologies of the cervico-thoracic region, which impair intraoperative fluoroscopy or 3D C-arm imaging, landmark based registration of image-guidance using cranial software is a feasible, radiation-saving and a safe alternative.

3D-SIFT-Flow for atlas-based CT liver image segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Yan, E-mail: xuyan04@gmail.com; Xu, Chenchao, E-mail: chenchaoxu33@gmail.com; Kuang, Xiao, E-mail: kuangxiao.ace@gmail.com

Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation.more » In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.« less

Non-rigid estimation of cell motion in calcium time-lapse images

NASA Astrophysics Data System (ADS)

Hachi, Siham; Lucumi Moreno, Edinson; Desmet, An-Sofie; Vanden Berghe, Pieter; Fleming, Ronan M. T.

2016-03-01

Calcium imaging is a widely used technique in neuroscience permitting the simultaneous monitoring of electro- physiological activity of hundreds of neurons at single cell resolution. Identification of neuronal activity requires rapid and reliable image analysis techniques, especially when neurons fire and move simultaneously over time. Traditionally, image segmentation is performed to extract individual neurons in the first frame of a calcium sequence. Thereafter, the mean intensity is calculated from the same region of interest in each frame to infer calcium signals. However, when cells move, deform and fire, this segmentation on its own generates artefacts and therefore biased neuronal activity. Therefore, there is a pressing need to develop a more efficient cell tracking technique. We hereby present a novel vision-based cell tracking scheme using a thin-plate spline deformable model. The thin-plate spline warping is based on control points detected using the Fast from Accelerated Segment Test descriptor and tracked using the Lucas-Kanade optical flow. Our method is able to track neurons in calcium time-series, even when there are large changes in intensity, such as during a firing event. The robustness and efficiency of the proposed approach is validated on real calcium time-lapse images of a neuronal population.

4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling.

PubMed

Yang, Deshan; Lu, Wei; Low, Daniel A; Deasy, Joseph O; Hope, Andrew J; El Naqa, Issam

2008-10-01

Four-dimensional computed tomography (4D-CT) imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1 +/- 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D (spatial 3D+tidal volume+airflow rate) motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.

Tissue Probability Map Constrained 4-D Clustering Algorithm for Increased Accuracy and Robustness in Serial MR Brain Image Segmentation

PubMed Central

Xue, Zhong; Shen, Dinggang; Li, Hai; Wong, Stephen

2010-01-01

The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue probability maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders. PMID:26566399

SEGMENTING CT PROSTATE IMAGES USING POPULATION AND PATIENT-SPECIFIC STATISTICS FOR RADIOTHERAPY.

PubMed

Feng, Qianjin; Foskey, Mark; Tang, Songyuan; Chen, Wufan; Shen, Dinggang

2009-08-07

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application.

SEGMENTING CT PROSTATE IMAGES USING POPULATION AND PATIENT-SPECIFIC STATISTICS FOR RADIOTHERAPY

PubMed Central

Feng, Qianjin; Foskey, Mark; Tang, Songyuan; Chen, Wufan; Shen, Dinggang

2010-01-01

This paper presents a new deformable model using both population and patient-specific statistics to segment the prostate from CT images. There are two novelties in the proposed method. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than general intensity and gradient features, is used to characterize the image features. Second, an online training approach is used to build the shape statistics for accurately capturing intra-patient variation, which is more important than inter-patient variation for prostate segmentation in clinical radiotherapy. Experimental results show that the proposed method is robust and accurate, suitable for clinical application. PMID:21197416

High-contrast imaging with an arbitrary aperture: active correction of aperture discontinuities

NASA Astrophysics Data System (ADS)

Pueyo, Laurent; Norman, Colin; Soummer, Rémi; Perrin, Marshall; N'Diaye, Mamadou; Choquet, Elodie

2013-09-01

We present a new method to achieve high-contrast images using segmented and/or on-axis telescopes. Our approach relies on using two sequential Deformable Mirrors to compensate for the large amplitude excursions in the telescope aperture due to secondary support structures and/or segment gaps. In this configuration the parameter landscape of Deformable Mirror Surfaces that yield high contrast Point Spread Functions is not linear, and non-linear methods are needed to find the true minimum in the optimization topology. We solve the highly non-linear Monge-Ampere equation that is the fundamental equation describing the physics of phase induced amplitude modulation. We determine the optimum configuration for our two sequential Deformable Mirror system and show that high-throughput and high contrast solutions can be achieved using realistic surface deformations that are accessible using existing technologies. We name this process Active Compensation of Aperture Discontinuities (ACAD). We show that for geometries similar to JWST, ACAD can attain at least 10-7 in contrast and an order of magnitude higher for future Extremely Large Telescopes, even when the pupil features a missing segment" . We show that the converging non-linear mappings resulting from our Deformable Mirror shapes actually damp near-field diffraction artifacts in the vicinity of the discontinuities. Thus ACAD actually lowers the chromatic ringing due to diffraction by segment gaps and strut's while not amplifying the diffraction at the aperture edges beyond the Fresnel regime and illustrate the broadband properties of ACAD in the case of the pupil configuration corresponding to the Astrophysics Focused Telescope Assets. Since details about these telescopes are not yet available to the broader astronomical community, our test case is based on a geometry mimicking the actual one, to the best of our knowledge.

Deformable segmentation via sparse representation and dictionary learning.

PubMed

Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

2012-10-01

"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Automated breast segmentation in ultrasound computer tomography SAFT images

NASA Astrophysics Data System (ADS)

Hopp, T.; You, W.; Zapf, M.; Tan, W. Y.; Gemmeke, H.; Ruiter, N. V.

2017-03-01

Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.

Poster — Thur Eve — 70: Automatic lung bronchial and vessel bifurcations detection algorithm for deformable image registration assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Labine, Alexandre; Carrier, Jean-François; Bedwani, Stéphane

2014-08-15

Purpose: To investigate an automatic bronchial and vessel bifurcations detection algorithm for deformable image registration (DIR) assessment to improve lung cancer radiation treatment. Methods: 4DCT datasets were acquired and exported to Varian treatment planning system (TPS) EclipseTM for contouring. The lungs TPS contour was used as the prior shape for a segmentation algorithm based on hierarchical surface deformation that identifies the deformed lungs volumes of the 10 breathing phases. Hounsfield unit (HU) threshold filter was applied within the segmented lung volumes to identify blood vessels and airways. Segmented blood vessels and airways were skeletonised using a hierarchical curve-skeleton algorithm basedmore » on a generalized potential field approach. A graph representation of the computed skeleton was generated to assign one of three labels to each node: the termination node, the continuation node or the branching node. Results: 320 ± 51 bifurcations were detected in the right lung of a patient for the 10 breathing phases. The bifurcations were visually analyzed. 92 ± 10 bifurcations were found in the upper half of the lung and 228 ± 45 bifurcations were found in the lower half of the lung. Discrepancies between ten vessel trees were mainly ascribed to large deformation and in regions where the HU varies. Conclusions: We established an automatic method for DIR assessment using the morphological information of the patient anatomy. This approach allows a description of the lung's internal structure movement, which is needed to validate the DIR deformation fields for accurate 4D cancer treatment planning.« less

Intervertebral disc segmentation in MR images with 3D convolutional networks

NASA Astrophysics Data System (ADS)

Korez, Robert; Ibragimov, Bulat; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2017-02-01

The vertebral column is a complex anatomical construct, composed of vertebrae and intervertebral discs (IVDs) supported by ligaments and muscles. During life, all components undergo degenerative changes, which may in some cases cause severe, chronic and debilitating low back pain. The main diagnostic challenge is to locate the pain generator, and degenerated IVDs have been identified to act as such. Accurate and robust segmentation of IVDs is therefore a prerequisite for computer-aided diagnosis and quantification of IVD degeneration, and can be also used for computer-assisted planning and simulation in spinal surgery. In this paper, we present a novel fully automated framework for supervised segmentation of IVDs from three-dimensional (3D) magnetic resonance (MR) spine images. By considering global intensity appearance and local shape information, a landmark-based approach is first used for the detection of IVDs in the observed image, which then initializes the segmentation of IVDs by coupling deformable models with convolutional networks (ConvNets). For this purpose, a 3D ConvNet architecture was designed that learns rich high-level appearance representations from a training repository of IVDs, and then generates spatial IVD probability maps that guide deformable models towards IVD boundaries. By applying the proposed framework to 15 3D MR spine images containing 105 IVDs, quantitative comparison of the obtained against reference IVD segmentations yielded an overall mean Dice coefficient of 92.8%, mean symmetric surface distance of 0.4 mm and Hausdorff surface distance of 3.7 mm.

Multiscale segmentation-aided digital image correlation for strain concentration characterization of a turbine blade fir-tree root

NASA Astrophysics Data System (ADS)

Sun, Chen; Zhou, Yihao; Li, Yang; Chen, Jubing; Miao, Hong

2018-04-01

In this paper, a multiscale segmentation-aided digital image correlation method is proposed to characterize the strain concentration of a turbine blade fir-tree root during its contact with the disk groove. A multiscale approach is implemented to increase the local spatial resolution, as the strain concentration area undergoes highly non-uniform deformation and its size is much smaller than the contact elements. In this approach, a far-field view and several near-field views are selected, aiming to get the full-field deformation and local deformation simultaneously. To avoid the interference of different cameras, only the optical axis of the far-field camera is selected to be perpendicular to the specimen surface while the others are inclined. A homography transformation is optimized by matching the feature points, to rectify the artificial deformation caused by the inclination of the optical axis. The resultant genuine near-field strain is thus obtained after the transformation. A real-world experiment is carried out and the strain concentration is characterized. The strain concentration factor is defined accordingly to provide a quantitative analysis.

Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours

NASA Astrophysics Data System (ADS)

Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

2017-01-01

Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.

Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.

PubMed

Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

2017-01-07

Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.

Application of motion analysis in the study of the effect of botulinum toxin to rat vocal folds

NASA Astrophysics Data System (ADS)

Saadah, Abdul K.; Galatsanos, Nikolas P.; Inagi, K.; Bless, D.

1997-05-01

In the past we have proposed a system that measures the deformations of the vocal folds from videostroboscopic images of the larynx, in that system: (1) we extract the boundaries of the vocal folds, (2) we register elastically the vocal fold boundaries in successive frames. This yields the displacement vector field (DVF) between adjacent frames, and (3) we fit using a least-squares approach an affine transformation model to succinctly describe the deformations between adjacent frames. In this paper, we present as an example of the capabilities of this system, an initial study of the deformation changes in rat vocal folds pre and post injection with Botulinum toxin. For this application the generated DVF was segmented into right DVF and left DVF and the deformation of each segment is studied separately.

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, J; Han, J; Ailawadi, S

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warpedmore » to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.« less

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

PubMed

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hardisty, M.; Gordon, L.; Agarwal, P.

2007-08-15

Quantitative assessment of metastatic disease in bone is often considered immeasurable and, as such, patients with skeletal metastases are often excluded from clinical trials. In order to effectively quantify the impact of metastatic tumor involvement in the spine, accurate segmentation of the vertebra is required. Manual segmentation can be accurate but involves extensive and time-consuming user interaction. Potential solutions to automating segmentation of metastatically involved vertebrae are demons deformable image registration and level set methods. The purpose of this study was to develop a semiautomated method to accurately segment tumor-bearing vertebrae using the aforementioned techniques. By maintaining morphology of anmore » atlas, the demons-level set composite algorithm was able to accurately differentiate between trans-cortical tumors and surrounding soft tissue of identical intensity. The algorithm successfully segmented both the vertebral body and trabecular centrum of tumor-involved and healthy vertebrae. This work validates our approach as equivalent in accuracy to an experienced user.« less

Open-source image registration for MRI-TRUS fusion-guided prostate interventions.

PubMed

Fedorov, Andriy; Khallaghi, Siavash; Sánchez, C Antonio; Lasso, Andras; Fels, Sidney; Tuncali, Kemal; Sugar, Emily Neubauer; Kapur, Tina; Zhang, Chenxi; Wells, William; Nguyen, Paul L; Abolmaesumi, Purang; Tempany, Clare

2015-06-01

We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI-TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems. The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation. The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license. We release open-source tools that may be used for registration during MRI-TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.

Segmentation, modeling and classification of the compact objects in a pile

NASA Technical Reports Server (NTRS)

Gupta, Alok; Funka-Lea, Gareth; Wohn, Kwangyoen

1990-01-01

The problem of interpreting dense range images obtained from the scene of a heap of man-made objects is discussed. A range image interpretation system consisting of segmentation, modeling, verification, and classification procedures is described. First, the range image is segmented into regions and reasoning is done about the physical support of these regions. Second, for each region several possible three-dimensional interpretations are made based on various scenarios of the objects physical support. Finally each interpretation is tested against the data for its consistency. The superquadric model is selected as the three-dimensional shape descriptor, plus tapering deformations along the major axis. Experimental results obtained from some complex range images of mail pieces are reported to demonstrate the soundness and the robustness of our approach.

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

PubMed

Avendi, M R; Kheradvar, Arash; Jafarkhani, Hamid

2016-05-01

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are used to infer the LV shape. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81 mm and 0.86, versus those of 79.2-95.62%, 0.87-0.9, 1.76-2.97 mm and 0.67-0.78, obtained by other methods, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Infants and young children modeling method for numerical dosimetry studies: application to plane wave exposure

NASA Astrophysics Data System (ADS)

Dahdouh, S.; Varsier, N.; Nunez Ochoa, M. A.; Wiart, J.; Peyman, A.; Bloch, I.

2016-02-01

Numerical dosimetry studies require the development of accurate numerical 3D models of the human body. This paper proposes a novel method for building 3D heterogeneous young children models combining results obtained from a semi-automatic multi-organ segmentation algorithm and an anatomy deformation method. The data consist of 3D magnetic resonance images, which are first segmented to obtain a set of initial tissues. A deformation procedure guided by the segmentation results is then developed in order to obtain five young children models ranging from the age of 5 to 37 months. By constraining the deformation of an older child model toward a younger one using segmentation results, we assure the anatomical realism of the models. Using the proposed framework, five models, containing thirteen tissues, are built. Three of these models are used in a prospective dosimetry study to analyze young child exposure to radiofrequency electromagnetic fields. The results lean to show the existence of a relationship between age and whole body exposure. The results also highlight the necessity to specifically study and develop measurements of child tissues dielectric properties.

Quantifying Dynamic Deformity After Dual Plane Breast Augmentation.

PubMed

Cheffe, Marcelo Recondo; Valentini, Jorge Diego; Collares, Marcus Vinicius Martins; Piccinini, Pedro Salomão; da Silva, Jefferson Luis Braga

2018-06-01

Dynamic breast deformity (DBD) is characterized by visible distortion and deformity of the breast due to contraction of the pectoralis major muscle after submuscular breast augmentation; fortunately, in most cases, this is not a clinically significant complaint from patients. The purpose of this study is to present a simple method for objectively measuring DBD in patients submitted to dual plane breast augmentation (DPBA). We studied 32 women, between 18 and 50 years old, who underwent primary DPBA with at least 1 year of follow-up. Anthropometric landmarks of the breast were marked, creating linear segments. Standardized photographs were obtained both during no pectoralis contraction (NPC) and during maximum pectoralis muscle contraction (MPC); measurements of the linear segments were taken through ImageJ imaging software, and both groups were compared. We found statistically significant differences in all analyzed segments when comparing measurements of the breasts during NPC and MPC (p < 0.001). Our study proposes a novel, standardized method for measuring DBD after DPBA. This technique is reproducible, allowing for objective quantification of the deformity in any patient, which can be valuable for both patients and surgeons, as it allows for a more thorough discussion on DBD, both pre- and postoperatively, and may help both patients and surgeons to make more informed decisions regarding potential animation deformities after breast augmentation. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Temporal subtraction contrast-enhanced dedicated breast CT

NASA Astrophysics Data System (ADS)

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-09-01

The development of a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, intensity difference adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using normalized cross correlation (NCC), symmetric uncertainty coefficient, normalized mutual information (NMI), mean square error (MSE) and target registration error (TRE). The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE (0-16%), NCC (0-6%), NMI (0-13%) and TRE (0-34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake.

Afar-wide Crustal Strain Field from Multiple InSAR Tracks

NASA Astrophysics Data System (ADS)

Pagli, C.; Wright, T. J.; Wang, H.; Calais, E.; Bennati Rassion, L. S.; Ebinger, C. J.; Lewi, E.

2010-12-01

Onset of a rifting episode in the Dabbahu volcanic segment, Afar (Ethiopia), in 2005 renewed interest in crustal deformation studies in the area. As a consequence, an extensive geodetic data set, including InSAR and GPS measurements have been acquired over Afar and hold great potential towards improving our understanding of the extensional processes that operate during the final stages of continental rupture. The current geodetic observational and modelling strategy has focused on detailed, localised studies of dyke intrusions and eruptions mainly in the Dabbahu segment. However, an eruption in the Erta ‘Ale volcanic segment in 2008, and cluster of earthquakes observed in the Tat Ale segment, are testament to activity elsewhere in Afar. Here we make use of the vast geodetic dataset available to obtain strain information over the whole Afar depression. A systematic analysis of all the volcanic segments, including Dabbahu, Manda-Hararo, Alayta, Tat ‘Ale Erta Ale and the Djibouti deformation zone, is undertaken. We use InSAR data from multiple tracks together with available GPS measurements to obtain a velocity field model for Afar. We use over 300 radar images acquired by the Envisat satellite in both descending and ascending orbits, from 12 distinct tracks in image and wide swath modes, spanning the time period from October 2005 to present time. We obtain the line-of-sight deformation rates from each InSAR track using a network approach and then combine the InSAR velocities with the GPS observations, as suggested by Wright and Wang (2010) following the method of England and Molnar (1997). A mesh is constructed over the Afar area and then we solve for the horizontal and vertical velocities on each node. The resultant full 3D Afar-wide velocity field shows where current strains are being accumulated within the various volcanic segments of Afar, the width of the plate boundary deformation zone and possible connections between distinct volcanic segments on a regional scale. A comparison of crustal strains from the geodetic analysis with the seismicity data will also be made.

Geodesic active fields--a geometric framework for image registration.

PubMed

Zosso, Dominique; Bresson, Xavier; Thiran, Jean-Philippe

2011-05-01

In this paper we present a novel geometric framework called geodesic active fields for general image registration. In image registration, one looks for the underlying deformation field that best maps one image onto another. This is a classic ill-posed inverse problem, which is usually solved by adding a regularization term. Here, we propose a multiplicative coupling between the registration term and the regularization term, which turns out to be equivalent to embed the deformation field in a weighted minimal surface problem. Then, the deformation field is driven by a minimization flow toward a harmonic map corresponding to the solution of the registration problem. This proposed approach for registration shares close similarities with the well-known geodesic active contours model in image segmentation, where the segmentation term (the edge detector function) is coupled with the regularization term (the length functional) via multiplication as well. As a matter of fact, our proposed geometric model is actually the exact mathematical generalization to vector fields of the weighted length problem for curves and surfaces introduced by Caselles-Kimmel-Sapiro. The energy of the deformation field is measured with the Polyakov energy weighted by a suitable image distance, borrowed from standard registration models. We investigate three different weighting functions, the squared error and the approximated absolute error for monomodal images, and the local joint entropy for multimodal images. As compared to specialized state-of-the-art methods tailored for specific applications, our geometric framework involves important contributions. Firstly, our general formulation for registration works on any parametrizable, smooth and differentiable surface, including nonflat and multiscale images. In the latter case, multiscale images are registered at all scales simultaneously, and the relations between space and scale are intrinsically being accounted for. Second, this method is, to the best of our knowledge, the first reparametrization invariant registration method introduced in the literature. Thirdly, the multiplicative coupling between the registration term, i.e. local image discrepancy, and the regularization term naturally results in a data-dependent tuning of the regularization strength. Finally, by choosing the metric on the deformation field one can freely interpolate between classic Gaussian and more interesting anisotropic, TV-like regularization.

Multi-atlas-based segmentation of the parotid glands of MR images in patients following head-and-neck cancer radiotherapy

NASA Astrophysics Data System (ADS)

Cheng, Guanghui; Yang, Xiaofeng; Wu, Ning; Xu, Zhijian; Zhao, Hongfu; Wang, Yuefeng; Liu, Tian

2013-02-01

Xerostomia (dry mouth), resulting from radiation damage to the parotid glands, is one of the most common and distressing side effects of head-and-neck cancer radiotherapy. Recent MRI studies have demonstrated that the volume reduction of parotid glands is an important indicator for radiation damage and xerostomia. In the clinic, parotid-volume evaluation is exclusively based on physicians' manual contours. However, manual contouring is time-consuming and prone to inter-observer and intra-observer variability. Here, we report a fully automated multi-atlas-based registration method for parotid-gland delineation in 3D head-and-neck MR images. The multi-atlas segmentation utilizes a hybrid deformable image registration to map the target subject to multiple patients' images, applies the transformation to the corresponding segmented parotid glands, and subsequently uses the multiple patient-specific pairs (head-and-neck MR image and transformed parotid-gland mask) to train support vector machine (SVM) to reach consensus to segment the parotid gland of the target subject. This segmentation algorithm was tested with head-and-neck MRIs of 5 patients following radiotherapy for the nasopharyngeal cancer. The average parotid-gland volume overlapped 85% between the automatic segmentations and the physicians' manual contours. In conclusion, we have demonstrated the feasibility of an automatic multi-atlas based segmentation algorithm to segment parotid glands in head-and-neck MR images.

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

PubMed

Cai, Ken; Yang, Rongqian; Yue, Hongwei; Li, Lihua; Ou, Shanxing; Liu, Feng

2017-09-01

Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8 mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences. Copyright © 2016 John Wiley & Sons, Ltd.

TU-AB-202-03: Prediction of PET Transfer Uncertainty by DIR Error Estimating Software, AUTODIRECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, H; Chen, J; Phillips, J

2016-06-15

Purpose: Deformable image registration (DIR) is a powerful tool, but DIR errors can adversely affect its clinical applications. To estimate voxel-specific DIR uncertainty, a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), has been developed and validated. This work tests the ability of this software to predict uncertainty for the transfer of standard uptake values (SUV) from positron-emission tomography (PET) with DIR. Methods: Virtual phantoms are used for this study. Each phantom has a planning computed tomography (CT) image and a diagnostic PET-CT image set. A deformation was digitally applied to the diagnostic CT to create the planningmore » CT image and establish a known deformation between the images. One lung and three rectum patient datasets were employed to create the virtual phantoms. Both of these sites have difficult deformation scenarios associated with them, which can affect DIR accuracy (lung tissue sliding and changes in rectal filling). The virtual phantoms were created to simulate these scenarios by introducing discontinuities in the deformation field at the lung rectum border. The DIR algorithm from Plastimatch software was applied to these phantoms. The SUV mapping errors from the DIR were then compared to that predicted by AUTODIRECT. Results: The SUV error distributions closely followed the AUTODIRECT predicted error distribution for the 4 test cases. The minimum and maximum PET SUVs were produced from AUTODIRECT at 95% confidence interval before applying gradient-based SUV segmentation for each of these volumes. Notably, 93.5% of the target volume warped by the true deformation was included within the AUTODIRECT-predicted maximum SUV volume after the segmentation, while 78.9% of the target volume was within the target volume warped by Plastimatch. Conclusion: The AUTODIRECT framework is able to predict PET transfer uncertainty caused by DIR, which enables an understanding of the associated target volume uncertainty.« less

Automatically tracking neurons in a moving and deforming brain

PubMed Central

Nguyen, Jeffrey P.; Linder, Ashley N.; Plummer, George S.; Shaevitz, Joshua W.

2017-01-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal’s brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches. PMID:28545068

Automatically tracking neurons in a moving and deforming brain.

PubMed

Nguyen, Jeffrey P; Linder, Ashley N; Plummer, George S; Shaevitz, Joshua W; Leifer, Andrew M

2017-05-01

Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals. Brain motion in these recordings pose a unique challenge. The location of individual neurons must be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C. elegans are especially challenging because they undergo very large brain motion and deformation during animal movement. Here we present an automated computer vision pipeline to reliably track populations of neurons with single neuron resolution in the brain of a freely moving C. elegans undergoing large motion and deformation. 3D volumetric fluorescent images of the animal's brain are straightened, aligned and registered, and the locations of neurons in the images are found via segmentation. Each neuron is then assigned an identity using a new time-independent machine-learning approach we call Neuron Registration Vector Encoding. In this approach, non-rigid point-set registration is used to match each segmented neuron in each volume with a set of reference volumes taken from throughout the recording. The way each neuron matches with the references defines a feature vector which is clustered to assign an identity to each neuron in each volume. Finally, thin-plate spline interpolation is used to correct errors in segmentation and check consistency of assigned identities. The Neuron Registration Vector Encoding approach proposed here is uniquely well suited for tracking neurons in brains undergoing large deformations. When applied to whole-brain calcium imaging recordings in freely moving C. elegans, this analysis pipeline located 156 neurons for the duration of an 8 minute recording and consistently found more neurons more quickly than manual or semi-automated approaches.

Model-based registration for assessment of spinal deformities in idiopathic scoliosis

NASA Astrophysics Data System (ADS)

Forsberg, Daniel; Lundström, Claes; Andersson, Mats; Knutsson, Hans

2014-01-01

Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

Joint tumor segmentation and dense deformable registration of brain MR images.

PubMed

Parisot, Sarah; Duffau, Hugues; Chemouny, Stéphane; Paragios, Nikos

2012-01-01

In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.

Weakly supervised automatic segmentation and 3D modeling of the knee joint from MR images

NASA Astrophysics Data System (ADS)

Amami, Amal; Ben Azouz, Zouhour

2013-12-01

Automatic segmentation and 3D modeling of the knee joint from MR images, is a challenging task. Most of the existing techniques require the tedious manual segmentation of a training set of MRIs. We present an approach that necessitates the manual segmentation of one MR image. It is based on a volumetric active appearance model. First, a dense tetrahedral mesh is automatically created on a reference MR image that is arbitrary selected. Second, a pairwise non-rigid registration between each MRI from a training set and the reference MRI is computed. The non-rigid registration is based on a piece-wise affine deformation using the created tetrahedral mesh. The minimum description length is then used to bring all the MR images into a correspondence. An average image and tetrahedral mesh, as well as a set of main modes of variations, are generated using the established correspondence. Any manual segmentation of the average MRI can be mapped to other MR images using the AAM. The proposed approach has the advantage of simultaneously generating 3D reconstructions of the surface as well as a 3D solid model of the knee joint. The generated surfaces and tetrahedral meshes present the interesting property of fulfilling a correspondence between different MR images. This paper shows preliminary results of the proposed approach. It demonstrates the automatic segmentation and 3D reconstruction of a knee joint obtained by mapping a manual segmentation of a reference image.

Registration-based interpolation applied to cardiac MRI

NASA Astrophysics Data System (ADS)

Ólafsdóttir, Hildur; Pedersen, Henrik; Hansen, Michael S.; Lyksborg, Mark; Hansen, Mads Fogtmann; Darkner, Sune; Larsen, Rasmus

2010-03-01

Various approaches have been proposed for segmentation of cardiac MRI. An accurate segmentation of the myocardium and ventricles is essential to determine parameters of interest for the function of the heart, such as the ejection fraction. One problem with MRI is the poor resolution in one dimension. A 3D registration algorithm will typically use a trilinear interpolation of intensities to determine the intensity of a deformed template image. Due to the poor resolution across slices, such linear approximation is highly inaccurate since the assumption of smooth underlying intensities is violated. Registration-based interpolation is based on 2D registrations between adjacent slices and is independent of segmentations. Hence, rather than assuming smoothness in intensity, the assumption is that the anatomy is consistent across slices. The basis for the proposed approach is the set of 2D registrations between each pair of slices, both ways. The intensity of a new slice is then weighted by (i) the deformation functions and (ii) the intensities in the warped images. Unlike the approach by Penney et al. 2004, this approach takes into account deformation both ways, which gives more robustness where correspondence between slices is poor. We demonstrate the approach on a toy example and on a set of cardiac CINE MRI. Qualitative inspection reveals that the proposed approach provides a more convincing transition between slices than images obtained by linear interpolation. A quantitative validation reveals significantly lower reconstruction errors than both linear and registration-based interpolation based on one-way registrations.

Automatic MRI 2D brain segmentation using graph searching technique.

PubMed

Pedoia, Valentina; Binaghi, Elisabetta

2013-09-01

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. Copyright © 2012 John Wiley & Sons, Ltd.

Infantile developmental thoracolumbar kyphosis with segmental subluxation of the spine: report of five patients and review of the literature.

PubMed

Ganesan, S; Karampalis, C; Garrido, E; Tsirikos, A I

2015-07-01

Acute angulation at the thoracolumbar junction with segmental subluxation of the spine occurring at the level above an anteriorly hypoplastic vertebra in otherwise normal children is a rare condition described as infantile developmental thoracolumbar kyphosis. Three patient series with total of 18 children have been reported in the literature. We report five children who presented with thoracolumbar kyphosis and discuss the treatment algorithm. We reviewed the medical records and spinal imaging at initial clinical presentation and at minimum two-year follow-up. The mean age at presentation was eight months (two to 12). All five children had L2 anterior vertebral body hypoplasia. The kyphosis improved spontaneously in three children kept under monitoring. In contrast, the deformity was progressive in two patients who were treated with bracing. The kyphosis and segmental subluxation corrected at latest follow-up (mean age 52 months; 48 to 60) in all patients with near complete reconstitution of the anomalous vertebra. The deformity and radiological imaging on a young child can cause anxiety to both parents and treating physicians. Diagnostic workup and treatment algorithm in the management of infantile developmental thoracolumbar kyphosis is proposed. Observation is indicated for non-progressive kyphosis and bracing if there is evidence of kyphosis and segmental subluxation deterioration beyond walking age. Surgical stabilisation of the spine can be reserved for severe progressive deformities unresponsive to conservative treatment. ©2015 The British Editorial Society of Bone & Joint Surgery.

Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

PubMed

Lee, Wen-Li; Chang, Koyin; Hsieh, Kai-Sheng

2016-09-01

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy.

PubMed

Orczyk, Clément; Rosenkrantz, Andrew B; Mikheev, Artem; Villers, Arnauld; Bernaudin, Myriam; Taneja, Samir S; Valable, Samuel; Rusinek, Henry

2017-12-01

This study aimed to assess a novel method of three-dimensional (3D) co-registration of prostate magnetic resonance imaging (MRI) examinations performed before and after prostate cancer focal therapy. We developed a software platform for automatic 3D deformable co-registration of prostate MRI at different time points and applied this method to 10 patients who underwent focal ablative therapy. MRI examinations were performed preoperatively, as well as 1 week and 6 months post treatment. Rigid registration served as reference for assessing co-registration accuracy and precision. Segmentation of preoperative and postoperative prostate revealed a significant postoperative volume decrease of the gland that averaged 6.49 cc (P = .017). Applying deformable transformation based on mutual information from 120 pairs of MRI slices, we refined by 2.9 mm (max. 6.25 mm) the alignment of the ablation zone, segmented from contrast-enhanced images on the 1-week postoperative examination, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (P = .001), corrected by volume. The dissimilarity by Dice index of the mapped ablation zone using deformable transformation vs rigid control was significantly (P = .04) higher at the ablation site than in the whole gland. Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from mutual information of preoperative and follow-up MRI is accurate in co-registration of MRI examinations performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pursley, Jennifer; Risholm, Petter; Fedorov, Andriy

2012-11-15

Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measuremore » of the associated registration uncertainty. Methods: The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate. Results: The authors observed variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was Less-Than-Or-Slanted-Equal-To 3 mm (95th percentiles within {+-}4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from -0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of Less-Than-Or-Slanted-Equal-To 5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate. Conclusions: Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance.« less

A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization

PubMed Central

Pursley, Jennifer; Risholm, Petter; Fedorov, Andriy; Tuncali, Kemal; Fennessy, Fiona M.; Wells, William M.; Tempany, Clare M.; Cormack, Robert A.

2012-01-01

Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty. Methods: The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate. Results: The authors observed variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was ⩽3 mm (95th percentiles within ±4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from −0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of ⩽5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate. Conclusions: Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance. PMID:23127078

A Complete System for Automatic Extraction of Left Ventricular Myocardium From CT Images Using Shape Segmentation and Contour Evolution

PubMed Central

Zhu, Liangjia; Gao, Yi; Appia, Vikram; Yezzi, Anthony; Arepalli, Chesnal; Faber, Tracy; Stillman, Arthur; Tannenbaum, Allen

2014-01-01

The left ventricular myocardium plays a key role in the entire circulation system and an automatic delineation of the myocardium is a prerequisite for most of the subsequent functional analysis. In this paper, we present a complete system for an automatic segmentation of the left ventricular myocardium from cardiac computed tomography (CT) images using the shape information from images to be segmented. The system follows a coarse-to-fine strategy by first localizing the left ventricle and then deforming the myocardial surfaces of the left ventricle to refine the segmentation. In particular, the blood pool of a CT image is extracted and represented as a triangulated surface. Then, the left ventricle is localized as a salient component on this surface using geometric and anatomical characteristics. After that, the myocardial surfaces are initialized from the localization result and evolved by applying forces from the image intensities with a constraint based on the initial myocardial surface locations. The proposed framework has been validated on 34-human and 12-pig CT images, and the robustness and accuracy are demonstrated. PMID:24723531

A patient-specific segmentation framework for longitudinal MR images of traumatic brain injury

NASA Astrophysics Data System (ADS)

Wang, Bo; Prastawa, Marcel; Irimia, Andrei; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.; Gerig, Guido

2012-02-01

Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Robust, reproducible segmentations of MR images with TBI are crucial for quantitative analysis of recovery and treatment efficacy. However, this is a significant challenge due to severe anatomy changes caused by edema (swelling), bleeding, tissue deformation, skull fracture, and other effects related to head injury. In this paper, we introduce a multi-modal image segmentation framework for longitudinal TBI images. The framework is initialized through manual input of primary lesion sites at each time point, which are then refined by a joint approach composed of Bayesian segmentation and construction of a personalized atlas. The personalized atlas construction estimates the average of the posteriors of the Bayesian segmentation at each time point and warps the average back to each time point to provide the updated priors for Bayesian segmentation. The difference between our approach and segmenting longitudinal images independently is that we use the information from all time points to improve the segmentations. Given a manual initialization, our framework automatically segments healthy structures (white matter, grey matter, cerebrospinal fluid) as well as different lesions such as hemorrhagic lesions and edema. Our framework can handle different sets of modalities at each time point, which provides flexibility in analyzing clinical scans. We show results on three subjects with acute baseline scans and chronic follow-up scans. The results demonstrate that joint analysis of all the points yields improved segmentation compared to independent analysis of the two time points.

Filtering and left ventricle segmentation of the fetal heart in ultrasound images

NASA Astrophysics Data System (ADS)

Vargas-Quintero, Lorena; Escalante-Ramírez, Boris

2013-11-01

In this paper, we propose to use filtering methods and a segmentation algorithm for the analysis of fetal heart in ultrasound images. Since noise speckle makes difficult the analysis of ultrasound images, the filtering process becomes a useful task in these types of applications. The filtering techniques consider in this work assume that the speckle noise is a random variable with a Rayleigh distribution. We use two multiresolution methods: one based on wavelet decomposition and the another based on the Hermite transform. The filtering process is used as way to strengthen the performance of the segmentation tasks. For the wavelet-based approach, a Bayesian estimator at subband level for pixel classification is employed. The Hermite method computes a mask to find those pixels that are corrupted by speckle. On the other hand, we picked out a method based on a deformable model or "snake" to evaluate the influence of the filtering techniques in the segmentation task of left ventricle in fetal echocardiographic images.

Computer object segmentation by nonlinear image enhancement, multidimensional clustering, and geometrically constrained contour optimization

NASA Astrophysics Data System (ADS)

Bruynooghe, Michel M.

1998-04-01

In this paper, we present a robust method for automatic object detection and delineation in noisy complex images. The proposed procedure is a three stage process that integrates image segmentation by multidimensional pixel clustering and geometrically constrained optimization of deformable contours. The first step is to enhance the original image by nonlinear unsharp masking. The second step is to segment the enhanced image by multidimensional pixel clustering, using our reducible neighborhoods clustering algorithm that has a very interesting theoretical maximal complexity. Then, candidate objects are extracted and initially delineated by an optimized region merging algorithm, that is based on ascendant hierarchical clustering with contiguity constraints and on the maximization of average contour gradients. The third step is to optimize the delineation of previously extracted and initially delineated objects. Deformable object contours have been modeled by cubic splines. An affine invariant has been used to control the undesired formation of cusps and loops. Non linear constrained optimization has been used to maximize the external energy. This avoids the difficult and non reproducible choice of regularization parameters, that are required by classical snake models. The proposed method has been applied successfully to the detection of fine and subtle microcalcifications in X-ray mammographic images, to defect detection by moire image analysis, and to the analysis of microrugosities of thin metallic films. The later implementation of the proposed method on a digital signal processor associated to a vector coprocessor would allow the design of a real-time object detection and delineation system for applications in medical imaging and in industrial computer vision.

Non-invasive imaging of zebrafish with spinal deformities using optical coherence tomography: a preliminary study

NASA Astrophysics Data System (ADS)

Bernstein, Liane; Beaudette, Kathy; Patten, Kessen; Beaulieu-Ouellet, Émilie; Strupler, Mathias; Moldovan, Florina; Boudoux, Caroline

2013-03-01

A zebrafish model has recently been introduced to study various genetic mutations that could lead to spinal deformities such as scoliosis. However, current imaging techniques make it difficult to perform longitudinal studies of this condition in zebrafish, especially in the early stages of development. The goal of this project is to determine whether optical coherence tomography (OCT) is a viable non-invasive method to image zebrafish exhibiting spinal deformities. Images of both live and fixed malformed zebrafish (5 to 21 days postfertilization) as well as wild-type fish (5 to 29 days postfertilization) were acquired non-invasively using a commercial SD-OCT system, with a laser source centered at 930nm (λ=100nm), permitting axial and lateral resolutions of 7 and 8μm respectively. Using two-dimensional images and three-dimensional reconstructions, it was possible to identify the malformed notochord as well as deformities in other major organs at different stages of formation. Visualization of the notochord was facilitated with the development of a segmentation algorithm. OCT images were compared to HE histological sections and images obtained by calcein staining. Because of the possibility of performing longitudinal studies on a same fish and reducing image processing time as compared with staining techniques and histology, the use of OCT could facilitate phenotypic characterization in studying genetic factors leading to spinal deformities in zebrafish and could eventually contribute to the identification of the genetic causes of spinal deformities such as scoliosis.

Analysis of electrodes' placement and deformation in deep brain stimulation from medical images

NASA Astrophysics Data System (ADS)

Mehri, Maroua; Lalys, Florent; Maumet, Camille; Haegelen, Claire; Jannin, Pierre

2012-02-01

Deep brain stimulation (DBS) is used to reduce the motor symptoms such as rigidity or bradykinesia, in patients with Parkinson's disease (PD). The Subthalamic Nucleus (STN) has emerged as prime target of DBS in idiopathic PD. However, DBS surgery is a difficult procedure requiring the exact positioning of electrodes in the pre-operative selected targets. This positioning is usually planned using patients' pre-operative images, along with digital atlases, assuming that electrode's trajectory is linear. However, it has been demonstrated that anatomical brain deformations induce electrode's deformations resulting in errors in the intra-operative targeting stage. In order to meet the need of a higher degree of placement accuracy and to help constructing a computer-aided-placement tool, we studied the electrodes' deformation in regards to patients' clinical data (i.e., sex, mean PD duration and brain atrophy index). Firstly, we presented an automatic algorithm for the segmentation of electrode's axis from post-operative CT images, which aims to localize the electrodes' stimulated contacts. To assess our method, we applied our algorithm on 25 patients who had undergone bilateral STNDBS. We found a placement error of 0.91+/-0.38 mm. Then, from the segmented axis, we quantitatively analyzed the electrodes' curvature and correlated it with patients' clinical data. We found a positive significant correlation between mean curvature index of the electrode and brain atrophy index for male patients and between mean curvature index of the electrode and mean PD duration for female patients. These results help understanding DBS electrode' deformations and would help ensuring better anticipation of electrodes' placement.

Image-Guided Abdominal Surgery and Therapy Delivery

PubMed Central

Galloway, Robert L.; Herrell, S. Duke; Miga, Michael I.

2013-01-01

Image-Guided Surgery has become the standard of care in intracranial neurosurgery providing more exact resections while minimizing damage to healthy tissue. Moving that process to abdominal organs presents additional challenges in the form of image segmentation, image to physical space registration, organ motion and deformation. In this paper, we present methodologies and results for addressing these challenges in two specific organs: the liver and the kidney. PMID:25077012

Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming.

PubMed

Pitiot, Alain; Toga, Arthur W; Thompson, Paul M

2002-08-01

This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequent quantitative analysis. We propose a new hybrid strategy that combines a general elastic template matching approach and an evolutionary heuristic. The evolutionary algorithm uses prior statistical information about the shape of the target structure to control the behavior of a number of deformable templates. Each template, modeled in the form of a B-spline, is warped in a potential field which is itself dynamically adapted. Such a hybrid scheme proves to be promising: by maintaining a population of templates, we cover a large domain of the solution space under the global guidance of the evolutionary heuristic, and thoroughly explore interesting areas. We address key issues of automated image segmentation systems. The potential fields are initially designed based on the spatial features of the edges in the input image, and are subjected to spatially adaptive diffusion to guarantee the deformation of the template. This also improves its global consistency and convergence speed. The deformation algorithm can modify the internal structure of the templates to allow a better match. We investigate in detail the preprocessing phase that the images undergo before they can be used more effectively in the iterative elastic matching procedure: a texture classifier, trained via linear discriminant analysis of a learning set, is used to enhance the contrast of the target structure with respect to surrounding tissues. We show how these techniques interact within a statistically driven evolutionary scheme to achieve a better tradeoff between template flexibility and sensitivity to noise and outliers. We focus on understanding the features of template matching that are most beneficial in terms of the achieved match. Examples from simulated and real image data are discussed, with considerations of algorithmic efficiency.

Segmentation of radiographic images under topological constraints: application to the femur.

PubMed

Gamage, Pavan; Xie, Sheng Quan; Delmas, Patrice; Xu, Wei Liang

2010-09-01

A framework for radiographic image segmentation under topological control based on two-dimensional (2D) image analysis was developed. The system is intended for use in common radiological tasks including fracture treatment analysis, osteoarthritis diagnostics and osteotomy management planning. The segmentation framework utilizes a generic three-dimensional (3D) model of the bone of interest to define the anatomical topology. Non-rigid registration is performed between the projected contours of the generic 3D model and extracted edges of the X-ray image to achieve the segmentation. For fractured bones, the segmentation requires an additional step where a region-based active contours curve evolution is performed with a level set Mumford-Shah method to obtain the fracture surface edge. The application of the segmentation framework to analysis of human femur radiographs was evaluated. The proposed system has two major innovations. First, definition of the topological constraints does not require a statistical learning process, so the method is generally applicable to a variety of bony anatomy segmentation problems. Second, the methodology is able to handle both intact and fractured bone segmentation. Testing on clinical X-ray images yielded an average root mean squared distance (between the automatically segmented femur contour and the manual segmented ground truth) of 1.10 mm with a standard deviation of 0.13 mm. The proposed point correspondence estimation algorithm was benchmarked against three state-of-the-art point matching algorithms, demonstrating successful non-rigid registration for the cases of interest. A topologically constrained automatic bone contour segmentation framework was developed and tested, providing robustness to noise, outliers, deformations and occlusions.

Active telescope systems; Proceedings of the Meeting, Orlando, FL, Mar. 28-31, 1989

NASA Astrophysics Data System (ADS)

Roddier, Francois J.

1989-09-01

The present conference discusses topics in the fundamental limitations of adaptive optics in astronomical telescopy, integrated telescope systems designs, novel components for adaptive telescopes, active interferometry, flexible-mirror and segmented-mirror telescopes, and various aspects of the NASA Precision Segmented Reflectors Program. Attention is given to near-ground atmospheric turbulence effects, a near-IR astronomical adaptive optics system, a simplified wavefront sensor for adaptive mirror control, excimer laser guide star techniques for adaptive astronomical imaging, active systems in long-baseline interferometry, mirror figure control primitives for a 10-m primary mirror, and closed-loop active optics for large flexible mirrors subject to wind buffet deformations. Also discussed are active pupil geometry control for a phased-array telescope, extremely lightweight space telescope mirrors, segmented-mirror manufacturing tolerances, and composite deformable mirror design.

MR and CT data with multiobserver delineations of organs in the pelvic area-Part of the Gold Atlas project.

PubMed

Nyholm, Tufve; Svensson, Stina; Andersson, Sebastian; Jonsson, Joakim; Sohlin, Maja; Gustafsson, Christian; Kjellén, Elisabeth; Söderström, Karin; Albertsson, Per; Blomqvist, Lennart; Zackrisson, Björn; Olsson, Lars E; Gunnlaugsson, Adalsteinn

2018-03-01

We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. The dataset has been made publically available to be used for academic purposes, and can be accessed from https://zenodo.org/record/583096. The dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm. © 2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Automatic and hierarchical segmentation of the human skeleton in CT images.

PubMed

Fu, Yabo; Liu, Shi; Li, Harold; Yang, Deshan

2017-04-07

Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic vertebrae, and 1.45 mm for pelvis bones.

Automatic and hierarchical segmentation of the human skeleton in CT images

NASA Astrophysics Data System (ADS)

Fu, Yabo; Liu, Shi; Li, H. Harold; Yang, Deshan

2017-04-01

Accurate segmentation of each bone of the human skeleton is useful in many medical disciplines. The results of bone segmentation could facilitate bone disease diagnosis and post-treatment assessment, and support planning and image guidance for many treatment modalities including surgery and radiation therapy. As a medium level medical image processing task, accurate bone segmentation can facilitate automatic internal organ segmentation by providing stable structural reference for inter- or intra-patient registration and internal organ localization. Even though bones in CT images can be visually observed with minimal difficulty due to the high image contrast between the bony structures and surrounding soft tissues, automatic and precise segmentation of individual bones is still challenging due to the many limitations of the CT images. The common limitations include low signal-to-noise ratio, insufficient spatial resolution, and indistinguishable image intensity between spongy bones and soft tissues. In this study, a novel and automatic method is proposed to segment all the major individual bones of the human skeleton above the upper legs in CT images based on an articulated skeleton atlas. The reported method is capable of automatically segmenting 62 major bones, including 24 vertebrae and 24 ribs, by traversing a hierarchical anatomical tree and by using both rigid and deformable image registration. The degrees of freedom of femora and humeri are modeled to support patients in different body and limb postures. The segmentation results are evaluated using the Dice coefficient and point-to-surface error (PSE) against manual segmentation results as the ground-truth. The results suggest that the reported method can automatically segment and label the human skeleton into detailed individual bones with high accuracy. The overall average Dice coefficient is 0.90. The average PSEs are 0.41 mm for the mandible, 0.62 mm for cervical vertebrae, 0.92 mm for thoracic vertebrae, and 1.45 mm for pelvis bones.

Segmentation of pelvic structures for planning CT using a geometrical shape model tuned by a multi-scale edge detector

PubMed Central

Martínez, Fabio; Romero, Eduardo; Dréan, Gaël; Simon, Antoine; Haigron, Pascal; De Crevoisier, Renaud; Acosta, Oscar

2014-01-01

Accurate segmentation of the prostate and organs at risk in computed tomography (CT) images is a crucial step for radiotherapy (RT) planning. Manual segmentation, as performed nowadays, is a time consuming process and prone to errors due to the a high intra- and inter-expert variability. This paper introduces a new automatic method for prostate, rectum and bladder segmentation in planning CT using a geometrical shape model under a Bayesian framework. A set of prior organ shapes are first built by applying Principal Component Analysis (PCA) to a population of manually delineated CT images. Then, for a given individual, the most similar shape is obtained by mapping a set of multi-scale edge observations to the space of organs with a customized likelihood function. Finally, the selected shape is locally deformed to adjust the edges of each organ. Experiments were performed with real data from a population of 116 patients treated for prostate cancer. The data set was split in training and test groups, with 30 and 86 patients, respectively. Results show that the method produces competitive segmentations w.r.t standard methods (Averaged Dice = 0.91 for prostate, 0.94 for bladder, 0.89 for Rectum) and outperforms the majority-vote multi-atlas approaches (using rigid registration, free-form deformation (FFD) and the demons algorithm) PMID:24594798

Multiscale approach to contour fitting for MR images

NASA Astrophysics Data System (ADS)

Rueckert, Daniel; Burger, Peter

1996-04-01

We present a new multiscale contour fitting process which combines information about the image and the contour of the object at different levels of scale. The algorithm is based on energy minimizing deformable models but avoids some of the problems associated with these models. The segmentation algorithm starts by constructing a linear scale-space of an image through convolution of the original image with a Gaussian kernel at different levels of scale, where the scale corresponds to the standard deviation of the Gaussian kernel. At high levels of scale large scale features of the objects are preserved while small scale features, like object details as well as noise, are suppressed. In order to maximize the accuracy of the segmentation, the contour of the object of interest is then tracked in scale-space from coarse to fine scales. We propose a hybrid multi-temperature simulated annealing optimization to minimize the energy of the deformable model. At high levels of scale the SA optimization is started at high temperatures, enabling the SA optimization to find a global optimal solution. At lower levels of scale the SA optimization is started at lower temperatures (at the lowest level the temperature is close to 0). This enforces a more deterministic behavior of the SA optimization at lower scales and leads to an increasingly local optimization as high energy barriers cannot be crossed. The performance and robustness of the algorithm have been tested on spin-echo MR images of the cardiovascular system. The task was to segment the ascending and descending aorta in 15 datasets of different individuals in order to measure regional aortic compliance. The results show that the algorithm is able to provide more accurate segmentation results than the classic contour fitting process and is at the same time very robust to noise and initialization.

Correcting Surface Figure Error in Imaging Satellites Using a Deformable Mirror

DTIC Science & Technology

2013-12-01

background understanding about the Naval Postgraduate School’s SMT test- bed and the required performance for mirror surface figures. The...Postgraduate School. Larger than the Hubble Space Telescope, but smaller than the JWST (see Figure 2), the SMT is an advanced test- bed to research the...orientation (from [3]). The six segments of the primary mirror have a lightweight, deformable, nano- laminate face with actuators across the rear

Right ventricle functional parameters estimation in arrhythmogenic right ventricular dysplasia using a robust shape based deformable model.

PubMed

Oghli, Mostafa Ghelich; Dehlaghi, Vahab; Zadeh, Ali Mohammad; Fallahi, Alireza; Pooyan, Mohammad

2014-07-01

Assessment of cardiac right-ventricle functions plays an essential role in diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). Among clinical tests, cardiac magnetic resonance imaging (MRI) is now becoming the most valid imaging technique to diagnose ARVD. Fatty infiltration of the right ventricular free wall can be visible on cardiac MRI. Finding right-ventricle functional parameters from cardiac MRI images contains segmentation of right-ventricle in each slice of end diastole and end systole phases of cardiac cycle and calculation of end diastolic and end systolic volume and furthermore other functional parameters. The main problem of this task is the segmentation part. We used a robust method based on deformable model that uses shape information for segmentation of right-ventricle in short axis MRI images. After segmentation of right-ventricle from base to apex in end diastole and end systole phases of cardiac cycle, volume of right-ventricle in these phases calculated and then, ejection fraction calculated. We performed a quantitative evaluation of clinical cardiac parameters derived from the automatic segmentation by comparison against a manual delineation of the ventricles. The manually and automatically determined quantitative clinical parameters were statistically compared by means of linear regression. This fits a line to the data such that the root-mean-square error (RMSE) of the residuals is minimized. The results show low RMSE for Right Ventricle Ejection Fraction and Volume (≤ 0.06 for RV EF, and ≤ 10 mL for RV volume). Evaluation of segmentation results is also done by means of four statistical measures including sensitivity, specificity, similarity index and Jaccard index. The average value of similarity index is 86.87%. The Jaccard index mean value is 83.85% which shows a good accuracy of segmentation. The average of sensitivity is 93.9% and mean value of the specificity is 89.45%. These results show the reliability of proposed method in these cases that manual segmentation is inapplicable. Huge shape variety of right-ventricle led us to use a shape prior based method and this work can develop by four-dimensional processing for determining the first ventricular slices.

Temporal deformation pattern in acute and late phases of ST-elevation myocardial infarction: incremental value of longitudinal post-systolic strain to assess myocardial viability.

PubMed

Huttin, Olivier; Marie, Pierre-Yves; Benichou, Maxime; Bozec, Erwan; Lemoine, Simon; Mandry, Damien; Juillière, Yves; Sadoul, Nicolas; Micard, Emilien; Duarte, Kevin; Beaumont, Marine; Rossignol, Patrick; Girerd, Nicolas; Selton-Suty, Christine

2016-10-01

Identification of transmural extent and degree of non-viability after ST-segment elevation myocardial infarction (STEMI) is clinically important. The objective of the present study was to assess the regional mechanics and temporal deformation patterns using speckle tracking echocardiography (STE) in acute and later phases of STEMI to predict myocardial damage in these patients. Ninety-eight patients with first STEMI underwent both echocardiography and cardiac magnetic resonance imaging in acute phase and at 6 months follow-up with 2D STE-derived measurements of peak longitudinal strain (PLS), Pre-STretch index (PST) and post-systolic deformation index (PSI). For each segment, late gadolinium enhancement (LGE) was defined as transmural (LGE >66 %) or non-transmural (<66 %). Global deformation values were significantly correlated with LVEFCMR and infarct size at both visits. A significantly lower value of segmental PLS and higher PSI and PST in necrotic segments were observed comparatively to control, adjacent and remote segments. The best parameters to predict transmural extent in acute phase were PSI with a cutoff value of 8 % (AUC: 0.84) and PLS with a cutoff value of -13 % (AUC: 0.86). PST showed high specificity, but poor sensitivity in predicting transmural extent. More importantly, the addition of PSI and PST to PLS in acute phase was associated with improved prediction of viability at 6 months (integrated discrimination improvement 2.5 % p < 0.01; net reclassification improvement 27 %; p < 0.01). All systolic deformation values separated transmural from non-transmural scarring. PLS combined with additional information relative to post-systolic deformation appears to be the most informative parameters to predict the transmural extent of MI in the early and late phases of MI. http://clinicaltrials.gov/show/NCT01109225 ; NCT01109225.

Object-constrained meshless deformable algorithm for high speed 3D nonrigid registration between CT and CBCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen Ting; Kim, Sung; Goyal, Sharad

2010-01-15

Purpose: High-speed nonrigid registration between the planning CT and the treatment CBCT data is critical for real time image guided radiotherapy (IGRT) to improve the dose distribution and to reduce the toxicity to adjacent organs. The authors propose a new fully automatic 3D registration framework that integrates object-based global and seed constraints with the grayscale-based ''demons'' algorithm. Methods: Clinical objects were segmented on the planning CT images and were utilized as meshless deformable models during the nonrigid registration process. The meshless models reinforced a global constraint in addition to the grayscale difference between CT and CBCT in order to maintainmore » the shape and the volume of geometrically complex 3D objects during the registration. To expedite the registration process, the framework was stratified into hierarchies, and the authors used a frequency domain formulation to diffuse the displacement between the reference and the target in each hierarchy. Also during the registration of pelvis images, they replaced the air region inside the rectum with estimated pixel values from the surrounding rectal wall and introduced an additional seed constraint to robustly track and match the seeds implanted into the prostate. The proposed registration framework and algorithm were evaluated on 15 real prostate cancer patients. For each patient, prostate gland, seminal vesicle, bladder, and rectum were first segmented by a radiation oncologist on planning CT images for radiotherapy planning purpose. The same radiation oncologist also manually delineated the tumor volumes and critical anatomical structures in the corresponding CBCT images acquired at treatment. These delineated structures on the CBCT were only used as the ground truth for the quantitative validation, while structures on the planning CT were used both as the input to the registration method and the ground truth in validation. By registering the planning CT to the CBCT, a displacement map was generated. Segmented volumes in the CT images deformed using the displacement field were compared against the manual segmentations in the CBCT images to quantitatively measure the convergence of the shape and the volume. Other image features were also used to evaluate the overall performance of the registration. Results: The algorithm was able to complete the segmentation and registration process within 1 min, and the superimposed clinical objects achieved a volumetric similarity measure of over 90% between the reference and the registered data. Validation results also showed that the proposed registration could accurately trace the deformation inside the target volume with average errors of less than 1 mm. The method had a solid performance in registering the simulated images with up to 20 Hounsfield unit white noise added. Also, the side by side comparison with the original demons algorithm demonstrated its improved registration performance over the local pixel-based registration approaches. Conclusions: Given the strength and efficiency of the algorithm, the proposed method has significant clinical potential to accelerate and to improve the CBCT delineation and targets tracking in online IGRT applications.« less

SU-E-I-87: Automated Liver Segmentation Method for CBCT Dataset by Combining Sparse Shape Composition and Probabilistic Atlas Construction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Dengwang; Liu, Li; Chen, Jinhu

2014-06-01

Purpose: The aiming of this study was to extract liver structures for daily Cone beam CT (CBCT) images automatically. Methods: Datasets were collected from 50 intravenous contrast planning CT images, which were regarded as training dataset for probabilistic atlas and shape prior model construction. Firstly, probabilistic atlas and shape prior model based on sparse shape composition (SSC) were constructed by iterative deformable registration. Secondly, the artifacts and noise were removed from the daily CBCT image by an edge-preserving filtering using total variation with L1 norm (TV-L1). Furthermore, the initial liver region was obtained by registering the incoming CBCT image withmore » the atlas utilizing edge-preserving deformable registration with multi-scale strategy, and then the initial liver region was converted to surface meshing which was registered with the shape model where the major variation of specific patient was modeled by sparse vectors. At the last stage, the shape and intensity information were incorporated into joint probabilistic model, and finally the liver structure was extracted by maximum a posteriori segmentation.Regarding the construction process, firstly the manually segmented contours were converted into meshes, and then arbitrary patient data was chosen as reference image to register with the rest of training datasets by deformable registration algorithm for constructing probabilistic atlas and prior shape model. To improve the efficiency of proposed method, the initial probabilistic atlas was used as reference image to register with other patient data for iterative construction for removing bias caused by arbitrary selection. Results: The experiment validated the accuracy of the segmentation results quantitatively by comparing with the manually ones. The volumetric overlap percentage between the automatically generated liver contours and the ground truth were on an average 88%–95% for CBCT images. Conclusion: The experiment demonstrated that liver structures of CBCT with artifacts can be extracted accurately for following adaptive radiation therapy. This work is supported by National Natural Science Foundation of China (No. 61201441), Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (No. BS2012DX038), Project of Shandong Province Higher Educational Science and Technology Program (No. J12LN23), Jinan youth science and technology star (No.20120109)« less

Temporal subtraction contrast-enhanced dedicated breast CT

PubMed Central

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-01-01

Purpose To develop a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. Methods An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, Intensity Difference Adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using Normalized Cross Correlation (NCC), Symmetric Uncertainty Coefficient (SUC), Normalized Mutual Information (NMI), Mean Square Error (MSE) and Target Registration Error (TRE). Results The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE(0–16%), NCC (0–6%), NMI (0–13%) and TRE (0–34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Conclusion Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake. PMID:27494376

Impact of large field angles on the requirements for deformable mirror in imaging satellites

NASA Astrophysics Data System (ADS)

Kim, Jae Jun; Mueller, Mark; Martinez, Ty; Agrawal, Brij

2018-04-01

For certain imaging satellite missions, a large aperture with wide field-of-view is needed. In order to achieve diffraction limited performance, the mirror surface Root Mean Square (RMS) error has to be less than 0.05 waves. In the case of visible light, it has to be less than 30 nm. This requirement is difficult to meet as the large aperture will need to be segmented in order to fit inside a launch vehicle shroud. To reduce this requirement and to compensate for the residual wavefront error, Micro-Electro-Mechanical System (MEMS) deformable mirrors can be considered in the aft optics of the optical system. MEMS deformable mirrors are affordable and consume low power, but are small in size. Due to the major reduction in pupil size for the deformable mirror, the effective field angle is magnified by the diameter ratio of the primary and deformable mirror. For wide field of view imaging, the required deformable mirror correction is field angle dependant, impacting the required parameters of a deformable mirror such as size, number of actuators, and actuator stroke. In this paper, a representative telescope and deformable mirror system model is developed and the deformable mirror correction is simulated to study the impact of the large field angles in correcting a wavefront error using a deformable mirror in the aft optics.

GLISTR: Glioma Image Segmentation and Registration

PubMed Central

Pohl, Kilian M.; Bilello, Michel; Cirillo, Luigi; Biros, George; Melhem, Elias R.; Davatzikos, Christos

2015-01-01

We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels. The proposed method is based on the expectation maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the original atlas into one with tumor and edema adapted to best match a given set of patient’s images. The modified atlas is registered into the patient space and utilized for estimating the posterior probabilities of various tissue labels. EM iteratively refines the estimates of the posterior probabilities of tissue labels, the deformation field and the tumor growth model parameters. Hence, in addition to segmentation, the proposed method results in atlas registration and a low-dimensional description of the patient scans through estimation of tumor model parameters. We validate the method by automatically segmenting 10 MR scans and comparing the results to those produced by clinical experts and two state-of-the-art methods. The resulting segmentations of tumor and edema outperform the results of the reference methods, and achieve a similar accuracy from a second human rater. We additionally apply the method to 122 patients scans and report the estimated tumor model parameters and their relations with segmentation and registration results. Based on the results from this patient population, we construct a statistical atlas of the glioma by inverting the estimated deformation fields to warp the tumor segmentations of patients scans into a common space. PMID:22907965

Automatic 3D segmentation of spinal cord MRI using propagated deformable models

NASA Astrophysics Data System (ADS)

De Leener, B.; Cohen-Adad, J.; Kadoury, S.

2014-03-01

Spinal cord diseases or injuries can cause dysfunction of the sensory and locomotor systems. Segmentation of the spinal cord provides measures of atrophy and allows group analysis of multi-parametric MRI via inter-subject registration to a template. All these measures were shown to improve diagnostic and surgical intervention. We developed a framework to automatically segment the spinal cord on T2-weighted MR images, based on the propagation of a deformable model. The algorithm is divided into three parts: first, an initialization step detects the spinal cord position and orientation by using the elliptical Hough transform on multiple adjacent axial slices to produce an initial tubular mesh. Second, a low-resolution deformable model is iteratively propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a contrast adaptation at each iteration. Third, a refinement process and a global deformation are applied on the low-resolution mesh to provide an accurate segmentation of the spinal cord. Our method was evaluated against a semi-automatic edge-based snake method implemented in ITK-SNAP (with heavy manual adjustment) by computing the 3D Dice coefficient, mean and maximum distance errors. Accuracy and robustness were assessed from 8 healthy subjects. Each subject had two volumes: one at the cervical and one at the thoracolumbar region. Results show a precision of 0.30 +/- 0.05 mm (mean absolute distance error) in the cervical region and 0.27 +/- 0.06 mm in the thoracolumbar region. The 3D Dice coefficient was of 0.93 for both regions.

The use of the Kalman filter in the automated segmentation of EIT lung images.

PubMed

Zifan, A; Liatsis, P; Chapman, B E

2013-06-01

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.

Whole vertebral bone segmentation method with a statistical intensity-shape model based approach

NASA Astrophysics Data System (ADS)

Hanaoka, Shouhei; Fritscher, Karl; Schuler, Benedikt; Masutani, Yoshitaka; Hayashi, Naoto; Ohtomo, Kuni; Schubert, Rainer

2011-03-01

An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939 mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar vertebrae.

Tectonic evolution of Gorda Ridge inferred from sidescan sonar images

USGS Publications Warehouse

Masson, D.G.; Cacchione, D.A.; Drake, D.E.

1988-01-01

Gorda Ridge is the southern segment of the Juan de Fuca Ridge complex, in the north-east Pacific. Along-strike spreading-rate variation on Gorda Ridge and deformation of Gorda Plate are evidence for compression between the Pacific and Gorda Plates. GLORIA sidescan sonographs allow the spreading fabric associated with Gorda Ridge to be mapped in detail. Between 5 and 2 Ma, a pair of propagating rifts re-orientated the northern segment of Gorda Ridge by about 10?? clockwise, accommodating a clockwise shift in Pacific-Juan de Fuca plate motion that occurred around 5 Ma. Deformation of Gorda Plate, associated with southward decreasing spreading rates along southern Gorda Ridge, is accommodated by a combination of clockwise rotation of Gorda Plate crust, coupled with left-lateral motion on the original normal faults of the ocean crust. Segments of Gorda Plate which have rotated by different amounts are separated by narrow deformation zones across which sharp changes in ocean fabric trend are seen. Although minor lateral movement may occur on these NW to WNW structures, no major right-lateral movement, as predicted by previous models, is observed. ?? 1988 Kluwer Academic Publishers.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

NASA Astrophysics Data System (ADS)

Luiza Bondar, M.; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-01

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer.

PubMed

Bondar, M Luiza; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-07

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Adaptive deformable model for colonic polyp segmentation and measurement on CT colonography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yao Jianhua; Summers, Ronald M.

2007-05-15

Polyp size is one important biomarker for the malignancy risk of a polyp. This paper presents an improved approach for colonic polyp segmentation and measurement on CT colonography images. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy clustering, and adaptive deformable model. Since polyps on haustral folds are the most difficult to be segmented, we propose a dual-distance algorithm to first identify voxels on the folds, and then introduce a counter-force to control the model evolution. We derive linear and volumetric measurements from the segmentation. The experiment was conducted on 395 patients with 83 polyps, ofmore » which 43 polyps were on haustral folds. The results were validated against manual measurement from the optical colonoscopy and the CT colonography. The paired t-test showed no significant difference, and the R{sup 2} correlation was 0.61 for the linear measurement and 0.98 for the volumetric measurement. The mean Dice coefficient for volume overlap between automatic and manual segmentation was 0.752 (standard deviation 0.154)« less

TU-AB-BRA-06: BEST IN PHYSICS (JOINT IMAGING-THERAPY): An MRI Compatible Externally and Internally Deformable Lung Motion Phantom for Multi-Modality IGRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sabouri, P; Sawant, A; Arai, T

Purpose: MRI has become an attractive tool for tumor motion management. Current MR-compatible phantoms are only capable of reproducing translational motion. This study describes the construction and validation of a more realistic, MRI-compatible lung phantom that is deformable internally as well as externally. We demonstrate a radiotherapy application of this phantom by validating the geometric accuracy of the open-source deformable image registration software NiftyReg (UCL, UK). Methods: The outer shell of a commercially-available dynamic breathing torso phantom was filled with natural latex foam with eleven water tubes. A rigid foam cut-out served as the diaphragm. A high-precision programmable, in-house, MRI-compatiblemore » motion platform was used to drive the diaphragm. The phantom was imaged on a 3T scanner (Philips, Ingenia). Twenty seven tumor traces previously recorded from lung cancer patients were programmed into the phantom and 2D+t image sequences were acquired using a sparse-sampling sequence k-t BLAST (accn=3, resolution=0.66×0.66×5mm3; acquisition-time=110ms/slice). The geometric fidelity of the MRI-derived trajectories was validated against those obtained via fluoroscopy using the on board kV imager on a Truebeam linac. NiftyReg was used to perform frame by frame deformable image registration. The location of each marker predicted by using NiftyReg was compared with the values calculated by intensity-based segmentation on each frame. Results: In all cases, MR trajectories were within 1 mm of corresponding fluoroscopy trajectories. RMSE between centroid positions obtained from segmentation with those obtained by NiftyReg varies from 0.1 to 0.21 mm in the SI direction and 0.08 to 0.13 mm in the LR direction showing the high accuracy of deformable registration. Conclusion: We have successfully designed and demonstrated a phantom that can accurately reproduce deformable motion under a variety of imaging modalities including MRI, CT and x-ray fluodoscopy, making it an invaluable research tool for validating novel motion management strategies. This work was partially supported through research funding from National Institutes of Health (R01CA169102).« less

Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Jun; McKenzie, Elizabeth; Fan, Zhaoyang

Purpose: To denoise self-gated k-space sorted 4-dimensional magnetic resonance imaging (SG-KS-4D-MRI) by applying a nonlocal means denoising filter, block-matching and 3-dimensional filtering (BM3D), to test its impact on the accuracy of 4D image deformable registration and automated tumor segmentation for pancreatic cancer patients. Methods and Materials: Nine patients with pancreatic cancer and abdominal SG-KS-4D-MRI were included in the study. Block-matching and 3D filtering was adapted to search in the axial slices/frames adjacent to the reference image patch in the spatial and temporal domains. The patches with high similarity to the reference patch were used to collectively denoise the 4D-MRI image. Themore » pancreas tumor was manually contoured on the first end-of-exhalation phase for both the raw and the denoised 4D-MRI. B-spline deformable registration was applied to the subsequent phases for contour propagation. The consistency of tumor volume defined by the standard deviation of gross tumor volumes from 10 breathing phases (σ-GTV), tumor motion trajectories in 3 cardinal motion planes, 4D-MRI imaging noise, and image contrast-to-noise ratio were compared between the raw and denoised groups. Results: Block-matching and 3D filtering visually and quantitatively reduced image noise by 52% and improved image contrast-to-noise ratio by 56%, without compromising soft tissue edge definitions. Automatic tumor segmentation is statistically more consistent on the denoised 4D-MRI (σ-GTV = 0.6 cm{sup 3}) than on the raw 4D-MRI (σ-GTV = 0.8 cm{sup 3}). Tumor end-of-exhalation location is also more reproducible on the denoised 4D-MRI than on the raw 4D-MRI in all 3 cardinal motion planes. Conclusions: Block-matching and 3D filtering can significantly reduce random image noise while maintaining structural features in the SG-KS-4D-MRI datasets. In this study of pancreatic tumor segmentation, automatic segmentation of GTV in the registered image sets is shown to be more consistent on the denoised 4D-MRI than on the raw 4D-MRI.« less

Automatic knee cartilage delineation using inheritable segmentation

NASA Astrophysics Data System (ADS)

Dries, Sebastian P. M.; Pekar, Vladimir; Bystrov, Daniel; Heese, Harald S.; Blaffert, Thomas; Bos, Clemens; van Muiswinkel, Arianne M. C.

2008-03-01

We present a fully automatic method for segmentation of knee joint cartilage from fat suppressed MRI. The method first applies 3-D model-based segmentation technology, which allows to reliably segment the femur, patella, and tibia by iterative adaptation of the model according to image gradients. Thin plate spline interpolation is used in the next step to position deformable cartilage models for each of the three bones with reference to the segmented bone models. After initialization, the cartilage models are fine adjusted by automatic iterative adaptation to image data based on gray value gradients. The method has been validated on a collection of 8 (3 left, 5 right) fat suppressed datasets and demonstrated the sensitivity of 83+/-6% compared to manual segmentation on a per voxel basis as primary endpoint. Gross cartilage volume measurement yielded an average error of 9+/-7% as secondary endpoint. For cartilage being a thin structure, already small deviations in distance result in large errors on a per voxel basis, rendering the primary endpoint a hard criterion.

Mean curvature and texture constrained composite weighted random walk algorithm for optic disc segmentation towards glaucoma screening.

PubMed

Panda, Rashmi; Puhan, N B; Panda, Ganapati

2018-02-01

Accurate optic disc (OD) segmentation is an important step in obtaining cup-to-disc ratio-based glaucoma screening using fundus imaging. It is a challenging task because of the subtle OD boundary, blood vessel occlusion and intensity inhomogeneity. In this Letter, the authors propose an improved version of the random walk algorithm for OD segmentation to tackle such challenges. The algorithm incorporates the mean curvature and Gabor texture energy features to define the new composite weight function to compute the edge weights. Unlike the deformable model-based OD segmentation techniques, the proposed algorithm remains unaffected by curve initialisation and local energy minima problem. The effectiveness of the proposed method is verified with DRIVE, DIARETDB1, DRISHTI-GS and MESSIDOR database images using the performance measures such as mean absolute distance, overlapping ratio, dice coefficient, sensitivity, specificity and precision. The obtained OD segmentation results and quantitative performance measures show robustness and superiority of the proposed algorithm in handling the complex challenges in OD segmentation.

Comparative study on different types of segmented micro deformable mirrors

NASA Astrophysics Data System (ADS)

Qiao, Dayong; Yuan, Weizheng; Li, Kaicheng; Li, Xiaoying; Rao, Fubo

2006-02-01

In an adaptive-optical (AO) system, the wavefront of optical beam can be corrected with deformable mirror (DM). Based on MicroElectroMechanical System (MEMS) technology, segmented micro deformable mirrors can be built with denser actuator spacing than continuous face-sheet designs and have been widely researched. But the influence of the segment structure has not been thoroughly discussed until now. In this paper, the design, performance and fabrication of several micromachined, segmented deformable mirror for AO were investigated. The wavefront distorted by atmospheric turbulence was simulated in the frame of Kolmogorov turbulence model. Position function was used to describe the surfaces of the micro deformable mirrors in working state. The performances of deformable mirrors featuring square, brick, hexagonal and ring segment structures were evaluated in criteria of phase fitting error, the Strehl ratio after wavefront correction and the design considerations. Then the micro fabrication process and mask layout were designed and the fabrication of micro deformable mirrors was implemented. The results show that the micro deformable mirror with ring segments performs the best, but it is very difficult in terms of layout design. The micro deformable mirrors with square and brick segments are easy to design, but their performances are not good. The micro deformable mirror with hexagonal segments has not only good performance in terms of phase fitting error, the Strehl ratio and actuation voltage, but also no overwhelming difficulty in layout design.

Implementation and evaluation of various demons deformable image registration algorithms on a GPU.

PubMed

Gu, Xuejun; Pan, Hubert; Liang, Yun; Castillo, Richard; Yang, Deshan; Choi, Dongju; Castillo, Edward; Majumdar, Amitava; Guerrero, Thomas; Jiang, Steve B

2010-01-07

Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A gray-scale-based DIR algorithm called demons and five of its variants were implemented on GPUs using the compute unified device architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4D CT images with an average size of 256 x 256 x 100 and more than 1100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 s to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency and ease of implementation.

Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.

PubMed

Almeida, Diogo F; Ruben, Rui B; Folgado, João; Fernandes, Paulo R; Audenaert, Emmanuel; Verhegghe, Benedict; De Beule, Matthieu

2016-12-01

Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans. Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach. With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1mm. For the low resolution image group the results are also accurate and the average error is less than 1.5mm. The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

PubMed

Asman, Andrew J; Huo, Yuankai; Plassard, Andrew J; Landman, Bennett A

2015-12-01

We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on fusing local learners. In the largest whole-brain multi-atlas study yet reported, multi-atlas segmentations are estimated for a training set of 3464 MR brain images. Using these multi-atlas estimates we (1) estimate a low-dimensional representation for selecting locally appropriate example images, and (2) build AdaBoost learners that map a weak initial segmentation to the multi-atlas segmentation result. Thus, to segment a new target image we project the image into the low-dimensional space, construct a weak initial segmentation, and fuse the trained, locally selected, learners. The MLF framework cuts the runtime on a modern computer from 36 h down to 3-8 min - a 270× speedup - by completely bypassing the need for deformable atlas-target registrations. Additionally, we (1) describe a technique for optimizing the weak initial segmentation and the AdaBoost learning parameters, (2) quantify the ability to replicate the multi-atlas result with mean accuracies approaching the multi-atlas intra-subject reproducibility on a testing set of 380 images, (3) demonstrate significant increases in the reproducibility of intra-subject segmentations when compared to a state-of-the-art multi-atlas framework on a separate reproducibility dataset, (4) show that under the MLF framework the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework, and (5) indicate that the MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information. Copyright © 2015 Elsevier B.V. All rights reserved.

Real-Time Ultrasound Segmentation, Analysis and Visualisation of Deep Cervical Muscle Structure.

PubMed

Cunningham, Ryan J; Harding, Peter J; Loram, Ian D

2017-02-01

Despite widespread availability of ultrasound and a need for personalised muscle diagnosis (neck/back pain-injury, work related disorder, myopathies, neuropathies), robust, online segmentation of muscles within complex groups remains unsolved by existing methods. For example, Cervical Dystonia (CD) is a prevalent neurological condition causing painful spasticity in one or multiple muscles in the cervical muscle system. Clinicians currently have no method for targeting/monitoring treatment of deep muscles. Automated methods of muscle segmentation would enable clinicians to study, target, and monitor the deep cervical muscles via ultrasound. We have developed a method for segmenting five bilateral cervical muscles and the spine via ultrasound alone, in real-time. Magnetic Resonance Imaging (MRI) and ultrasound data were collected from 22 participants (age: 29.0±6.6, male: 12). To acquire ultrasound muscle segment labels, a novel multimodal registration method was developed, involving MRI image annotation, and shape registration to MRI-matched ultrasound images, via approximation of the tissue deformation. We then applied polynomial regression to transform our annotations and textures into a mean space, before using shape statistics to generate a texture-to-shape dictionary. For segmentation, test images were compared to dictionary textures giving an initial segmentation, and then we used a customized Active Shape Model to refine the fit. Using ultrasound alone, on unseen participants, our technique currently segments a single image in [Formula: see text] to over 86% accuracy (Jaccard index). We propose this approach is applicable generally to segment, extrapolate and visualise deep muscle structure, and analyse statistical features online.

Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation

PubMed Central

Zhu, Y; Young, G; Xue, Z; Huang, R; You, H; Setayesh, K; Hatabu, H; Cao, F; Wong, S.T.

2012-01-01

Rationale and Objectives Quantitative measurement provides essential information about disease progression and treatment response in patients with Glioblastoma multiforme (GBM). The goal of this paper is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Materials and Methods Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted MR brain data, and the latter refines the segmentation results with minimal manual input. Results Twenty six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface (GUI). Conclusion Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MRI data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology. PMID:22591720

Tumor growth model for atlas based registration of pathological brain MR images

NASA Astrophysics Data System (ADS)

Moualhi, Wafa; Ezzeddine, Zagrouba

2015-02-01

The motivation of this work is to register a tumor brain magnetic resonance (MR) image with a normal brain atlas. A normal brain atlas is deformed in order to take account of the presence of a large space occupying tumor. The method use a priori model of tumor growth assuming that the tumor grows in a radial way from a starting point. First, an affine transformation is used in order to bring the patient image and the brain atlas in a global correspondence. Second, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. Finally, the seeded atlas is deformed combining a method derived from optical flow principles and a model for tumor growth (MTG). Results show that an automatic segmentation method of brain structures in the presence of large deformation can be provided.

Dynamical Segmentation of the Left Ventricle in Echocardiographic Image Sequences

DTIC Science & Technology

2001-10-25

LV in echocardiographic images using 3D deformable models and superquadrics” EMBS-2000. Vol. 3, 2000, pp. 1724 -1727 [7] Malladi R , Sethian J, Vemuri...very noisy images. An original external energy P is determined automatically and stabilizes the snake on the boundary: ( ) . , ,P r k x y z...1) where r is a constant fixed by the operator and ( ), ,k x y z depends on the image gradient : ( ) 1, , 1 _ k x y z Grad Mod

The polyphased tectonic evolution of the Anegada Passage in the northern Lesser Antilles subduction zone

NASA Astrophysics Data System (ADS)

Laurencin, M.; Marcaillou, B.; Graindorge, D.; Klingelhoefer, F.; Lallemand, S.; Laigle, M.; Lebrun, J.-F.

2017-05-01

The influence of the highly oblique plate convergence at the northern Lesser Antilles onto the margin strain partitioning and deformation pattern, although frequently invoked, has never been clearly imaged. The Anegada Passage is a set of basins and deep valleys, regularly related to the southern boundary of the Puerto Rico-Virgin Islands (PRVI) microplate. Despite the publications of various tectonic models mostly based on bathymetric data, the tectonic origin and deformation of this Passage remains unconstrained in the absence of deep structure imaging. During cruises Antithesis 1 and 3 (2013-2016), we recorded the first deep multichannel seismic images and new multibeam data in the northern Lesser Antilles margin segment in order to shed a new light on the structure and tectonic pattern of the Anegada Passage. We image the northeastern extent of the Anegada Passage, from the Sombrero Basin to the Lesser Antilles margin front. Our results reveal that this northeastern segment is an EW trending left-stepping en échelon strike-slip system that consists of the Sombrero and Malliwana pull-apart basins, the Malliwana and Anguilla left-lateral faults, and the NE-SW compressional restraining bend at the Malliwana Hill. Reviewing the structure of the Anegada Passage, from the south of Puerto Rico to the Lesser Antilles margin front, reveals a polyphased tectonic history. The Anegada Passage is formed by a NW-SE extension, possibly related to the rotation or escape of PRVI block due to collision of the Bahamas Bank. Currently, it is deformed by an active WNW-ESE strike-slip deformation associated to the shear component of the strain partitioning resulting from the subduction obliquity.

An automatic rat brain extraction method based on a deformable surface model.

PubMed

Li, Jiehua; Liu, Xiaofeng; Zhuo, Jiachen; Gullapalli, Rao P; Zara, Jason M

2013-08-15

The extraction of the brain from the skull in medical images is a necessary first step before image registration or segmentation. While pre-clinical MR imaging studies on small animals, such as rats, are increasing, fully automatic imaging processing techniques specific to small animal studies remain lacking. In this paper, we present an automatic rat brain extraction method, the Rat Brain Deformable model method (RBD), which adapts the popular human brain extraction tool (BET) through the incorporation of information on the brain geometry and MR image characteristics of the rat brain. The robustness of the method was demonstrated on T2-weighted MR images of 64 rats and compared with other brain extraction methods (BET, PCNN, PCNN-3D). The results demonstrate that RBD reliably extracts the rat brain with high accuracy (>92% volume overlap) and is robust against signal inhomogeneity in the images. Copyright © 2013 Elsevier B.V. All rights reserved.

SU-E-J-103: Propagation of Rectum and Bladder Contours for Tandem and Ring (T&R) HDR Treatment Using Deformable Image Registration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan, Y; Chao, M; Sheu, R

2015-06-15

Purpose: To investigate the feasibility of using DIR to propagate the manually contoured rectum and bladder from the 1st insertion to the new CT images on subsequent insertions and evaluate the segmentation performance. Methods: Ten cervical cancer patients, who were treated by T&R brachytherapy in 3–4 insertions, were retrospectively collected. In each insertion, rectum and bladder were manually delineated on the planning CT by a physicist and verified by a radiation oncologist. Using VelocityAI (Velocity Medical Solutions, Atlanta, GA), a rigid registration was firstly employed to match the bony structures between the first insertion and each of the following insertions,more » then a multi-pass B-spine DIR was carried out to further map the sub volume that encompasses rectum and bladder. The resultant deformation fields propagated contours, and dice similarity coefficient (DSC) was used to quantitatively evaluate the agreement between the propagated contours and the manually-delineated organs. For the 3rd insertion, we also evaluated if the segmentation performance could be improved by propagating the contours from the most recent insertion, i.e., the 2nd insertion. Results: On average, the contour propagation took about 1 minute. The average and standard deviation of DSC over all insertions and patients was 0.67±0.10 (range: 0.44–0.81) for rectum, and 0.78±0.07 (range: 0.63–0.87) for bladder. For the 3rd insertion, propagating contours from the 2nd insertion could improve the segmentation performance in terms of DSC from 0.63±0.10 to 0.72±0.08 for rectum, and from 0.77±0.07 to 0.79±0.06 for bladder. A Wilcoxon signed rank test indicated that the improvement was statistically significant for rectum (p = 0.004). Conclusion: The preliminary results demonstrate that deformable image registration could efficiently and accurately propagate rectum and bladder contours between CT images in different T&R brachytherapy fractions. We are incorporating the propagated contours into our learning-based method to further segment these organs.« less

Automatic aortic root segmentation in CTA whole-body dataset

NASA Astrophysics Data System (ADS)

Gao, Xinpei; Kitslaar, Pieter H.; Scholte, Arthur J. H. A.; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke; Reiber, Johan H. C.

2016-03-01

Trans-catheter aortic valve replacement (TAVR) is an evolving technique for patients with serious aortic stenosis disease. Typically, in this application a CTA data set is obtained of the patient's arterial system from the subclavian artery to the femoral arteries, to evaluate the quality of the vascular access route and analyze the aortic root to determine if and which prosthesis should be used. In this paper, we concentrate on the automated segmentation of the aortic root. The purpose of this study was to automatically segment the aortic root in computed tomography angiography (CTA) datasets to support TAVR procedures. The method in this study includes 4 major steps. First, the patient's cardiac CTA image was resampled to reduce the computation time. Next, the cardiac CTA image was segmented using an atlas-based approach. The most similar atlas was selected from a total of 8 atlases based on its image similarity to the input CTA image. Third, the aortic root segmentation from the previous step was transferred to the patient's whole-body CTA image by affine registration and refined in the fourth step using a deformable subdivision surface model fitting procedure based on image intensity. The pipeline was applied to 20 patients. The ground truth was created by an analyst who semi-automatically corrected the contours of the automatic method, where necessary. The average Dice similarity index between the segmentations of the automatic method and the ground truth was found to be 0.965±0.024. In conclusion, the current results are very promising.

Inter-speaker speech variability assessment using statistical deformable models from 3.0 tesla magnetic resonance images.

PubMed

Vasconcelos, Maria J M; Ventura, Sandra M R; Freitas, Diamantino R S; Tavares, João Manuel R S

2012-03-01

The morphological and dynamic characterisation of the vocal tract during speech production has been gaining greater attention due to the motivation of the latest improvements in magnetic resonance (MR) imaging; namely, with the use of higher magnetic fields, such as 3.0 Tesla. In this work, the automatic study of the vocal tract from 3.0 Tesla MR images was assessed through the application of statistical deformable models. Therefore, the primary goal focused on the analysis of the shape of the vocal tract during the articulation of European Portuguese sounds, followed by the evaluation of the results concerning the automatic segmentation, i.e. identification of the vocal tract in new MR images. In what concerns speech production, this is the first attempt to automatically characterise and reconstruct the vocal tract shape of 3.0 Tesla MR images by using deformable models; particularly, by using active and appearance shape models. The achieved results clearly evidence the adequacy and advantage of the automatic analysis of the 3.0 Tesla MR images of these deformable models in order to extract the vocal tract shape and assess the involved articulatory movements. These achievements are mostly required, for example, for a better knowledge of speech production, mainly of patients suffering from articulatory disorders, and to build enhanced speech synthesizer models.

Simulation of brain tumors in MR images for evaluation of segmentation efficacy.

PubMed

Prastawa, Marcel; Bullitt, Elizabeth; Gerig, Guido

2009-04-01

Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors).

Propagation of registration uncertainty during multi-fraction cervical cancer brachytherapy

NASA Astrophysics Data System (ADS)

Amir-Khalili, A.; Hamarneh, G.; Zakariaee, R.; Spadinger, I.; Abugharbieh, R.

2017-10-01

Multi-fraction cervical cancer brachytherapy is a form of image-guided radiotherapy that heavily relies on 3D imaging during treatment planning, delivery, and quality control. In this context, deformable image registration can increase the accuracy of dosimetric evaluations, provided that one can account for the uncertainties associated with the registration process. To enable such capability, we propose a mathematical framework that first estimates the registration uncertainty and subsequently propagates the effects of the computed uncertainties from the registration stage through to the visualizations, organ segmentations, and dosimetric evaluations. To ensure the practicality of our proposed framework in real world image-guided radiotherapy contexts, we implemented our technique via a computationally efficient and generalizable algorithm that is compatible with existing deformable image registration software. In our clinical context of fractionated cervical cancer brachytherapy, we perform a retrospective analysis on 37 patients and present evidence that our proposed methodology for computing and propagating registration uncertainties may be beneficial during therapy planning and quality control. Specifically, we quantify and visualize the influence of registration uncertainty on dosimetric analysis during the computation of the total accumulated radiation dose on the bladder wall. We further show how registration uncertainty may be leveraged into enhanced visualizations that depict the quality of the registration and highlight potential deviations from the treatment plan prior to the delivery of radiation treatment. Finally, we show that we can improve the transfer of delineated volumetric organ segmentation labels from one fraction to the next by encoding the computed registration uncertainties into the segmentation labels.

Sparse intervertebral fence composition for 3D cervical vertebra segmentation

NASA Astrophysics Data System (ADS)

Liu, Xinxin; Yang, Jian; Song, Shuang; Cong, Weijian; Jiao, Peifeng; Song, Hong; Ai, Danni; Jiang, Yurong; Wang, Yongtian

2018-06-01

Statistical shape models are capable of extracting shape prior information, and are usually utilized to assist the task of segmentation of medical images. However, such models require large training datasets in the case of multi-object structures, and it also is difficult to achieve satisfactory results for complex shapes. This study proposed a novel statistical model for cervical vertebra segmentation, called sparse intervertebral fence composition (SiFC), which can reconstruct the boundary between adjacent vertebrae by modeling intervertebral fences. The complex shape of the cervical spine is replaced by a simple intervertebral fence, which considerably reduces the difficulty of cervical segmentation. The final segmentation results are obtained by using a 3D active contour deformation model without shape constraint, which substantially enhances the recognition capability of the proposed method for objects with complex shapes. The proposed segmentation framework is tested on a dataset with CT images from 20 patients. A quantitative comparison against corresponding reference vertebral segmentation yields an overall mean absolute surface distance of 0.70 mm and a dice similarity index of 95.47% for cervical vertebral segmentation. The experimental results show that the SiFC method achieves competitive cervical vertebral segmentation performances, and completely eliminates inter-process overlap.

Simultaneous Assessment of Myocardial Perfusion, Wall Motion, and Deformation during Myocardial Contrast Echocardiography: A Feasibility Study.

PubMed

Zoppellaro, Giacomo; Venneri, Lucia; Khattar, Rajdeep S; Li, Wei; Senior, Roxy

2016-06-01

Ultrasound contrast agents may be used for the assessment of regional wall motion and myocardial perfusion, but are generally considered not suitable for deformation analysis. The aim of our study was to assess the feasibility of deformation imaging on contrast-enhanced images using a novel methodology. We prospectively enrolled 40 patients who underwent stress echocardiography with continuous intravenous infusion of SonoVue for the assessment of myocardial perfusion imaging with flash replenishment technique. We compared longitudinal strain (Lε) values, assessed with a vendor-independent software (2D CPA), on 68 resting contrast-enhanced and 68 resting noncontrast recordings. Strain analysis on contrast recordings was evaluated in the first cardiac cycles after the flash. Tracking of contrast images was deemed feasible in all subjects and in all views. Contrast administration improved image quality and increased the number of segments used for deformation analysis. Lε of noncontrast and contrast-enhanced images were statistically different (-18.8 ± 4.5% and -22.8 ± 5.4%, respectively; P < 0.001), but their correlation was good (ICC 0.65, 95%CI 0.42-0.78). Patients with resting wall-motion abnormalities showed lower Lε values on contrast recordings (-18.6 ± 6.0% vs. -24.2 ± 5.5%, respectively; P < 0.01). Intra-operator and inter-operator reproducibility was good for both noncontrast and contrast images with no statistical differences. Our study shows that deformation analysis on postflash contrast-enhanced images is feasible and reproducible. Therefore, it would be possible to perform a simultaneous evaluation of wall-motion abnormalities, volumes, ejection fraction, perfusion defects, and cardiac deformation on the same contrast recording. © 2016, Wiley Periodicals, Inc.

SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, W; Wang, J; Zhang, H

Purpose: To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy. Methods: We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation. Results: Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss ormore » gain, and motion. Many semi-automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi-automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi-modality segmentation methods have been shown to improve accuracy compared to single-modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response. Conclusion: This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

Validation of automatic landmark identification for atlas-based segmentation for radiation treatment planning of the head-and-neck region

NASA Astrophysics Data System (ADS)

Leavens, Claudia; Vik, Torbjørn; Schulz, Heinrich; Allaire, Stéphane; Kim, John; Dawson, Laura; O'Sullivan, Brian; Breen, Stephen; Jaffray, David; Pekar, Vladimir

2008-03-01

Manual contouring of target volumes and organs at risk in radiation therapy is extremely time-consuming, in particular for treating the head-and-neck area, where a single patient treatment plan can take several hours to contour. As radiation treatment delivery moves towards adaptive treatment, the need for more efficient segmentation techniques will increase. We are developing a method for automatic model-based segmentation of the head and neck. This process can be broken down into three main steps: i) automatic landmark identification in the image dataset of interest, ii) automatic landmark-based initialization of deformable surface models to the patient image dataset, and iii) adaptation of the deformable models to the patient-specific anatomical boundaries of interest. In this paper, we focus on the validation of the first step of this method, quantifying the results of our automatic landmark identification method. We use an image atlas formed by applying thin-plate spline (TPS) interpolation to ten atlas datasets, using 27 manually identified landmarks in each atlas/training dataset. The principal variation modes returned by principal component analysis (PCA) of the landmark positions were used by an automatic registration algorithm, which sought the corresponding landmarks in the clinical dataset of interest using a controlled random search algorithm. Applying a run time of 60 seconds to the random search, a root mean square (rms) distance to the ground-truth landmark position of 9.5 +/- 0.6 mm was calculated for the identified landmarks. Automatic segmentation of the brain, mandible and brain stem, using the detected landmarks, is demonstrated.

SU-E-J-131: Augmenting Atlas-Based Segmentation by Incorporating Image Features Proximal to the Atlas Contours

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Dengwang; Liu, Li; Kapp, Daniel S.

2015-06-15

Purpose: For facilitating the current automatic segmentation, in this work we propose a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. Methods: In setting up an atlas-based library, we include not only the coordinates of contour points, but also the image features adjacent to the contour. 139 planning CT scans with normal appearing livers obtained during their radiotherapy treatment planning were used to construct the library. The CT images within the library were registered each other using affine registration. A nonlinear narrow shell with the regionalmore » thickness determined by the distance between two vertices alongside the contour. The narrow shell was automatically constructed both inside and outside of the liver contours. The common image features within narrow shell between a new case and a library case were first selected by a Speed-up Robust Features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the images of the new patient by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy function within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by a physician. Results: Application of the technique to 30 liver cases suggested that the technique was capable of reliably segment organs such as the liver with little human intervention. Compared with the manual segmentation results by a physician, the average and discrepancies of the volumetric overlap percentage (VOP) was found to be 92.43%+2.14%. Conclusion: Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. This work is supported by NIH/NIBIB (1R01-EB016777), National Natural Science Foundation of China (No.61471226 and No.61201441), Research funding from Shandong Province (No.BS2012DX038 and No.J12LN23), and Research funding from Jinan City (No.201401221 and No.20120109)« less

Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation

PubMed Central

Wang, Hongzhi; Yushkevich, Paul A.

2013-01-01

Label fusion based multi-atlas segmentation has proven to be one of the most competitive techniques for medical image segmentation. This technique transfers segmentations from expert-labeled images, called atlases, to a novel image using deformable image registration. Errors produced by label transfer are further reduced by label fusion that combines the results produced by all atlases into a consensus solution. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity is a simple and highly effective label fusion technique. However, one limitation of most weighted voting methods is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this problem, we recently developed the joint label fusion technique and the corrective learning technique, which won the first place of the 2012 MICCAI Multi-Atlas Labeling Challenge and was one of the top performers in 2013 MICCAI Segmentation: Algorithms, Theory and Applications (SATA) challenge. To make our techniques more accessible to the scientific research community, we describe an Insight-Toolkit based open source implementation of our label fusion methods. Our implementation extends our methods to work with multi-modality imaging data and is more suitable for segmentation problems with multiple labels. We demonstrate the usage of our tools through applying them to the 2012 MICCAI Multi-Atlas Labeling Challenge brain image dataset and the 2013 SATA challenge canine leg image dataset. We report the best results on these two datasets so far. PMID:24319427

Segmentation and Tracking of Cytoskeletal Filaments Using Open Active Contours

PubMed Central

Smith, Matthew B.; Li, Hongsheng; Shen, Tian; Huang, Xiaolei; Yusuf, Eddy; Vavylonis, Dimitrios

2010-01-01

We use open active contours to quantify cytoskeletal structures imaged by fluorescence microscopy in two and three dimensions. We developed an interactive software tool for segmentation, tracking, and visualization of individual fibers. Open active contours are parametric curves that deform to minimize the sum of an external energy derived from the image and an internal bending and stretching energy. The external energy generates (i) forces that attract the contour toward the central bright line of a filament in the image, and (ii) forces that stretch the active contour toward the ends of bright ridges. Images of simulated semiflexible polymers with known bending and torsional rigidity are analyzed to validate the method. We apply our methods to quantify the conformations and dynamics of actin in two examples: actin filaments imaged by TIRF microscopy in vitro, and actin cables in fission yeast imaged by spinning disk confocal microscopy. PMID:20814909

Three-dimensional nonrigid landmark-based magnetic resonance to transrectal ultrasound registration for image-guided prostate biopsy.

PubMed

Sun, Yue; Qiu, Wu; Yuan, Jing; Romagnoli, Cesare; Fenster, Aaron

2015-04-01

Registration of three-dimensional (3-D) magnetic resonance (MR) to 3-D transrectal ultrasound (TRUS) prostate images is an important step in the planning and guidance of 3-D TRUS guided prostate biopsy. In order to accurately and efficiently perform the registration, a nonrigid landmark-based registration method is required to account for the different deformations of the prostate when using these two modalities. We describe a nonrigid landmark-based method for registration of 3-D TRUS to MR prostate images. The landmark-based registration method first makes use of an initial rigid registration of 3-D MR to 3-D TRUS images using six manually placed approximately corresponding landmarks in each image. Following manual initialization, the two prostate surfaces are segmented from 3-D MR and TRUS images and then nonrigidly registered using the following steps: (1) rotationally reslicing corresponding segmented prostate surfaces from both 3-D MR and TRUS images around a specified axis, (2) an approach to find point correspondences on the surfaces of the segmented surfaces, and (3) deformation of the surface of the prostate in the MR image to match the surface of the prostate in the 3-D TRUS image and the interior using a thin-plate spline algorithm. The registration accuracy was evaluated using 17 patient prostate MR and 3-D TRUS images by measuring the target registration error (TRE). Experimental results showed that the proposed method yielded an overall mean TRE of [Formula: see text] for the rigid registration and [Formula: see text] for the nonrigid registration, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm. A landmark-based nonrigid 3-D MR-TRUS registration approach is proposed, which takes into account the correspondences on the prostate surface, inside the prostate, as well as the centroid of the prostate. Experimental results indicate that the proposed method yields clinically sufficient accuracy.

3D knee segmentation based on three MRI sequences from different planes.

PubMed

Zhou, L; Chav, R; Cresson, T; Chartrand, G; de Guise, J

2016-08-01

In clinical practice, knee MRI sequences with 3.5~5 mm slice distance in sagittal, coronal, and axial planes are often requested for the knee examination since its acquisition is faster than high-resolution MRI sequence in a single plane, thereby reducing the probability of motion artifact. In order to take advantage of the three sequences from different planes, a 3D segmentation method based on the combination of three knee models obtained from the three sequences is proposed in this paper. In the method, the sub-segmentation is respectively performed with sagittal, coronal, and axial MRI sequence in the image coordinate system. With each sequence, an initial knee model is hierarchically deformed, and then the three deformed models are mapped to reference coordinate system defined by the DICOM standard and combined to obtain a patient-specific model. The experimental results verified that the three sub-segmentation results can complement each other, and their integration can compensate for the insufficiency of boundary information caused by 3.5~5 mm gap between consecutive slices. Therefore, the obtained patient-specific model is substantially more accurate than each sub-segmentation results.

A gaussian mixture + demons deformable registration method for cone-beam CT-guided robotic transoral base-of-tongue surgery

NASA Astrophysics Data System (ADS)

Reaungamornrat, S.; Liu, W. P.; Schafer, S.; Otake, Y.; Nithiananthan, S.; Uneri, A.; Richmon, J.; Sorger, J.; Siewerdsen, J. H.; Taylor, R. H.

2013-03-01

Purpose: An increasingly popular minimally invasive approach to resection of oropharyngeal / base-of-tongue cancer is made possible by a transoral technique conducted with the assistance of a surgical robot. However, the highly deformed surgical setup (neck flexed, mouth open, and tongue retracted) compared to the typical patient orientation in preoperative images poses a challenge to guidance and localization of the tumor target and adjacent critical anatomy. Intraoperative cone-beam CT (CBCT) can account for such deformation, but due to the low contrast of soft-tissue in CBCT images, direct localization of the target and critical tissues in CBCT images can be difficult. Such structures may be more readily delineated in preoperative CT or MR images, so a method to deformably register such information to intraoperative CBCT could offer significant value. This paper details the initial implementation of a deformable registration framework to align preoperative images with the deformed intraoperative scene and gives preliminary evaluation of the geometric accuracy of registration in CBCT-guided TORS. Method: The deformable registration aligns preoperative CT or MR to intraoperative CBCT by integrating two established approaches. The volume of interest is first segmented (specifically, the region of the tongue from the tip to the hyoid), and a Gaussian mixture (GM) mode1 of surface point clouds is used for rigid initialization (GMRigid) as well as an initial deformation (GMNonRigid). Next, refinement of the registration is performed using the Demons algorithm applied to distance transformations of the GM-registered and CBCT volumes. The registration accuracy of the framework was quantified in preliminary studies using a cadaver emulating preoperative and intraoperative setups. Geometric accuracy of registration was quantified in terms of target registration error (TRE) and surface distance error. Result: With each step of the registration process, the framework demonstrated improved registration, achieving mean TRE of 3.0 mm following the GM rigid, 1.9 mm following GM nonrigid, and 1.5 mm at the output of the registration process. Analysis of surface distance demonstrated a corresponding improvement of 2.2, 0.4, and 0.3 mm, respectively. The evaluation of registration error revealed the accurate alignment in the region of interest for base-of-tongue robotic surgery owing to point-set selection in the GM steps and refinement in the deep aspect of the tongue in the Demons step. Conclusions: A promising framework has been developed for CBCT-guided TORS in which intraoperative CBCT provides a basis for registration of preoperative images to the highly deformed intraoperative setup. The registration framework is invariant to imaging modality (accommodating preoperative CT or MR) and is robust against CBCT intensity variations and artifact, provided corresponding segmentation of the volume of interest. The approach could facilitate overlay of preoperative planning data directly in stereo-endoscopic video in support of CBCT-guided TORS.

Pre-slip and Localized Strain Band - A Study Based on Large Sample Experiment and DIC

NASA Astrophysics Data System (ADS)

Ji, Y.; Zhuo, Y. Q.; Liu, L.; Ma, J.

2017-12-01

Meta-instability stage (MIS) is the stage occurs between a fault reaching the peak differential stress and the onset of the final stress drop. It is the crucial stage during which a fault transits from "stick" to "slip". Therefore, if one can quantitatively analyze the spatial and temporal characteristics of the deformation field of a fault at MIS, it will be of great significance both to fault mechanics and earthquake prediction study. In order to do so, a series of stick-slip experiments were conducted using a biaxial servo-controlled pressure machine. Digital images of the sample surfaces were captured by a high speed camera and processed using a digital image correlation method (DIC). If images of a rock sample are acquired before and after deformation, then DIC can be used to infer the displacement and strain fields. In our study, sample images were captured at the rate of 1000 frame per second and the resolution is 2048 by 2048 in pixel. The displacement filed, strain filed and fault displacement were calculated from the captured images. Our data shows that (1) pre-sliding can be a three-stage process, including a relative long and slow first stage at slipping rate of 7.9nm/s, a relatively short and fast second one at rate of 3µm/s and the last stage only last for 0.2s but the slipping rate reached as high as 220µm/s. (2) Localized strain bands were observed nearly perpendicular to the fault. A possible mechanism is that the pre-sliding is distributed heterogeneously along the fault, which means there are relatively adequately sliding segments and the less sliding ones, they become the constrain condition of deformation of the adjacent subregion. The localized deformation band tends to radiate from the discontinuity point of sliding. While the adequately sliding segments are competing with the less sliding ones, the strain bands are evolving accordingly.

Automatic liver segmentation in computed tomography using general-purpose shape modeling methods.

PubMed

Spinczyk, Dominik; Krasoń, Agata

2018-05-29

Liver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods. As part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field. Mutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55[Formula: see text], of which for three of them the coefficient was over 70[Formula: see text], which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5[Formula: see text] CONCLUSIONS: This value of 88.5 [Formula: see text] Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object-the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

SU-E-J-97: Quality Assurance of Deformable Image Registration Algorithms: How Realistic Should Phantoms Be?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saenz, D; Stathakis, S; Kirby, N

Purpose: Deformable image registration (DIR) has widespread uses in radiotherapy for applications such as dose accumulation studies, multi-modality image fusion, and organ segmentation. The quality assurance (QA) of such algorithms, however, remains largely unimplemented. This work aims to determine how detailed a physical phantom needs to be to accurately perform QA of a DIR algorithm. Methods: Virtual prostate and head-and-neck phantoms, made from patient images, were used for this study. Both sets consist of an undeformed and deformed image pair. The images were processed to create additional image pairs with one through five homogeneous tissue levels using Otsu’s method. Realisticmore » noise was then added to each image. The DIR algorithms from MIM and Velocity (Deformable Multipass) were applied to the original phantom images and the processed ones. The resulting deformations were then compared to the known warping. A higher number of tissue levels creates more contrast in an image and enables DIR algorithms to produce more accurate results. For this reason, error (distance between predicted and known deformation) is utilized as a metric to evaluate how many levels are required for a phantom to be a realistic patient proxy. Results: For the prostate image pairs, the mean error decreased from 1–2 tissue levels and remained constant for 3+ levels. The mean error reduction was 39% and 26% for Velocity and MIM respectively. For head and neck, mean error fell similarly through 2 levels and flattened with total reduction of 16% and 49% for Velocity and MIM. For Velocity, 3+ levels produced comparable accuracy as the actual patient images, whereas MIM showed further accuracy improvement. Conclusion: The number of tissue levels needed to produce an accurate patient proxy depends on the algorithm. For Velocity, three levels were enough, whereas five was still insufficient for MIM.« less

Dynamic programming in parallel boundary detection with application to ultrasound intima-media segmentation.

PubMed

Zhou, Yuan; Cheng, Xinyao; Xu, Xiangyang; Song, Enmin

2013-12-01

Segmentation of carotid artery intima-media in longitudinal ultrasound images for measuring its thickness to predict cardiovascular diseases can be simplified as detecting two nearly parallel boundaries within a certain distance range, when plaque with irregular shapes is not considered. In this paper, we improve the implementation of two dynamic programming (DP) based approaches to parallel boundary detection, dual dynamic programming (DDP) and piecewise linear dual dynamic programming (PL-DDP). Then, a novel DP based approach, dual line detection (DLD), which translates the original 2-D curve position to a 4-D parameter space representing two line segments in a local image segment, is proposed to solve the problem while maintaining efficiency and rotation invariance. To apply the DLD to ultrasound intima-media segmentation, it is imbedded in a framework that employs an edge map obtained from multiplication of the responses of two edge detectors with different scales and a coupled snake model that simultaneously deforms the two contours for maintaining parallelism. The experimental results on synthetic images and carotid arteries of clinical ultrasound images indicate improved performance of the proposed DLD compared to DDP and PL-DDP, with respect to accuracy and efficiency. Copyright © 2013 Elsevier B.V. All rights reserved.

A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology

PubMed Central

Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

2013-01-01

Summary Background Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms. Objectives In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging. Methods It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm. Results The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation. Conclusion The proposed overlapping cell segmentation algorithm can accurately detect the center of each overlapping cell and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation. PMID:22526139

Poster — Thur Eve — 59: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mallawi, A; Farrell, T; Diamond, K

2014-08-15

Automated atlas-based segmentation has recently been evaluated for use in planning prostate cancer radiotherapy. In the typical approach, the essential step is the selection of an atlas from a database that best matches the target image. This work proposes an atlas selection strategy and evaluates its impact on the final segmentation accuracy. Prostate length (PL), right femoral head diameter (RFHD), and left femoral head diameter (LFHD) were measured in CT images of 20 patients. Each subject was then taken as the target image to which all remaining 19 images were affinely registered. For each pair of registered images, the overlapmore » between prostate and femoral head contours was quantified using the Dice Similarity Coefficient (DSC). Finally, we designed an atlas selection strategy that computed the ratio of PL (prostate segmentation), RFHD (right femur segmentation), and LFHD (left femur segmentation) between the target subject and each subject in the atlas database. Five atlas subjects yielding ratios nearest to one were then selected for further analysis. RFHD and LFHD were excellent parameters for atlas selection, achieving a mean femoral head DSC of 0.82 ± 0.06. PL had a moderate ability to select the most similar prostate, with a mean DSC of 0.63 ± 0.18. The DSC obtained with the proposed selection method were slightly lower than the maximums established using brute force, but this does not include potential improvements expected with deformable registration. Atlas selection based on PL for prostate and femoral diameter for femoral heads provides reasonable segmentation accuracy.« less

Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

PubMed

Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

2015-09-01

Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics.

PubMed

Moccia, Sara; De Momi, Elena; El Hadji, Sara; Mattos, Leonardo S

2018-05-01

Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task. Copyright © 2018 Elsevier B.V. All rights reserved.

Image analysis of ocular fundus for retinopathy characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ushizima, Daniela; Cuadros, Jorge

2010-02-05

Automated analysis of ocular fundus images is a common procedure in countries as England, including both nonemergency examination and retinal screening of patients with diabetes mellitus. This involves digital image capture and transmission of the images to a digital reading center for evaluation and treatment referral. In collaboration with the Optometry Department, University of California, Berkeley, we have tested computer vision algorithms to segment vessels and lesions in ground-truth data (DRIVE database) and hundreds of images of non-macular centric and nonuniform illumination views of the eye fundus from EyePACS program. Methods under investigation involve mathematical morphology (Figure 1) for imagemore » enhancement and pattern matching. Recently, we have focused in more efficient techniques to model the ocular fundus vasculature (Figure 2), using deformable contours. Preliminary results show accurate segmentation of vessels and high level of true-positive microaneurysms.« less

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM).

PubMed

Wang, Xuehu; Zheng, Yongchang; Gan, Lan; Wang, Xuan; Sang, Xinting; Kong, Xiangfeng; Zhao, Jie

2017-01-01

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.

Neural network fusion: a novel CT-MR aortic aneurysm image segmentation method

NASA Astrophysics Data System (ADS)

Wang, Duo; Zhang, Rui; Zhu, Jin; Teng, Zhongzhao; Huang, Yuan; Spiga, Filippo; Du, Michael Hong-Fei; Gillard, Jonathan H.; Lu, Qingsheng; Liò, Pietro

2018-03-01

Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease. In this work, we present methods on using deep neural network models to perform such multi-modal medical image segmentation. As CT image and MR image of the abdominal area cannot be well registered due to non-affine deformations, a naive approach is to train CT and MR segmentation network separately. However, such approach is time-consuming and resource-inefficient. We propose a new approach to fuse the high-level part of the CT and MR network together, hypothesizing that neurons recognizing the high level concepts of Aortic Aneurysm can be shared across multiple modalities. Such network is able to be trained end-to-end with non-registered CT and MR image using shorter training time. Moreover network fusion allows a shared representation of Aorta in both CT and MR images to be learnt. Through experiments we discovered that for parts of Aorta showing similar aneurysm conditions, their neural presentations in neural network has shorter distances. Such distances on the feature level is helpful for registering CT and MR image.

Gradient-based reliability maps for ACM-based segmentation of hippocampus.

PubMed

Zarpalas, Dimitrios; Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

2014-04-01

Automatic segmentation of deep brain structures, such as the hippocampus (HC), in MR images has attracted considerable scientific attention due to the widespread use of MRI and to the principal role of some structures in various mental disorders. In this literature, there exists a substantial amount of work relying on deformable models incorporating prior knowledge about structures' anatomy and shape information. However, shape priors capture global shape characteristics and thus fail to model boundaries of varying properties; HC boundaries present rich, poor, and missing gradient regions. On top of that, shape prior knowledge is blended with image information in the evolution process, through global weighting of the two terms, again neglecting the spatially varying boundary properties, causing segmentation faults. An innovative method is hereby presented that aims to achieve highly accurate HC segmentation in MR images, based on the modeling of boundary properties at each anatomical location and the inclusion of appropriate image information for each of those, within an active contour model framework. Hence, blending of image information and prior knowledge is based on a local weighting map, which mixes gradient information, regional and whole brain statistical information with a multi-atlas-based spatial distribution map of the structure's labels. Experimental results on three different datasets demonstrate the efficacy and accuracy of the proposed method.

MRI and Additive Manufacturing of Nasal Alar Constructs for Patient-specific Reconstruction.

PubMed

Visscher, Dafydd O; van Eijnatten, Maureen; Liberton, Niels P T J; Wolff, Jan; Hofman, Mark B M; Helder, Marco N; Don Griot, J Peter W; Zuijlen, Paul P M van

2017-08-30

Surgical reconstruction of cartilaginous defects remains a major challenge. In the current study, we aimed to identify an imaging strategy for the development of patient-specific constructs that aid in the reconstruction of nasal deformities. Magnetic Resonance Imaging (MRI) was performed on a human cadaver head to find the optimal MRI sequence for nasal cartilage. This sequence was subsequently used on a volunteer. Images of both were assessed by three independent researchers to determine measurement error and total segmentation time. Three dimensionally (3D) reconstructed alar cartilage was then additively manufactured. Validity was assessed by comparing manually segmented MR images to the gold standard (micro-CT). Manual segmentation allowed delineation of the nasal cartilages. Inter- and intra-observer agreement was acceptable in the cadaver (coefficient of variation 4.6-12.5%), but less in the volunteer (coefficient of variation 0.6-21.9%). Segmentation times did not differ between observers (cadaver P = 0.36; volunteer P = 0.6). The lateral crus of the alar cartilage was consistently identified by all observers, whereas part of the medial crus was consistently missed. This study suggests that MRI is a feasible imaging modality for the development of 3D alar constructs for patient-specific reconstruction.

Segmentation-free empirical beam hardening correction for CT.

PubMed

Schüller, Sören; Sawall, Stefan; Stannigel, Kai; Hülsbusch, Markus; Ulrici, Johannes; Hell, Erich; Kachelrieß, Marc

2015-02-01

The polychromatic nature of the x-ray beams and their effects on the reconstructed image are often disregarded during standard image reconstruction. This leads to cupping and beam hardening artifacts inside the reconstructed volume. To correct for a general cupping, methods like water precorrection exist. They correct the hardening of the spectrum during the penetration of the measured object only for the major tissue class. In contrast, more complex artifacts like streaks between dense objects need other techniques of correction. If using only the information of one single energy scan, there are two types of corrections. The first one is a physical approach. Thereby, artifacts can be reproduced and corrected within the original reconstruction by using assumptions in a polychromatic forward projector. These assumptions could be the used spectrum, the detector response, the physical attenuation and scatter properties of the intersected materials. A second method is an empirical approach, which does not rely on much prior knowledge. This so-called empirical beam hardening correction (EBHC) and the previously mentioned physical-based technique are both relying on a segmentation of the present tissues inside the patient. The difficulty thereby is that beam hardening by itself, scatter, and other effects, which diminish the image quality also disturb the correct tissue classification and thereby reduce the accuracy of the two known classes of correction techniques. The herein proposed method works similar to the empirical beam hardening correction but does not require a tissue segmentation and therefore shows improvements on image data, which are highly degraded by noise and artifacts. Furthermore, the new algorithm is designed in a way that no additional calibration or parameter fitting is needed. To overcome the segmentation of tissues, the authors propose a histogram deformation of their primary reconstructed CT image. This step is essential for the proposed algorithm to be segmentation-free (sf). This deformation leads to a nonlinear accentuation of higher CT-values. The original volume and the gray value deformed volume are monochromatically forward projected. The two projection sets are then monomially combined and reconstructed to generate sets of basis volumes which are used for correction. This is done by maximization of the image flatness due to adding additionally a weighted sum of these basis images. sfEBHC is evaluated on polychromatic simulations, phantom measurements, and patient data. The raw data sets were acquired by a dual source spiral CT scanner, a digital volume tomograph, and a dual source micro CT. Different phantom and patient data were used to illustrate the performance and wide range of usability of sfEBHC across different scanning scenarios. The artifact correction capabilities are compared to EBHC. All investigated cases show equal or improved image quality compared to the standard EBHC approach. The artifact correction is capable of correcting beam hardening artifacts for different scan parameters and scan scenarios. sfEBHC generates beam hardening-reduced images and is furthermore capable of dealing with images which are affected by high noise and strong artifacts. The algorithm can be used to recover structures which are hardly visible inside the beam hardening-affected regions.

Segmentation-free empirical beam hardening correction for CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schüller, Sören; Sawall, Stefan; Stannigel, Kai

2015-02-15

Purpose: The polychromatic nature of the x-ray beams and their effects on the reconstructed image are often disregarded during standard image reconstruction. This leads to cupping and beam hardening artifacts inside the reconstructed volume. To correct for a general cupping, methods like water precorrection exist. They correct the hardening of the spectrum during the penetration of the measured object only for the major tissue class. In contrast, more complex artifacts like streaks between dense objects need other techniques of correction. If using only the information of one single energy scan, there are two types of corrections. The first one ismore » a physical approach. Thereby, artifacts can be reproduced and corrected within the original reconstruction by using assumptions in a polychromatic forward projector. These assumptions could be the used spectrum, the detector response, the physical attenuation and scatter properties of the intersected materials. A second method is an empirical approach, which does not rely on much prior knowledge. This so-called empirical beam hardening correction (EBHC) and the previously mentioned physical-based technique are both relying on a segmentation of the present tissues inside the patient. The difficulty thereby is that beam hardening by itself, scatter, and other effects, which diminish the image quality also disturb the correct tissue classification and thereby reduce the accuracy of the two known classes of correction techniques. The herein proposed method works similar to the empirical beam hardening correction but does not require a tissue segmentation and therefore shows improvements on image data, which are highly degraded by noise and artifacts. Furthermore, the new algorithm is designed in a way that no additional calibration or parameter fitting is needed. Methods: To overcome the segmentation of tissues, the authors propose a histogram deformation of their primary reconstructed CT image. This step is essential for the proposed algorithm to be segmentation-free (sf). This deformation leads to a nonlinear accentuation of higher CT-values. The original volume and the gray value deformed volume are monochromatically forward projected. The two projection sets are then monomially combined and reconstructed to generate sets of basis volumes which are used for correction. This is done by maximization of the image flatness due to adding additionally a weighted sum of these basis images. sfEBHC is evaluated on polychromatic simulations, phantom measurements, and patient data. The raw data sets were acquired by a dual source spiral CT scanner, a digital volume tomograph, and a dual source micro CT. Different phantom and patient data were used to illustrate the performance and wide range of usability of sfEBHC across different scanning scenarios. The artifact correction capabilities are compared to EBHC. Results: All investigated cases show equal or improved image quality compared to the standard EBHC approach. The artifact correction is capable of correcting beam hardening artifacts for different scan parameters and scan scenarios. Conclusions: sfEBHC generates beam hardening-reduced images and is furthermore capable of dealing with images which are affected by high noise and strong artifacts. The algorithm can be used to recover structures which are hardly visible inside the beam hardening-affected regions.« less

A Novel Fast Helical 4D-CT Acquisition Technique to Generate Low-Noise Sorting Artifact–Free Images at User-Selected Breathing Phases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomas, David, E-mail: dhthomas@mednet.ucla.edu; Lamb, James; White, Benjamin

2014-05-01

Purpose: To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts. Methods and Materials: Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. Themore » tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Results: Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. Conclusions: The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact–free images at a patient dose similar to or less than current 4D-CT techniques.« less

A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases.

PubMed

Thomas, David; Lamb, James; White, Benjamin; Jani, Shyam; Gaudio, Sergio; Lee, Percy; Ruan, Dan; McNitt-Gray, Michael; Low, Daniel

2014-05-01

To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts. Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques. Copyright © 2014 Elsevier Inc. All rights reserved.

Model-based segmentation of abdominal aortic aneurysms in CTA images

NASA Astrophysics Data System (ADS)

de Bruijne, Marleen; van Ginneken, Bram; Niessen, Wiro J.; Loog, Marco; Viergever, Max A.

2003-05-01

Segmentation of thrombus in abdominal aortic aneurysms is complicated by regions of low boundary contrast and by the presence of many neighboring structures in close proximity to the aneurysm wall. We present an automated method that is similar to the well known Active Shape Models (ASM), combining a three-dimensional shape model with a one-dimensional boundary appearance model. Our contribution is twofold: we developed a non-parametric appearance modeling scheme that effectively deals with a highly varying background, and we propose a way of generalizing models of curvilinear structures from small training sets. In contrast with the conventional ASM approach, the new appearance model trains on both true and false examples of boundary profiles. The probability that a given image profile belongs to the boundary is obtained using k nearest neighbor (kNN) probability density estimation. The performance of this scheme is compared to that of original ASMs, which minimize the Mahalanobis distance to the average true profile in the training set. The generalizability of the shape model is improved by modeling the objects axis deformation independent of its cross-sectional deformation. A leave-one-out experiment was performed on 23 datasets. Segmentation using the kNN appearance model significantly outperformed the original ASM scheme; average volume errors were 5.9% and 46% respectively.

Deformable image registration with content mismatch: a demons variant to account for added material and surgical devices in the target image

NASA Astrophysics Data System (ADS)

Nithiananthan, S.; Uneri, A.; Schafer, S.; Mirota, D.; Otake, Y.; Stayman, J. W.; Zbijewski, W.; Khanna, A. J.; Reh, D. D.; Gallia, G. L.; Siewerdsen, J. H.

2013-03-01

Fast, accurate, deformable image registration is an important aspect of image-guided interventions. Among the factors that can confound registration is the presence of additional material in the intraoperative image - e.g., contrast bolus or a surgical implant - that was not present in the prior image. Existing deformable registration methods generally fail to account for tissue excised between image acquisitions and typically simply "move" voxels within the images with no ability to account for tissue that is removed or introduced between scans. We present a variant of the Demons algorithm to accommodate such content mismatch. The approach combines segmentation of mismatched content with deformable registration featuring an extra pseudo-spatial dimension representing a reservoir from which material can be drawn into the registered image. Previous work tested the registration method in the presence of tissue excision ("missing tissue"). The current paper tests the method in the presence of additional material in the target image and presents a general method by which either missing or additional material can be accommodated. The method was tested in phantom studies, simulations, and cadaver models in the context of intraoperative cone-beam CT with three examples of content mismatch: a variable-diameter bolus (contrast injection); surgical device (rod), and additional material (bone cement). Registration accuracy was assessed in terms of difference images and normalized cross correlation (NCC). We identify the difficulties that traditional registration algorithms encounter when faced with content mismatch and evaluate the ability of the proposed method to overcome these challenges.

40Ar* loss in experimentally deformed muscovite and biotite with implications for 40Ar/39Ar geochronology of naturally deformed rocks

USGS Publications Warehouse

Cosca, Michael; Stunitz, Holger; Bourgiex, Anne-Lise; Lee, John P.

2011-01-01

The effects of deformation on radiogenic argon (40Ar*) retentivity in mica are described from high pressure experiments performed on rock samples of peraluminous granite containing euhedral muscovite and biotite. Cylindrical cores, ~15 mm in length and 6.25 mm in diameter, were drilled from granite collected from the South Armorican Massif in northwestern France, loaded into gold capsules, and weld-sealed in the presence of excess water. The samples were deformed at a pressure of 10 kb and a temperature of 600 degrees C over a period 29 of hours within a solid medium assembly in a Griggs-type triaxial hydraulic deformation apparatus. Overall shortening in the experiments was approximately 10%. Transmitted light and secondary and backscattered electron imaging of the deformed granite samples reveals evidence of induced defects and for significant physical grain size reduction by kinking, cracking, and grain segmentation of the micas.

In Situ Casting and Imaging of the Rat Airway Tree for Accurate 3D Reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacob, Rick E.; Colby, Sean M.; Kabilan, Senthil

The use of anatomically accurate, animal-specific airway geometries is important for understanding and modeling the physiology of the respiratory system. One approach for acquiring detailed airway architecture is to create a bronchial cast of the conducting airways. However, typical casting procedures either do not faithfully preserve the in vivo branching angles, or produce rigid casts that when removed for imaging are fragile and thus easily damaged. We address these problems by creating an in situ bronchial cast of the conducting airways in rats that can be subsequently imaged in situ using 3D micro-CT imaging. We also demonstrate that deformations inmore » airway branch angles resulting from the casting procedure are small, and that these angle deformations can be reversed through an interactive adjustment of the segmented cast geometry. Animal work was approved by the Institutional Animal Care and Use Committee of Pacific Northwest National Laboratory.« less

Influence of increasing convergence obliquity and shallow slab geometry onto tectonic deformation and seismogenic behavior along the Northern Lesser Antilles zone

NASA Astrophysics Data System (ADS)

Laurencin, M.; Graindorge, D.; Klingelhoefer, F.; Marcaillou, B.; Evain, M.

2018-06-01

In subduction zones, the 3D geometry of the plate interface is one of the key parameters that controls margin tectonic deformation, interplate coupling and seismogenic behavior. The North American plate subducts beneath the convex Northern Lesser Antilles margin. This convergent plate boundary, with a northward increasing convergence obliquity, turns into a sinistral strike-slip limit at the northwestern end of the system. This geodynamic context suggests a complex slab geometry, which has never been imaged before. Moreover, the seismic activity and particularly the number of events with thrust focal mechanism compatible with subduction earthquakes, increases northward from the Barbuda-Anguilla segment to the Anguilla-Virgin Islands segment. One of the major questions in this area is thus to analyze the influence of the increasing convergence obliquity and the slab geometry onto tectonic deformation and seismogenic behavior of the subduction zone. Based on wide-angle and multichannel reflection seismic data acquired during the Antithesis cruises (2013-2016), we decipher the deep structure of this subduction zone. Velocity models derived from wide-angle data acquired across the Anegada Passage are consistent with the presence of a crust of oceanic affinity thickened by hotspot magmatism and probably affected by the Upper Cretaceous-Eocene arc magmatism forming the 'Great Arc of the Caribbean'. The slab is shallower beneath the Anguilla-Virgin Islands margin segment than beneath the Anguilla-Barbuda segment which is likely to be directly related to the convex geometry of the upper plate. This shallower slab is located under the forearc where earthquakes and partitioning deformations increase locally. Thus, the shallowing slab might result in local greater interplate coupling and basal friction favoring seismic activity and tectonic partitioning beneath the Virgin Islands platform.

SU-E-J-133: Autosegmentation of Linac CBCT: Improved Accuracy Via Penalized Likelihood Reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Y

2015-06-15

Purpose: To improve the quality of kV X-ray cone beam CT (CBCT) for use in radiotherapy delivery assessment and re-planning by using penalized likelihood (PL) iterative reconstruction and auto-segmentation accuracy of the resulting CBCTs as an image quality metric. Methods: Present filtered backprojection (FBP) CBCT reconstructions can be improved upon by PL reconstruction with image formation models and appropriate regularization constraints. We use two constraints: 1) image smoothing via an edge preserving filter, and 2) a constraint minimizing the differences between the reconstruction and a registered prior image. Reconstructions of prostate therapy CBCTs were computed with constraint 1 alone andmore » with both constraints. The prior images were planning CTs(pCT) deformable-registered to the FBP reconstructions. Anatomy segmentations were done using atlas-based auto-segmentation (Elekta ADMIRE). Results: We observed small but consistent improvements in the Dice similarity coefficients of PL reconstructions over the FBP results, and additional small improvements with the added prior image constraint. For a CBCT with anatomy very similar in appearance to the pCT, we observed these changes in the Dice metric: +2.9% (prostate), +8.6% (rectum), −1.9% (bladder). For a second CBCT with a very different rectum configuration, we observed +0.8% (prostate), +8.9% (rectum), −1.2% (bladder). For a third case with significant lateral truncation of the field of view, we observed: +0.8% (prostate), +8.9% (rectum), −1.2% (bladder). Adding the prior image constraint raised Dice measures by about 1%. Conclusion: Efficient and practical adaptive radiotherapy requires accurate deformable registration and accurate anatomy delineation. We show here small and consistent patterns of improved contour accuracy using PL iterative reconstruction compared with FBP reconstruction. However, the modest extent of these results and the pattern of differences across CBCT cases suggest that significant further development will be required to make CBCT useful to adaptive radiotherapy.« less

Analysis of 3-D Tongue Motion From Tagged and Cine Magnetic Resonance Images

PubMed Central

Woo, Jonghye; Lee, Junghoon; Murano, Emi Z.; Stone, Maureen; Prince, Jerry L.

2016-01-01

Purpose Measuring tongue deformation and internal muscle motion during speech has been a challenging task because the tongue deforms in 3 dimensions, contains interdigitated muscles, and is largely hidden within the vocal tract. In this article, a new method is proposed to analyze tagged and cine magnetic resonance images of the tongue during speech in order to estimate 3-dimensional tissue displacement and deformation over time. Method The method involves computing 2-dimensional motion components using a standard tag-processing method called harmonic phase, constructing superresolution tongue volumes using cine magnetic resonance images, segmenting the tongue region using a random-walker algorithm, and estimating 3-dimensional tongue motion using an incompressible deformation estimation algorithm. Results Evaluation of the method is presented with a control group and a group of people who had received a glossectomy carrying out a speech task. A 2-step principal-components analysis is then used to reveal the unique motion patterns of the subjects. Azimuth motion angles and motion on the mirrored hemi-tongues are analyzed. Conclusion Tests of the method with a various collection of subjects show its capability of capturing patient motion patterns and indicate its potential value in future speech studies. PMID:27295428

3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images

NASA Astrophysics Data System (ADS)

Castro-Mateos, Isaac; Pozo, Jose M.; Eltes, Peter E.; Del Rio, Luis; Lazary, Aron; Frangi, Alejandro F.

2014-12-01

Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy. The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

Deep convolutional networks for pancreas segmentation in CT imaging

NASA Astrophysics Data System (ADS)

Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

2015-03-01

Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

The Correlation between Insertion Depth of Prodisc-C Artificial Disc and Postoperative Kyphotic Deformity: Clinical Importance of Insertion Depth of Artificial Disc.

PubMed

Lee, Do-Youl; Kim, Se-Hoon; Suh, Jung-Keun; Cho, Tai-Hyoung; Chung, Yong-Gu

2012-09-01

This study was designed to investigate the correlation between insertion depth of artificial disc and postoperative kyphotic deformity after Prodisc-C total disc replacement surgery, and the range of artificial disc insertion depth which is effective in preventing postoperative whole cervical or segmental kyphotic deformity. A retrospective radiological analysis was performed in 50 patients who had undergone single level total disc replacement surgery. Records were reviewed to obtain demographic data. Preoperative and postoperative radiographs were assessed to determine C2-7 Cobb's angle and segmental angle and to investigate postoperative kyphotic deformity. A formula was introduced to calculate insertion depth of Prodisc-C artificial disc. Statistical analysis was performed to search the correlation between insertion depth of Prodisc-C artificial disc and postoperative kyphotic deformity, and to estimate insertion depth of Prodisc-C artificial disc to prevent postoperative kyphotic deformity. In this study no significant statistical correlation was observed between insertion depth of Prodisc-C artificial disc and postoperative kyphotic deformity regarding C2-7 Cobb's angle. Statistical correlation between insertion depth of Prodisc-C artificial disc and postoperative kyphotic deformity was observed regarding segmental angle (p<0.05). It failed to estimate proper insertion depth of Prodisc-C artificial disc effective in preventing postoperative kyphotic deformity. Postoperative segmental kyphotic deformity is associated with insertion depth of Prodisc-C artificial disc. Anterior located artificial disc leads to lordotic segmental angle and posterior located artificial disc leads to kyphotic segmental angle postoperatively. But C2-7 Cobb's angle is not affected by artificial disc location after the surgery.

Seismotectonics of Bhutan: Evidence for segmentation of the Eastern Himalayas and link to foreland deformation

NASA Astrophysics Data System (ADS)

Diehl, Tobias; Singer, Julia; Hetényi, György; Grujic, Djordje; Clinton, John; Giardini, Domenico; Kissling, Edi

2017-04-01

The instrumental seismicity of Bhutan is characterized by a lower activity compared to most other parts of the Himalayan arc. To understand this low activity and its impact on the seismic hazard, a seismic network was installed in Bhutan for 22 months between 2013 and 2014. From the recorded seismicity, earthquake moment tensors, and local earthquake tomography, we reveal along-strike variations in structure and crustal deformation regime. Imaged structural variations, primarily a thickened crust in western Bhutan, suggest lateral differences in stresses on the Main Himalayan Thrust (MHT), potentially affecting interseismic coupling and style of deformation. Sikkim, western Bhutan, and its foreland are characterized by strike-slip faulting in the Indian basement. Strain is particularly localized along a NW-SE striking dextral fault zone reaching from Chungthang in northeast Sikkim to Dhubri at the northwestern edge of the Shillong Plateau in the foreland. The dextral Dhubri-Chungthang fault zone (DCF) might segment the MHT between eastern Nepal and western Bhutan and connect the deformation front of the Himalaya with the Shillong Plateau in the foreland by forming the western boundary of a West-Assam block. In contrast, the eastern boundary of this block, hitherto associated with the Kopili foreland fault, appears to be diffuse. In eastern Bhutan, we image a seismogenic, flat portion of the MHT, which might be related to a partially creeping fault segment or increased background seismicity originating from the 2009 MW6.1 earthquake. In western-central Bhutan, clusters of micro-earthquakes at the front of the High-Himalayas indicate the presence of a mid-crustal ramp and stress buildup on a fully coupled MHT. The area bounded by the DCF in the west and the seismogenic MHT in the east has the potential for M7-8 earthquakes in Bhutan. Similarly, the DCF has the potential to host M7 earthquakes beneath the densely populated foreland basin as documented by the Dhubri earthquake of 1930, which is likely associated to this structure.

Seismotectonics of Bhutan: Evidence for segmentation of the Eastern Himalayas and link to foreland deformation

NASA Astrophysics Data System (ADS)

Diehl, Tobias; Singer, Julia; Hetényi, György; Grujic, Djordje; Clinton, John; Giardini, Domenico; Kissling, Edi; Gansser Working Group

2017-08-01

The instrumental record of Bhutan is characterized by a lower seismicity compared to other parts of the Himalayan arc. To understand this low activity and its impact on the seismic hazard, a seismic network was installed in Bhutan for 22 months between 2013 and 2014. Recorded seismicity, earthquake moment tensors and local earthquake tomography reveal along-strike variations in structure and crustal deformation regime. A thickened crust imaged in western Bhutan suggests lateral differences in stresses on the Main Himalayan Thrust (MHT), potentially affecting the interseismic coupling and deformation regime. Sikkim, western Bhutan and its foreland are characterized by strike-slip faulting in the Indian basement. Strain is particularly localized along a NW-SE striking mid-crustal fault zone reaching from Chungthang in northeast Sikkim to Dhubri at the northwestern edge of the Shillong Plateau in the foreland. The dextral Dhubri-Chungthang fault zone (DCF) causes segmentation of the Indian basement and the MHT between eastern Nepal and western Bhutan and connects the deformation front of the Himalaya with the Shillong Plateau by forming the western boundary of the Shillong block. The Kopili fault, the proposed eastern boundary of this block, appears to be a diffuse zone of mid-crustal seismicity in the foreland. In eastern Bhutan we image a seismogenic, flat portion of the MHT, which might be either related to a partially creeping segment or to increased background seismicity originating from the 2009 MW 6.1 earthquake. In western-central Bhutan clusters of micro-earthquakes at the front of the High-Himalayas indicate the presence of a mid-crustal ramp and stress buildup on a fully coupled MHT. The area bounded by the DCF in the west and the seismogenic MHT in the east has the potential for M7-8 earthquakes in Bhutan. Similarly, the DCF has the potential to host M7 earthquakes as documented by the 2011 Sikkim and the 1930 Dhubri earthquakes, which were potentially associated with this structure.

TU-F-17A-03: A 4D Lung Phantom for Coupled Registration/Segmentation Evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Markel, D; El Naqa, I; Levesque, I

2014-06-15

Purpose: Coupling the processes of segmentation and registration (regmentation) is a recent development that allows improved efficiency and accuracy for both steps and may improve the clinical feasibility of online adaptive radiotherapy. Presented is a multimodality animal tissue model designed specifically to provide a ground truth to simultaneously evaluate segmentation and registration errors during respiratory motion. Methods: Tumor surrogates were constructed from vacuum sealed hydrated natural sea sponges with catheters used for the injection of PET radiotracer. These contained two compartments allowing for two concentrations of radiotracer mimicking both tumor and background signals. The lungs were inflated to different volumesmore » using an air pump and flow valve and scanned using PET/CT and MRI. Anatomical landmarks were used to evaluate the registration accuracy using an automated bifurcation tracking pipeline for reproducibility. The bifurcation tracking accuracy was assessed using virtual deformations of 2.6 cm, 5.2 cm and 7.8 cm of a CT scan of a corresponding human thorax. Bifurcations were detected in the deformed dataset and compared to known deformation coordinates for 76 points. Results: The bifurcation tracking accuracy was found to have a mean error of −0.94, 0.79 and −0.57 voxels in the left-right, anterior-posterior and inferior-superior axes using a 1×1×5 mm3 resolution after the CT volume was deformed 7.8 cm. The tumor surrogates provided a segmentation ground truth after being registered to the phantom image. Conclusion: A swine lung model in conjunction with vacuum sealed sponges and a bifurcation tracking algorithm is presented that is MRI, PET and CT compatible and anatomically and kinetically realistic. Corresponding software for tracking anatomical landmarks within the phantom shows sub-voxel accuracy. Vacuum sealed sponges provide realistic tumor surrogate with a known boundary. A ground truth with minimal uncertainty is thus realized that can be used for comparing the performance of registration and segmentation algorithms.« less

Study of the deformation in Central Afar using InSAR NSBAS chain

NASA Astrophysics Data System (ADS)

Deprez, A.; Doubre, C.; Grandin, R.; Saad, I.; Masson, F.; Socquet, A.

2013-12-01

The Afar Depression (East Africa) connects all three continental plates of Arabia, Somalia and Nubia plates. For over 20 Ma, the divergent motion of these plates has led to the formation of large normal faults building tall scarps between the high plateaus and the depression, and the development of large basins and an incipient seafloor spreading along a series of active volcano-tectonic rift segments within the depression. The space-time evolution of the active surface deformation over the whole Afar region remains uncertain. Previous tectonic and geodetic studies confirm that a large part of the current deformation is concentrated along these segments. However, the amount of extension accommodated by other non-volcanic basins and normal faulting remains unclear, despite significant micro-seismic activity. Due to the active volcanism, large transient displacements related to dyking sequence, notably in the Manda Hararo rift (2005-2010), increase the difficulty to characterize the deformation field over simple time and space scales. In this study, we attempt to obtain a complete inventory of the deformation within the whole Afar Depression and to understand the associated phenomena, which occurred in this singular tectonic environment. We study in particular, the behavior of the structures activated during the post-dyking stage of the rift segments. For this purpose, we conduct a careful processing of a large set of SAR ENVISAT images over the 2004-2010 period, we also use previous InSAR results and GPS data from permanent stations and from campaigns conducted in 1999, 2003, 2010, 2012 within a GPS network particularly dense along the Asal-Ghoubbet segment. In one hand, in the western part of Afar, the far-field response of the 2005-2010 dyke sequence appears to be the dominant surface motion on the mean velocity field. In an other hand, more eastward across the Asal-Ghoubbet rift, strong gradients of deformation are observed. The time series analysis of both InSAR and GPS data allow us to (1) point out the role of volcano activity on the localization of the extensive deformation within these rifts, (2) describe the temporal evolution of the mostly aseismic fault slips, and eventually (3) characterize the behavior of the crust after the dyking events in relation to visco-elastic relaxation. Moreover, we analyze several interesting small patches of localized deformation revealing transient displacements by combining time series results and seismic data collected by the Arta Geophysical Observatory in Djibouti. In particular, a specific clear deformation pattern on the northern margin of the Tadjoura Bay could be associated with a seismic swarm, probably resulting from the occurrence of an offshore dyking event sequence along the immerged Tadjoura rift segment.

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation.

PubMed

Mansoor, Awais; Cerrolaza, Juan J; Perez, Geovanny; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-02-11

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM 1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

Marginal shape deep learning: applications to pediatric lung field segmentation

NASA Astrophysics Data System (ADS)

Mansoor, Awais; Cerrolaza, Juan J.; Perez, Geovany; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-02-01

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, local- ization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0:927 using only the four highest modes of variation (compared to 0:888 with classical ASM1 (p-value=0:01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation

PubMed Central

Mansoor, Awais; Cerrolaza, Juan J.; Perez, Geovanny; Biggs, Elijah; Nino, Gustavo; Linguraru, Marius George

2017-01-01

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects. PMID:28592911

Interactive deformation registration of endorectal prostate MRI using ITK thin plate splines.

PubMed

Cheung, M Rex; Krishnan, Karthik

2009-03-01

Magnetic resonance imaging with an endorectal coil allows high-resolution imaging of prostate cancer and the surrounding normal organs. These anatomic details can be used to direct radiotherapy. However, organ deformation introduced by the endorectal coil makes it difficult to register magnetic resonance images for treatment planning. In this study, plug-ins for the volume visualization software VolView were implemented on the basis of algorithms from the National Library of Medicine's Insight Segmentation and Registration Toolkit (ITK). Magnetic resonance images of a phantom simulating human pelvic structures were obtained with and without the endorectal coil balloon inflated. The prostate not deformed by the endorectal balloon was registered to the deformed prostate using an ITK thin plate spline (TPS). This plug-in allows the use of crop planes to limit the deformable registration in the region of interest around the prostate. These crop planes restricted the support of the TPS to the area around the prostate, where most of the deformation occurred. The region outside the crop planes was anchored by grid points. The TPS was more accurate in registering the local deformation of the prostate compared with a TPS variant, the elastic body spline. The TPS was also applied to register an in vivo T(2)-weighted endorectal magnetic resonance image. The intraprostatic tumor was accurately registered. This could potentially guide the boosting of intraprostatic targets. The source and target landmarks were placed graphically. This TPS plug-in allows the registration to be undone. The landmarks could be added, removed, and adjusted in real time and in three dimensions between repeated registrations. This interactive TPS plug-in allows a user to obtain a high level of accuracy satisfactory to a specific application efficiently. Because it is open-source software, the imaging community will be able to validate and improve the algorithm.

Segmentation of the heart and major vascular structures in cardiovascular CT images

NASA Astrophysics Data System (ADS)

Peters, J.; Ecabert, O.; Lorenz, C.; von Berg, J.; Walker, M. J.; Ivanc, T. B.; Vembar, M.; Olszewski, M. E.; Weese, J.

2008-03-01

Segmentation of organs in medical images can be successfully performed with shape-constrained deformable models. A surface mesh is attracted to detected image boundaries by an external energy, while an internal energy keeps the mesh similar to expected shapes. Complex organs like the heart with its four chambers can be automatically segmented using a suitable shape variablility model based on piecewise affine degrees of freedom. In this paper, we extend the approach to also segment highly variable vascular structures. We introduce a dedicated framework to adapt an extended mesh model to freely bending vessels. This is achieved by subdividing each vessel into (short) tube-shaped segments ("tubelets"). These are assigned to individual similarity transformations for local orientation and scaling. Proper adaptation is achieved by progressively adapting distal vessel parts to the image only after proximal neighbor tubelets have already converged. In addition, each newly activated tubelet inherits the local orientation and scale of the preceeding one. To arrive at a joint segmentation of chambers and vasculature, we extended a previous model comprising endocardial surfaces of the four chambers, the left ventricular epicardium, and a pulmonary artery trunk. Newly added are the aorta (ascending and descending plus arch), superior and inferior vena cava, coronary sinus, and four pulmonary veins. These vessels are organized as stacks of triangulated rings. This mesh configuration is most suitable to define tubelet segments. On 36 CT data sets reconstructed at several cardiac phases from 17 patients, segmentation accuracies of 0.61-0.80mm are obtained for the cardiac chambers. For the visible parts of the newly added great vessels, surface accuracies of 0.47-1.17mm are obtained (larger errors are asscociated with faintly contrasted venous structures).

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

PubMed

Karimi, Davood; Samei, Golnoosh; Kesch, Claudia; Nir, Guy; Salcudean, Septimiu E

2018-05-15

Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.

A shape prior-based MRF model for 3D masseter muscle segmentation

NASA Astrophysics Data System (ADS)

Majeed, Tahir; Fundana, Ketut; Lüthi, Marcel; Beinemann, Jörg; Cattin, Philippe

2012-02-01

Medical image segmentation is generally an ill-posed problem that can only be solved by incorporating prior knowledge. The ambiguities arise due to the presence of noise, weak edges, imaging artifacts, inhomogeneous interior and adjacent anatomical structures having similar intensity profile as the target structure. In this paper we propose a novel approach to segment the masseter muscle using the graph-cut incorporating additional 3D shape priors in CT datasets, which is robust to noise; artifacts; and shape deformations. The main contribution of this paper is in translating the 3D shape knowledge into both unary and pairwise potentials of the Markov Random Field (MRF). The segmentation task is casted as a Maximum-A-Posteriori (MAP) estimation of the MRF. Graph-cut is then used to obtain the global minimum which results in the segmentation of the masseter muscle. The method is tested on 21 CT datasets of the masseter muscle, which are noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. the very common dental fillings and dental implants. We show that the proposed technique produces clinically acceptable results to the challenging problem of muscle segmentation, and further provide a quantitative and qualitative comparison with other methods. We statistically show that adding additional shape prior into both unary and pairwise potentials can increase the robustness of the proposed method in noisy datasets.

Subcortical structure segmentation using probabilistic atlas priors

NASA Astrophysics Data System (ADS)

Gouttard, Sylvain; Styner, Martin; Joshi, Sarang; Smith, Rachel G.; Cody Hazlett, Heather; Gerig, Guido

2007-03-01

The segmentation of the subcortical structures of the brain is required for many forms of quantitative neuroanatomic analysis. The volumetric and shape parameters of structures such as lateral ventricles, putamen, caudate, hippocampus, pallidus and amygdala are employed to characterize a disease or its evolution. This paper presents a fully automatic segmentation of these structures via a non-rigid registration of a probabilistic atlas prior and alongside a comprehensive validation. Our approach is based on an unbiased diffeomorphic atlas with probabilistic spatial priors built from a training set of MR images with corresponding manual segmentations. The atlas building computes an average image along with transformation fields mapping each training case to the average image. These transformation fields are applied to the manually segmented structures of each case in order to obtain a probabilistic map on the atlas. When applying the atlas for automatic structural segmentation, an MR image is first intensity inhomogeneity corrected, skull stripped and intensity calibrated to the atlas. Then the atlas image is registered to the image using an affine followed by a deformable registration matching the gray level intensity. Finally, the registration transformation is applied to the probabilistic maps of each structures, which are then thresholded at 0.5 probability. Using manual segmentations for comparison, measures of volumetric differences show high correlation with our results. Furthermore, the dice coefficient, which quantifies the volumetric overlap, is higher than 62% for all structures and is close to 80% for basal ganglia. The intraclass correlation coefficient computed on these same datasets shows a good inter-method correlation of the volumetric measurements. Using a dataset of a single patient scanned 10 times on 5 different scanners, reliability is shown with a coefficient of variance of less than 2 percents over the whole dataset. Overall, these validation and reliability studies show that our method accurately and reliably segments almost all structures. Only the hippocampus and amygdala segmentations exhibit relative low correlation with the manual segmentation in at least one of the validation studies, whereas they still show appropriate dice overlap coefficients.

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images.

PubMed

Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

2010-07-21

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression conditions.

Utility of three-dimensional and multiplanar reformatted computed tomography for evaluation of pediatric congenital spine abnormalities.

PubMed

Newton, Peter O; Hahn, Gregory W; Fricka, Kevin B; Wenger, Dennis R

2002-04-15

A retrospective radiographic review of 31 patients with congenital spine abnormalities who underwent conventional radiography and advanced imaging studies was conducted. To analyze the utility of three-dimensional computed tomography with multiplanar reformatted images for congenital spine anomalies, as compared with plain radiographs and axial two-dimensional computed tomography imaging. Conventional radiographic imaging for congenital spine disorders often are difficult to interpret because of the patient's small size, the complexity of the disorder, a deformity not in the plane of the radiographs, superimposed structures, and difficulty in forming a mental three-dimensional image. Multiplanar reformatted and three-dimensional computed tomographic imaging offers many potential advantages for defining congenital spine anomalies including visualization of the deformity in any plane, from any angle, with the overlying structures subtracted. The imaging studies of patients who had undergone a three-dimensional computed tomography for congenital deformities of the spine between 1992 and 1998 were reviewed (31 cases). All plain radiographs and axial two-dimensional computed tomography images performed before the three-dimensional computed tomography were reviewed and the findings documented. This was repeated for the three-dimensional reconstructions and, when available, the multiplanar reformatted images (15 cases). In each case, the utility of the advanced imaging was graded as one of the following: Grade A (substantial new information obtained), Grade B (confirmatory with improved visualization and understanding of the deformity), and Grade C (no added useful information obtained). In 17 of 31 cases, the multiplanar reformatted and three-dimensional images allowed identification of unrecognized malformations. In nine additional cases, the advanced imaging was helpful in better visualizing and understanding previously identified deformities. In five cases, no new information was gained. The standard and curved multiplanar reformatted images were best for defining the occiput-C1-C2 anatomy and the extent of segmentation defects. The curved multiplanar reformatted images were especially helpful in keeping the spine from "coming in" and "going out" of the plane of the image when there was significant spine deformity in the sagittal or coronal plane. The three-dimensional reconstructions proved valuable in defining failures of formation. Advanced computed tomography imaging (three-dimensional computed tomography and curved/standard multiplanar reformatted images) allows better definition of congenital spine anomalies. More than 50% of the cases showed additional abnormalities not appreciated on plain radiographs or axial two-dimensional computed tomography images. Curved multiplanar reformatted images allowed imaging in the coronal and sagittal planes of the entire deformity.

Tomographic particle image velocimetry of desert locust wakes: instantaneous volumes combine to reveal hidden vortex elements and rapid wake deformation

PubMed Central

Bomphrey, Richard J.; Henningsson, Per; Michaelis, Dirk; Hollis, David

2012-01-01

Aerodynamic structures generated by animals in flight are unstable and complex. Recent progress in quantitative flow visualization has advanced our understanding of animal aerodynamics, but measurements have hitherto been limited to flow velocities at a plane through the wake. We applied an emergent, high-speed, volumetric fluid imaging technique (tomographic particle image velocimetry) to examine segments of the wake of desert locusts, capturing fully three-dimensional instantaneous flow fields. We used those flow fields to characterize the aerodynamic footprint in unprecedented detail and revealed previously unseen wake elements that would have gone undetected by two-dimensional or stereo-imaging technology. Vortex iso-surface topographies show the spatio-temporal signature of aerodynamic force generation manifest in the wake of locusts, and expose the extent to which animal wakes can deform, potentially leading to unreliable calculations of lift and thrust when using conventional diagnostic methods. We discuss implications for experimental design and analysis as volumetric flow imaging becomes more widespread. PMID:22977102

Automated pulmonary lobar ventilation measurements using volume-matched thoracic CT and MRI

NASA Astrophysics Data System (ADS)

Guo, F.; Svenningsen, S.; Bluemke, E.; Rajchl, M.; Yuan, J.; Fenster, A.; Parraga, G.

2015-03-01

Objectives: To develop and evaluate an automated registration and segmentation pipeline for regional lobar pulmonary structure-function measurements, using volume-matched thoracic CT and MRI in order to guide therapy. Methods: Ten subjects underwent pulmonary function tests and volume-matched 1H and 3He MRI and thoracic CT during a single 2-hr visit. CT was registered to 1H MRI using an affine method that incorporated block-matching and this was followed by a deformable step using free-form deformation. The resultant deformation field was used to deform the associated CT lobe mask that was generated using commercial software. 3He-1H image registration used the same two-step registration method and 3He ventilation was segmented using hierarchical k-means clustering. Whole lung and lobar 3He ventilation and ventilation defect percent (VDP) were generated by mapping ventilation defects to CT-defined whole lung and lobe volumes. Target CT-3He registration accuracy was evaluated using region- , surface distance- and volume-based metrics. Automated whole lung and lobar VDP was compared with semi-automated and manual results using paired t-tests. Results: The proposed pipeline yielded regional spatial agreement of 88.0+/-0.9% and surface distance error of 3.9+/-0.5 mm. Automated and manual whole lung and lobar ventilation and VDP were not significantly different and they were significantly correlated (r = 0.77, p < 0.0001). Conclusion: The proposed automated pipeline can be used to generate regional pulmonary structural-functional maps with high accuracy and robustness, providing an important tool for image-guided pulmonary interventions.

Individual bone structure segmentation and labeling from low-dose chest CT

NASA Astrophysics Data System (ADS)

Liu, Shuang; Xie, Yiting; Reeves, Anthony P.

2017-03-01

The segmentation and labeling of the individual bones serve as the first step to the fully automated measurement of skeletal characteristics and the detection of abnormalities such as skeletal deformities, osteoporosis, and vertebral fractures. Moreover, the identified landmarks on the segmented bone structures can potentially provide relatively reliable location reference to other non-rigid human organs, such as breast, heart and lung, thereby facilitating the corresponding image analysis and registration. A fully automated anatomy-directed framework for the segmentation and labeling of the individual bone structures from low-dose chest CT is presented in this paper. The proposed system consists of four main stages: First, both clavicles are segmented and labeled by fitting a piecewise cylindrical envelope. Second, the sternum is segmented under the spatial constraints provided by the segmented clavicles. Third, all ribs are segmented and labeled based on 3D region growing within the volume of interest defined with reference to the spinal canal centerline and lungs. Fourth, the individual thoracic vertebrae are segmented and labeled by image intensity based analysis in the spatial region constrained by the previously segmented bone structures. The system performance was validated with 1270 lowdose chest CT scans through visual evaluation. Satisfactory performance was obtained respectively in 97.1% cases for the clavicle segmentation and labeling, in 97.3% cases for the sternum segmentation, in 97.2% cases for the rib segmentation, in 94.2% cases for the rib labeling, in 92.4% cases for vertebra segmentation and in 89.9% cases for the vertebra labeling.

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

PubMed

Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

2016-07-01

We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI.

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images.

PubMed

Berton, Florian; Cheriet, Farida; Miron, Marie-Claude; Laporte, Catherine

2016-05-01

Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key vertebral landmarks. However, the interpretation of vertebral ultrasound images is challenging, primarily because acoustic waves are entirely reflected by bone. To alleviate this problem, we propose an algorithm to segment these images into three regions: the spinous process, its acoustic shadow and other tissues. This method consists, first, in the extraction of several image features and the selection of the most relevant ones for the discrimination of the three regions. Then, using this set of features and linear discriminant analysis, each pixel of the image is classified as belonging to one of the three regions. Finally, the image is segmented by regularizing the pixel-wise classification results to account for some geometrical properties of vertebrae. The feature set was first validated by analyzing the classification results across a learning database. The database contained 107 vertebral ultrasound images acquired with convex and linear probes. Classification rates of 84%, 92% and 91% were achieved for the spinous process, the acoustic shadow and other tissues, respectively. Dice similarity coefficients of 0.72 and 0.88 were obtained respectively for the spinous process and acoustic shadow, confirming that the proposed method accurately segments the spinous process and its acoustic shadow in vertebral ultrasound images. Furthermore, the centroid of the automatically segmented spinous process was located at an average distance of 0.38 mm from that of the manually labeled spinous process, which is on the order of image resolution. This suggests that the proposed method is a promising tool for the measurement of the Spinous Process Angle and, more generally, for assisting ultrasound-based assessment of scoliosis progression. Copyright © 2016 Elsevier Ltd. All rights reserved.

Ranges of Cervical Intervertebral Disc Deformation During an In Vivo Dynamic Flexion–Extension of the Neck

PubMed Central

Yu, Yan; Mao, Haiqing; Li, Jing-Sheng; Tsai, Tsung-Yuan; Cheng, Liming; Wood, Kirkham B.; Li, Guoan; Cha, Thomas D.

2017-01-01

While abnormal loading is widely believed to cause cervical spine disc diseases, in vivo cervical disc deformation during dynamic neck motion has not been well delineated. This study investigated the range of cervical disc deformation during an in vivo functional flexion–extension of the neck. Ten asymptomatic human subjects were tested using a combined dual fluoroscopic imaging system (DFIS) and magnetic resonance imaging (MRI)-based three-dimensional (3D) modeling technique. Overall disc deformation was determined using the changes of the space geometry between upper and lower endplates of each intervertebral segment (C3/4, C4/5, C5/6, and C6/7). Five points (anterior, center, posterior, left, and right) of each disc were analyzed to examine the disc deformation distributions. The data indicated that between the functional maximum flexion and extension of the neck, the anterior points of the discs experienced large changes of distraction/compression deformation and shear deformation. The higher level discs experienced higher ranges of disc deformation. No significant difference was found in deformation ranges at posterior points of all the discs. The data indicated that the range of disc deformation is disc level dependent and the anterior region experienced larger changes of deformation than the center and posterior regions, except for the C6/7 disc. The data obtained from this study could serve as baseline knowledge for the understanding of the cervical spine disc biomechanics and for investigation of the biomechanical etiology of disc diseases. These data could also provide insights for development of motion preservation surgeries for cervical spine. PMID:28334358

Ranges of Cervical Intervertebral Disc Deformation During an In Vivo Dynamic Flexion-Extension of the Neck.

PubMed

Yu, Yan; Mao, Haiqing; Li, Jing-Sheng; Tsai, Tsung-Yuan; Cheng, Liming; Wood, Kirkham B; Li, Guoan; Cha, Thomas D

2017-06-01

While abnormal loading is widely believed to cause cervical spine disc diseases, in vivo cervical disc deformation during dynamic neck motion has not been well delineated. This study investigated the range of cervical disc deformation during an in vivo functional flexion-extension of the neck. Ten asymptomatic human subjects were tested using a combined dual fluoroscopic imaging system (DFIS) and magnetic resonance imaging (MRI)-based three-dimensional (3D) modeling technique. Overall disc deformation was determined using the changes of the space geometry between upper and lower endplates of each intervertebral segment (C3/4, C4/5, C5/6, and C6/7). Five points (anterior, center, posterior, left, and right) of each disc were analyzed to examine the disc deformation distributions. The data indicated that between the functional maximum flexion and extension of the neck, the anterior points of the discs experienced large changes of distraction/compression deformation and shear deformation. The higher level discs experienced higher ranges of disc deformation. No significant difference was found in deformation ranges at posterior points of all the discs. The data indicated that the range of disc deformation is disc level dependent and the anterior region experienced larger changes of deformation than the center and posterior regions, except for the C6/7 disc. The data obtained from this study could serve as baseline knowledge for the understanding of the cervical spine disc biomechanics and for investigation of the biomechanical etiology of disc diseases. These data could also provide insights for development of motion preservation surgeries for cervical spine.

Surface-based prostate registration with biomechanical regularization

NASA Astrophysics Data System (ADS)

van de Ven, Wendy J. M.; Hu, Yipeng; Barentsz, Jelle O.; Karssemeijer, Nico; Barratt, Dean; Huisman, Henkjan J.

2013-03-01

Adding MR-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound by using MRUS registration. A common approach is to use surface-based registration. We hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular surface-based registration method. We developed a novel method by extending a surface-based registration with finite element (FE) simulation to better predict internal deformation of the prostate. For each of six patients, a tetrahedral mesh was constructed from the manual prostate segmentation. Next, the internal prostate deformation was simulated using the derived radial surface displacement as boundary condition. The deformation field within the gland was calculated using the predicted FE node displacements and thin-plate spline interpolation. We tested our method on MR guided MR biopsy imaging data, as landmarks can easily be identified on MR images. For evaluation of the registration accuracy we used 45 anatomical landmarks located in all regions of the prostate. Our results show that the median target registration error of a surface-based registration with biomechanical regularization is 1.88 mm, which is significantly different from 2.61 mm without biomechanical regularization. We can conclude that biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to regular surface-based registration.

Extracting a Purely Non-rigid Deformation Field of a Single Structure

NASA Astrophysics Data System (ADS)

Demirci, Stefanie; Manstad-Hulaas, Frode; Navab, Nassir

During endovascular aortic repair (EVAR) treatment, the aortic shape is subject to severe deformation that is imposed by medical instruments such as guide wires, catheters, and the stent graft. The problem definition of deformable registration of images covering the entire abdominal region, however, is highly ill-posed. We present a new method for extracting the deformation of an aneurysmatic aorta. The outline of the procedure includes initial rigid alignment of two abdominal scans, segmentation of abdominal vessel trees, and automatic reduction of their centerline structures to one specified region of interest around the aorta. Our non-rigid registration procedure then only computes local non-rigid deformation and leaves out all remaining global rigid transformations. In order to evaluate our method, experiments for the extraction of aortic deformation fields are conducted on 15 patient datasets from endovascular aortic repair (EVAR) treatment. A visual assessment of the registration results were performed by two vascular surgeons and one interventional radiologist who are all experts in EVAR procedures.

Deformable torso phantoms of Chinese adults for personalized anatomy modelling.

PubMed

Wang, Hongkai; Sun, Xiaobang; Wu, Tongning; Li, Congsheng; Chen, Zhonghua; Liao, Meiying; Li, Mengci; Yan, Wen; Huang, Hui; Yang, Jia; Tan, Ziyu; Hui, Libo; Liu, Yue; Pan, Hang; Qu, Yue; Chen, Zhaofeng; Tan, Liwen; Yu, Lijuan; Shi, Hongcheng; Huo, Li; Zhang, Yanjun; Tang, Xin; Zhang, Shaoxiang; Liu, Changjian

2018-04-16

In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RC vlm ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community. © 2018 Anatomical Society.

Correlation-based discrimination between cardiac tissue and blood for segmentation of 3D echocardiographic images

NASA Astrophysics Data System (ADS)

Saris, Anne E. C. M.; Nillesen, Maartje M.; Lopata, Richard G. P.; de Korte, Chris L.

2013-03-01

Automated segmentation of 3D echocardiographic images in patients with congenital heart disease is challenging, because the boundary between blood and cardiac tissue is poorly defined in some regions. Cardiologists mentally incorporate movement of the heart, using temporal coherence of structures to resolve ambiguities. Therefore, we investigated the merit of temporal cross-correlation for automated segmentation over the entire cardiac cycle. Optimal settings for maximum cross-correlation (MCC) calculation, based on a 3D cross-correlation based displacement estimation algorithm, were determined to obtain the best contrast between blood and myocardial tissue over the entire cardiac cycle. Resulting envelope-based as well as RF-based MCC values were used as additional external force in a deformable model approach, to segment the left-ventricular cavity in entire systolic phase. MCC values were tested against, and combined with, adaptive filtered, demodulated RF-data. Segmentation results were compared with manually segmented volumes using a 3D Dice Similarity Index (3DSI). Results in 3D pediatric echocardiographic images sequences (n = 4) demonstrate that incorporation of temporal information improves segmentation. The use of MCC values, either alone or in combination with adaptive filtered, demodulated RF-data, resulted in an increase of the 3DSI in 75% of the cases (average 3DSI increase: 0.71 to 0.82). Results might be further improved by optimizing MCC-contrast locally, in regions with low blood-tissue contrast. Reducing underestimation of the endocardial volume due to MCC processing scheme (choice of window size) and consequential border-misalignment, could also lead to more accurate segmentations. Furthermore, increasing the frame rate will also increase MCC-contrast and thus improve segmentation.

Computational Simulation of Breast Compression Based on Segmented Breast and Fibroglandular Tissues on Magnetic Resonance Images

PubMed Central

Shih, Tzu-Ching; Chen, Jeon-Hor; Liu, Dongxu; Nie, Ke; Sun, Lizhi; Lin, Muqing; Chang, Daniel; Nalcioglu, Orhan; Su, Min-Ying

2010-01-01

This study presents a finite element based computational model to simulate the three-dimensional deformation of the breast and the fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and the craniocaudal and mediolateral oblique compression as used in mammography was applied. The geometry of whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo® 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the non-linear elastic tissue deformation under compression, using the MSC.Marc® software package. The model was tested in 4 cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these 4 cases at 60% compression ratio was in the range of 5-7 cm, which is the typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at 60% compression ratio was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on MRI, which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density measurements needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities – such as MRI, mammography, whole breast ultrasound, and molecular imaging – that are performed using different body positions and different compression conditions. PMID:20601773

Method to Reduce Target Motion Through Needle-Tissue Interactions.

PubMed

Oldfield, Matthew J; Leibinger, Alexander; Seah, Tian En Timothy; Rodriguez Y Baena, Ferdinando

2015-11-01

During minimally invasive surgical procedures, it is often important to deliver needles to particular tissue volumes. Needles, when interacting with a substrate, cause deformation and target motion. To reduce reliance on compensatory intra-operative imaging, a needle design and novel delivery mechanism is proposed. Three-dimensional finite element simulations of a multi-segment needle inserted into a pre-existing crack are presented. The motion profiles of the needle segments are varied to identify methods that reduce target motion. Experiments are then performed by inserting a needle into a gelatine tissue phantom and measuring the internal target motion using digital image correlation. Simulations indicate that target motion is reduced when needle segments are stroked cyclically and utilise a small amount of retraction instead of being held stationary. Results are confirmed experimentally by statistically significant target motion reductions of more than 8% during cyclic strokes and 29% when also incorporating retraction, with the same net insertion speed. By using a multi-segment needle and taking advantage of frictional interactions on the needle surface, it is demonstrated that target motion ahead of an advancing needle can be substantially reduced.

A potential means of improving the evaluation of deformity corrections with Taylor spatial frames over time by using volumetric imaging: preliminary results.

PubMed

Starr, Vanessa; Olivecrona, H; Noz, M E; Maguire, G Q; Zeleznik, M P; Jannsson, Karl-åke

2009-01-01

In this study we explore the possibility of accurately and cost-effectively monitoring tibial deformation induced by Taylor Spatial Frames (TSFs), using time-separated computed tomography (CT) scans and a volume fusion technique to determine tibial rotation and translation. Serial CT examinations (designated CT-A and CT-B, separated by a time interval of several months) of two patients were investigated using a previously described and validated volume fusion technique, in which user-defined landmarks drive the 3D registration of the two CT volumes. Both patients had undergone dual osteotomies to correct for tibial length and rotational deformity. For each registration, 10 or more landmarks were selected, and the quality of the fused volume was assessed both quantitatively and via 2D and 3D visualization tools. First, the proximal frame segment and tibia in CT-A and CT-B were brought into alignment (registered) by selecting landmarks on the frame and/or tibia. In the resulting "fused" volume, the proximal frame segment and tibia from CT-A and CT-B were aligned, while the distal frame segment and tibia from CT-A and CT-B were likely not aligned as a result of tibial deformation or frame adjustment having occurred between the CT scans. Using the proximal fused volume, the distal frame segment and tibia were then registered by selecting landmarks on the frame and/or tibia. The difference between the centroids of the final distal landmarks was used to evaluate the lengthening of the tibia, and the Euler angles from the registration were used to evaluate the rotation. Both the frame and bone could be effectively registered (based on visual interpretation). Movement between the proximal frame and proximal bone could be visualized in both cases. The spatial effect on the tibia could be both visually assessed and measured: 34 mm, 10 degrees in one case; 5 mm, 1 degrees in the other. This retrospective analysis of spatial correction of the tibia using Taylor Spatial Frames shows that CT offers an interesting potential means of quantitatively monitoring the patient's treatment. Compared with traditional techniques, modern CT scans in conjunction with image processing provide a high-resolution, spatially correct, and three-dimensional measurement system which can be used to quickly and easily assess the patient's treatment at low cost to the patient and hospital.

Evaluation of non-rigid registration parameters for atlas-based segmentation of CT images of human cochlea

NASA Astrophysics Data System (ADS)

Elfarnawany, Mai; Alam, S. Riyahi; Agrawal, Sumit K.; Ladak, Hanif M.

2017-02-01

Cochlear implant surgery is a hearing restoration procedure for patients with profound hearing loss. In this surgery, an electrode is inserted into the cochlea to stimulate the auditory nerve and restore the patient's hearing. Clinical computed tomography (CT) images are used for planning and evaluation of electrode placement, but their low resolution limits the visualization of internal cochlear structures. Therefore, high resolution micro-CT images are used to develop atlas-based segmentation methods to extract these nonvisible anatomical features in clinical CT images. Accurate registration of the high and low resolution CT images is a prerequisite for reliable atlas-based segmentation. In this study, we evaluate and compare different non-rigid B-spline registration parameters using micro-CT and clinical CT images of five cadaveric human cochleae. The varying registration parameters are cost function (normalized correlation (NC), mutual information and mean square error), interpolation method (linear, windowed-sinc and B-spline) and sampling percentage (1%, 10% and 100%). We compare the registration results visually and quantitatively using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and absolute percentage error in cochlear volume. Using MI or MSE cost functions and linear or windowed-sinc interpolation resulted in visually undesirable deformation of internal cochlear structures. Quantitatively, the transforms using 100% sampling percentage yielded the highest DSC and smallest HD (0.828+/-0.021 and 0.25+/-0.09mm respectively). Therefore, B-spline registration with cost function: NC, interpolation: B-spline and sampling percentage: moments 100% can be the foundation of developing an optimized atlas-based segmentation algorithm of intracochlear structures in clinical CT images.

Automated segmentation of serous pigment epithelium detachment in SD-OCT images

NASA Astrophysics Data System (ADS)

Sun, Zhuli; Shi, Fei; Xiang, Dehui; Chen, Haoyu; Chen, Xinjian

2015-03-01

Pigment epithelium detachment (PED) is an important clinical manifestation of multiple chorio-retinal disease processes, which can cause the loss of central vision. A 3-D method is proposed to automatically segment serous PED in SD-OCT images. The proposed method consists of five steps: first, a curvature anisotropic diffusion filter is applied to remove speckle noise. Second, the graph search method is applied for abnormal retinal layer segmentation associated with retinal pigment epithelium (RPE) deformation. During this process, Bruch's membrane, which doesn't show in the SD-OCT images, is estimated with the convex hull algorithm. Third, the foreground and background seeds are automatically obtained from retinal layer segmentation result. Fourth, the serous PED is segmented based on the graph cut method. Finally, a post-processing step is applied to remove false positive regions based on mathematical morphology. The proposed method was tested on 20 SD-OCT volumes from 20 patients diagnosed with serous PED. The average true positive volume fraction (TPVF), false positive volume fraction (FPVF), dice similarity coefficient (DSC) and positive predictive value (PPV) are 97.19%, 0.03%, 96.34% and 95.59%, respectively. Linear regression analysis shows a strong correlation (r = 0.975) comparing the segmented PED volumes with the ground truth labeled by an ophthalmology expert. The proposed method can provide clinicians with accurate quantitative information, including shape, size and position of the PED regions, which can assist diagnose and treatment.

α-Information Based Registration of Dynamic Scans for Magnetic Resonance Cystography

PubMed Central

Han, Hao; Lin, Qin; Li, Lihong; Duan, Chaijie; Lu, Hongbing; Li, Haifang; Yan, Zengmin; Fitzgerald, John

2015-01-01

To continue our effort on developing magnetic resonance (MR) cystography, we introduce a novel non–rigid 3D registration method to compensate for bladder wall motion and deformation in dynamic MR scans, which are impaired by relatively low signal–to–noise ratio in each time frame. The registration method is developed on the similarity measure of α–information, which has the potential of achieving higher registration accuracy than the commonly-used mutual information (MI) measure for either mono-modality or multi-modality image registration. The α–information metric was also demonstrated to be superior to both the mean squares and the cross-correlation metrics in multi-modality scenarios. The proposed α–registration method was applied for bladder motion compensation via real patient studies, and its effect to the automatic and accurate segmentation of bladder wall was also evaluated. Compared with the prevailing MI-based image registration approach, the presented α–information based registration was more effective to capture the bladder wall motion and deformation, which ensured the success of the following bladder wall segmentation to achieve the goal of evaluating the entire bladder wall for detection and diagnosis of abnormality. PMID:26087506

ImageParser: a tool for finite element generation from three-dimensional medical images

PubMed Central

Yin, HM; Sun, LZ; Wang, G; Yamada, T; Wang, J; Vannier, MW

2004-01-01

Background The finite element method (FEM) is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures) of interest (ROIs) may be irregular and fuzzy. Methods A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements. Results The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues. Conclusion The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information. PMID:15461787

Modeling 4D Pathological Changes by Leveraging Normative Models

PubMed Central

Wang, Bo; Prastawa, Marcel; Irimia, Andrei; Saha, Avishek; Liu, Wei; Goh, S.Y. Matthew; Vespa, Paul M.; Van Horn, John D.; Gerig, Guido

2016-01-01

With the increasing use of efficient multimodal 3D imaging, clinicians are able to access longitudinal imaging to stage pathological diseases, to monitor the efficacy of therapeutic interventions, or to assess and quantify rehabilitation efforts. Analysis of such four-dimensional (4D) image data presenting pathologies, including disappearing and newly appearing lesions, represents a significant challenge due to the presence of complex spatio-temporal changes. Image analysis methods for such 4D image data have to include not only a concept for joint segmentation of 3D datasets to account for inherent correlations of subject-specific repeated scans but also a mechanism to account for large deformations and the destruction and formation of lesions (e.g., edema, bleeding) due to underlying physiological processes associated with damage, intervention, and recovery. In this paper, we propose a novel framework that provides a joint segmentation-registration framework to tackle the inherent problem of image registration in the presence of objects not present in all images of the time series. Our methodology models 4D changes in pathological anatomy across time and and also provides an explicit mapping of a healthy normative template to a subject’s image data with pathologies. Since atlas-moderated segmentation methods cannot explain appearance and locality pathological structures that are not represented in the template atlas, the new framework provides different options for initialization via a supervised learning approach, iterative semisupervised active learning, and also transfer learning, which results in a fully automatic 4D segmentation method. We demonstrate the effectiveness of our novel approach with synthetic experiments and a 4D multimodal MRI dataset of severe traumatic brain injury (TBI), including validation via comparison to expert segmentations. However, the proposed methodology is generic in regard to different clinical applications requiring quantitative analysis of 4D imaging representing spatio-temporal changes of pathologies. PMID:27818606

Biomechanical modelling for breast image registration

NASA Astrophysics Data System (ADS)

Lee, Angela; Rajagopal, Vijay; Chung, Jae-Hoon; Bier, Peter; Nielsen, Poul M. F.; Nash, Martyn P.

2008-03-01

Breast cancer is a leading cause of death in women. Tumours are usually detected by palpation or X-ray mammography followed by further imaging, such as magnetic resonance imaging (MRI) or ultrasound. The aim of this research is to develop a biophysically-based computational tool that will allow accurate collocation of features (such as suspicious lesions) across multiple imaging views and modalities in order to improve clinicians' diagnosis of breast cancer. We have developed a computational framework for generating individual-specific, 3D finite element models of the breast. MR images were obtained of the breast under gravity loading and neutrally buoyant conditions. Neutrally buoyant breast images, obtained whilst immersing the breast in water, were used to estimate the unloaded geometry of the breast (for present purposes, we have assumed that the densities of water and breast tissue are equal). These images were segmented to isolate the breast tissues, and a tricubic Hermite finite element mesh was fitted to the digitised data points in order to produce a customized breast model. The model was deformed, in accordance with finite deformation elasticity theory, to predict the gravity loaded state of the breast in the prone position. The unloaded breast images were embedded into the reference model and warped based on the predicted deformation. In order to analyse the accuracy of the model predictions, the cross-correlation image comparison metric was used to compare the warped, resampled images with the clinical images of the prone gravity loaded state. We believe that a biomechanical image registration tool of this kind will aid radiologists to provide more reliable diagnosis and localisation of breast cancer.

Generating patient specific pseudo-CT of the head from MR using atlas-based regression

NASA Astrophysics Data System (ADS)

Sjölund, J.; Forsberg, D.; Andersson, M.; Knutsson, H.

2015-01-01

Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation. Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT. We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use.

Idiopathic dilated cardiomyopathy: computerized anatomic study of relashionship between septal and free left ventricle wall.

PubMed

Juliani, Paulo Sérgio; Costa, Eder França da; Correia, Aristides Tadeu; Monteiro, Rosangela; Jatene, Fabio Biscegli

2014-01-01

A feature of dilated cardiomyopathy is the deformation of ventricular cavity, which contributes to systolic dysfunction. Few studies have evaluated this deformation bearing in mind ventricular regions and segments of the ventricle, which could reveal important details of the remodeling process, supporting a better understanding of its role in functional impairment and the development of new therapeutic strategies. To evaluate if, in basal, equatorial and apical regions, increased internal transverse perimeter of left ventricle in idiopathic dilated cardiomyopathy occurs proportionally between the septal and non-septal segment. We performed an anatomical study with 28 adult hearts from human cadavers. One group consisted of 18 hearts with idiopathic dilated cardiomyopathy and another group with 10 normal hearts. After lamination and left ventricle digital image capture, in three different regions (base, equator and apex), the transversal internal perimeter of left ventricle was divided into two segments: septal and not septal. These segments were measured by proper software. It was established an index of proportionality between these segments, called septal and non-septal segment index. Then we determined whether this index was the same in both groups. Among patients with normal hearts and idiopathic dilated cardiomyopathy, the index of proportionality between the two segments (septal and non-septal) showed no significant difference in the three regions analyzed. The comparison results of the indices NSS/SS among normal and enlarged hearts were respectively: in base 1.99 versus 1.86 (P=0.46), in equator 2.22 versus 2.18 (P=0.79) and in apex 2.96 versus 3.56 (P=0.11). In the idiopathic dilated cardiomyopathy, the transversal dilatation of left ventricular internal perimeter occurs proportionally between the segments corresponding to the septum and free wall at the basal, equatorial and apical regions of this chamber.

Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.

PubMed

Yan, Zhennan; Zhang, Shaoting; Tan, Chaowei; Qin, Hongxing; Belaroussi, Boubakeur; Yu, Hui Jing; Miller, Colin; Metaxas, Dimitris N

2015-04-01

Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic fat-fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to obtain fine segmentation. Fat-fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images--data from the Osteoarthritis Initiative.

PubMed

Paproki, A; Engstrom, C; Chandra, S S; Neubert, A; Fripp, J; Crozier, S

2014-09-01

To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee. We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points. The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees. Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Detection and visualization of endoleaks in CT data for monitoring of thoracic and abdominal aortic aneurysm stents

NASA Astrophysics Data System (ADS)

Lu, J.; Egger, J.; Wimmer, A.; Großkopf, S.; Freisleben, B.

2008-03-01

In this paper we present an efficient algorithm for the segmentation of the inner and outer boundary of thoratic and abdominal aortic aneurysms (TAA & AAA) in computed tomography angiography (CTA) acquisitions. The aneurysm segmentation includes two steps: first, the inner boundary is segmented based on a grey level model with two thresholds; then, an adapted active contour model approach is applied to the more complicated outer boundary segmentation, with its initialization based on the available inner boundary segmentation. An opacity image, which aims at enhancing important features while reducing spurious structures, is calculated from the CTA images and employed to guide the deformation of the model. In addition, the active contour model is extended by a constraint force that prevents intersections of the inner and outer boundary and keeps the outer boundary at a distance, given by the thrombus thickness, to the inner boundary. Based upon the segmentation results, we can measure the aneurysm size at each centerline point on the centerline orthogonal multiplanar reformatting (MPR) plane. Furthermore, a 3D TAA or AAA model is reconstructed from the set of segmented contours, and the presence of endoleaks is detected and highlighted. The implemented method has been evaluated on nine clinical CTA data sets with variations in anatomy and location of the pathology and has shown promising results.

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.

PubMed

Yang, Jinzhong; Beadle, Beth M; Garden, Adam S; Schwartz, David L; Aristophanous, Michalis

2015-09-01

To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. The median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.

Tissue Feature-Based and Segmented Deformable Image Registration for Improved Modeling of the Shear Movement of the Lungs

PubMed Central

Xie, Yaoqin; Chao, Ming; Xing, Lei

2009-01-01

Purpose To report a tissue feature-based image registration strategy with explicit inclusion of the differential motions of thoracic structures. Methods and Materials The proposed technique started with auto-identification of a number of corresponding points with distinct tissue features. The tissue feature points were found by using the scale-invariant feature transform (SIFT) method. The control point pairs were then sorted into different “colors” according to the organs they reside and used to model the involved organs individually. A thin-plate spline (TPS) method was used to register a structure characterized by the control points with a given “color”. The proposed technique was applied to study a digital phantom case, three lung and three liver cancer patients. Results For the phantom case, a comparison with the conventional TPS method showed that the registration accuracy was markedly improved when the differential motions of the lung and chest wall were taken into account. On average, the registration error and the standard deviation (SD) of the 15 points against the known ground truth are reduced from 3.0 mm to 0.5 mm and from 1.5 mm to 0.2 mm, respectively, when the new method was used. Similar level of improvement was achieved for the clinical cases. Conclusions The segmented deformable approach provides a natural and logical solution to model the discontinuous organ motions and greatly improves the accuracy and robustness of deformable registration. PMID:19545792

Development and evaluation of an articulated registration algorithm for human skeleton registration

NASA Astrophysics Data System (ADS)

Yip, Stephen; Perk, Timothy; Jeraj, Robert

2014-03-01

Accurate registration over multiple scans is necessary to assess treatment response of bone diseases (e.g. metastatic bone lesions). This study aimed to develop and evaluate an articulated registration algorithm for the whole-body skeleton registration in human patients. In articulated registration, whole-body skeletons are registered by auto-segmenting into individual bones using atlas-based segmentation, and then rigidly aligning them. Sixteen patients (weight = 80-117 kg, height = 168-191 cm) with advanced prostate cancer underwent the pre- and mid-treatment PET/CT scans over a course of cancer therapy. Skeletons were extracted from the CT images by thresholding (HU>150). Skeletons were registered using the articulated, rigid, and deformable registration algorithms to account for position and postural variability between scans. The inter-observers agreement in the atlas creation, the agreement between the manually and atlas-based segmented bones, and the registration performances of all three registration algorithms were all assessed using the Dice similarity index—DSIobserved, DSIatlas, and DSIregister. Hausdorff distance (dHausdorff) of the registered skeletons was also used for registration evaluation. Nearly negligible inter-observers variability was found in the bone atlases creation as the DSIobserver was 96 ± 2%. Atlas-based and manual segmented bones were in excellent agreement with DSIatlas of 90 ± 3%. Articulated (DSIregsiter = 75 ± 2%, dHausdorff = 0.37 ± 0.08 cm) and deformable registration algorithms (DSIregister = 77 ± 3%, dHausdorff = 0.34 ± 0.08 cm) considerably outperformed the rigid registration algorithm (DSIregsiter = 59 ± 9%, dHausdorff = 0.69 ± 0.20 cm) in the skeleton registration as the rigid registration algorithm failed to capture the skeleton flexibility in the joints. Despite superior skeleton registration performance, deformable registration algorithm failed to preserve the local rigidity of bones as over 60% of the skeletons were deformed. Articulated registration is superior to rigid and deformable registrations by capturing global flexibility while preserving local rigidity inherent in skeleton registration. Therefore, articulated registration can be employed to accurately register the whole-body human skeletons, and it enables the treatment response assessment of various bone diseases.

Soft tissue deformation for surgical simulation: a position-based dynamics approach.

PubMed

Camara, Mafalda; Mayer, Erik; Darzi, Ara; Pratt, Philip

2016-06-01

To assist the rehearsal and planning of robot-assisted partial nephrectomy, a real-time simulation platform is presented that allows surgeons to visualise and interact with rapidly constructed patient-specific biomechanical models of the anatomical regions of interest. Coupled to a framework for volumetric deformation, the platform furthermore simulates intracorporeal 2D ultrasound image acquisition, using preoperative imaging as the data source. This not only facilitates the planning of optimal transducer trajectories and viewpoints, but can also act as a validation context for manually operated freehand 3D acquisitions and reconstructions. The simulation platform was implemented within the GPU-accelerated NVIDIA FleX position-based dynamics framework. In order to validate the model and determine material properties and other simulation parameter values, a porcine kidney with embedded fiducial beads was CT-scanned and segmented. Acquisitions for the rest position and three different levels of probe-induced deformation were collected. Optimal values of the cluster stiffness coefficients were determined for a range of different particle radii, where the objective function comprised the mean distance error between real and simulated fiducial positions over the sequence of deformations. The mean fiducial error at each deformation stage was found to be compatible with the level of ultrasound probe calibration error typically observed in clinical practice. Furthermore, the simulation exhibited unconditional stability on account of its use of clustered shape-matching constraints. A novel position-based dynamics implementation of soft tissue deformation has been shown to facilitate several desirable simulation characteristics: real-time performance, unconditional stability, rapid model construction enabling patient-specific behaviour and accuracy with respect to reference CT images.

Does permanent extensional deformation in lower forearc slopes indicate shallow plate-boundary rupture?

NASA Astrophysics Data System (ADS)

Geersen, J.; Ranero, C. R.; Kopp, H.; Behrmann, J. H.; Lange, D.; Klaucke, I.; Barrientos, S.; Diaz-Naveas, J.; Barckhausen, U.; Reichert, C.

2018-05-01

Seismic rupture of the shallow plate-boundary can result in large tsunamis with tragic socio-economic consequences, as exemplified by the 2011 Tohoku-Oki earthquake. To better understand the processes involved in shallow earthquake rupture in seismic gaps (where megathrust earthquakes are expected), and investigate the tsunami hazard, it is important to assess whether the region experienced shallow earthquake rupture in the past. However, there are currently no established methods to elucidate whether a margin segment has repeatedly experienced shallow earthquake rupture, with the exception of mechanical studies on subducted fault-rocks. Here we combine new swath bathymetric data, unpublished seismic reflection images, and inter-seismic seismicity to evaluate if the pattern of permanent deformation in the marine forearc of the Northern Chile seismic gap allows inferences on past earthquake behavior. While the tectonic configuration of the middle and upper slope remains similar over hundreds of kilometers along the North Chilean margin, we document permanent extensional deformation of the lower slope localized to the region 20.8°S-22°S. Critical taper analyses, the comparison of permanent deformation to inter-seismic seismicity and plate-coupling models, as well as recent observations from other subduction-zones, including the area that ruptured during the 2011 Tohoku-Oki earthquake, suggest that the normal faults at the lower slope may have resulted from shallow, possibly near-trench breaking earthquake ruptures in the past. In the adjacent margin segments, the 1995 Antofagasta, 2007 Tocopilla, and 2014 Iquique earthquakes were limited to the middle and upper-slope and the terrestrial forearc, and so are upper-plate normal faults. Our findings suggest a seismo-tectonic segmentation of the North Chilean margin that seems to be stable over multiple earthquake cycles. If our interpretations are correct, they indicate a high tsunami hazard posed by the yet un-ruptured southern segment of the seismic gap.

Deformable Image Registration for Cone-Beam CT Guided Transoral Robotic Base of Tongue Surgery

PubMed Central

Reaungamornrat, S.; Liu, W. P.; Wang, A. S.; Otake, Y.; Nithiananthan, S.; Uneri, A.; Schafer, S.; Tryggestad, E.; Richmon, J.; Sorger, J. M.; Siewerdsen, J. H.; Taylor, R. H.

2013-01-01

Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base of tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam CT (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e., volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC), and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid, and Demons steps was 4.6, 2.1, and 1.7 mm, respectively. The respective ECC was 0.57, 0.70, and 0.73 and NPMI was 0.46, 0.57, and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base of tongue robotic surgery. PMID:23807549

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

NASA Astrophysics Data System (ADS)

Reaungamornrat, S.; Liu, W. P.; Wang, A. S.; Otake, Y.; Nithiananthan, S.; Uneri, A.; Schafer, S.; Tryggestad, E.; Richmon, J.; Sorger, J. M.; Siewerdsen, J. H.; Taylor, R. H.

2013-07-01

Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base-of-tongue robotic surgery.

Left Ventricular Endocardium Tracking by Fusion of Biomechanical and Deformable Models

PubMed Central

Gu, Jason

2014-01-01

This paper presents a framework for tracking left ventricular (LV) endocardium through 2D echocardiography image sequence. The framework is based on fusion of biomechanical (BM) model of the heart with the parametric deformable model. The BM model constitutive equation consists of passive and active strain energy functions. The deformations of the LV are obtained by solving the constitutive equations using ABAQUS FEM in each frame in the cardiac cycle. The strain energy functions are defined in two user subroutines for active and passive phases. Average fusion technique is used to fuse the BM and deformable model contours. Experimental results are conducted to verify the detected contours and the results are evaluated by comparing themto a created gold standard. The results and the evaluation proved that the framework has the tremendous potential to track and segment the LV through the whole cardiac cycle. PMID:24587814

Simultaneous skull-stripping and lateral ventricle segmentation via fast multi-atlas likelihood fusion

NASA Astrophysics Data System (ADS)

Tang, Xiaoying; Kutten, Kwame; Ceritoglu, Can; Mori, Susumu; Miller, Michael I.

2015-03-01

In this paper, we propose and validate a fully automated pipeline for simultaneous skull-stripping and lateral ventricle segmentation using T1-weighted images. The pipeline is built upon a segmentation algorithm entitled fast multi-atlas likelihood-fusion (MALF) which utilizes multiple T1 atlases that have been pre-segmented into six whole-brain labels - the gray matter, the white matter, the cerebrospinal fluid, the lateral ventricles, the skull, and the background of the entire image. This algorithm, MALF, was designed for estimating brain anatomical structures in the framework of coordinate changes via large diffeomorphisms. In the proposed pipeline, we use a variant of MALF to estimate those six whole-brain labels in the test T1-weighted image. The three tissue labels (gray matter, white matter, and cerebrospinal fluid) and the lateral ventricles are then grouped together to form a binary brain mask to which we apply morphological smoothing so as to create the final mask for brain extraction. For computational purposes, all input images to MALF are down-sampled by a factor of two. In addition, small deformations are used for the changes of coordinates. This substantially reduces the computational complexity, hence we use the term "fast MALF". The skull-stripping performance is qualitatively evaluated on a total of 486 brain scans from a longitudinal study on Alzheimer dementia. Quantitative error analysis is carried out on 36 scans for evaluating the accuracy of the pipeline in segmenting the lateral ventricle. The volumes of the automated lateral ventricle segmentations, obtained from the proposed pipeline, are compared across three different clinical groups. The ventricle volumes from our pipeline are found to be sensitive to the diagnosis.

Understanding how axial loads on the spine influence segmental biomechanics for idiopathic scoliosis patients: A magnetic resonance imaging study.

PubMed

Little, J P; Pearcy, M J; Izatt, M T; Boom, K; Labrom, R D; Askin, G N; Adam, C J

2016-02-01

Segmental biomechanics of the scoliotic spine are important since the overall spinal deformity is comprised of the cumulative coronal and axial rotations of individual joints. This study investigates the coronal plane segmental biomechanics for adolescent idiopathic scoliosis patients in response to physiologically relevant axial compression. Individual spinal joint compliance in the coronal plane was measured for a series of 15 idiopathic scoliosis patients using axially loaded magnetic resonance imaging. Each patient was first imaged in the supine position with no axial load, and then again following application of an axial compressive load. Coronal plane disc wedge angles in the unloaded and loaded configurations were measured. Joint moments exerted by the axial compressive load were used to derive estimates of individual joint compliance. The mean standing major Cobb angle for this patient series was 46°. Mean intra-observer measurement error for endplate inclination was 1.6°. Following loading, initially highly wedged discs demonstrated a smaller change in wedge angle, than less wedged discs for certain spinal levels (+2,+1,-2 relative to the apex, (p<0.05)). Highly wedged discs were observed near the apex of the curve, which corresponded to lower joint compliance in the apical region. While individual patients exhibit substantial variability in disc wedge angles and joint compliance, overall there is a pattern of increased disc wedging near the curve apex, and reduced joint compliance in this region. Approaches such as this can provide valuable biomechanical data on in vivo spinal biomechanics of the scoliotic spine, for analysis of deformity progression and surgical planning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Airway Obstruction and the Unilateral Cleft Lip and Palate Deformity: Contributions by the Bony Septum.

PubMed

Friel, Michael T; Starbuck, John M; Ghoneima, Ahmed M; Murage, Kariuki; Kula, Katherine S; Tholpady, Sunil; Havlik, Robert J; Flores, Roberto L

2015-07-01

Patients with unilateral cleft lip and palate (CLP) deformities commonly develop nasal airway obstruction, necessitating septoplasty at the time of definitive rhinoplasty. We assessed the contribution of the bony septum to airway obstruction using computed tomography (CT) and cone beam CT (CBCT). A 2-year retrospective review of all subjects with unilateral CLP who underwent CBCT imaging (n = 22) and age-matched controls (n = 9) who underwent CT imaging was conducted. Control CT scans were used to determine the segment of nasal septum comprised almost entirely of bone. The CBCT of the nasal airway was assessed using Dolphin software to determine the contribution of the bony septum to septal deviation and airway obstruction. The nasal septum posterior to the midpoint between anterior and posterior nasal spine is comprised of 96% bone. The nasal airway associated with this posterior bony segment was 43.1% (P < 0.001) larger by volume on the non-cleft side in patients with unilateral CLP. The average septal deviation within the posterior bony segment was 5.4 mm, accounting for 74.4% of the maximal deviation within the nasal airway. The average airway stenosis within the posterior bony nasal airway was 0.45 mm (0-2.2 mm). In patients with unilateral CLP, the bony nasal septum can demonstrate significant deviation and airway stenosis. Surgeons should consider a bony septoplasty in their treatment algorithm in unilateral CLP patients who have reached skeletal maturity.

A microstructural study of fault rocks from the SAFOD: Implications for the deformation mechanisms and strength of the creeping segment of the San Andreas Fault

NASA Astrophysics Data System (ADS)

Hadizadeh, Jafar; Mittempergher, Silvia; Gratier, Jean-Pierre; Renard, Francois; Di Toro, Giulio; Richard, Julie; Babaie, Hassan A.

2012-09-01

The San Andreas Fault zone in central California accommodates tectonic strain by stable slip and microseismic activity. We study microstructural controls of strength and deformation in the fault using core samples provided by the San Andreas Fault Observatory at Depth (SAFOD) including gouge corresponding to presently active shearing intervals in the main borehole. The methods of study include high-resolution optical and electron microscopy, X-ray fluorescence mapping, X-ray powder diffraction, energy dispersive X-ray spectroscopy, white light interferometry, and image processing. The fault zone at the SAFOD site consists of a strongly deformed and foliated core zone that includes 2-3 m thick active shear zones, surrounded by less deformed rocks. Results suggest deformation and foliation of the core zone outside the active shear zones by alternating cataclasis and pressure solution mechanisms. The active shear zones, considered zones of large-scale shear localization, appear to be associated with an abundance of weak phases including smectite clays, serpentinite alteration products, and amorphous material. We suggest that deformation along the active shear zones is by a granular-type flow mechanism that involves frictional sliding of microlithons along phyllosilicate-rich Riedel shear surfaces as well as stress-driven diffusive mass transfer. The microstructural data may be interpreted to suggest that deformation in the active shear zones is strongly displacement-weakening. The fault creeps because the velocity strengthening weak gouge in the active shear zones is being sheared without strong restrengthening mechanisms such as cementation or fracture sealing. Possible mechanisms for the observed microseismicity in the creeping segment of the SAF include local high fluid pressure build-ups, hard asperity development by fracture-and-seal cycles, and stress build-up due to slip zone undulations.

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

NASA Astrophysics Data System (ADS)

Nanayakkara, Nuwan D.; Samarabandu, Jagath; Fenster, Aaron

2006-04-01

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 ± 0.51 pixels (0.54 ± 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.

Plate boundary deformation at the latitude of the Salton Trough - northern Gulf of California (Invited)

NASA Astrophysics Data System (ADS)

Stock, J. M.

2013-12-01

Along the Pacific-North America plate boundary zone, the segment including the southern San Andreas fault to Salton Trough and northern Gulf of California basins has been transtensional throughout its evolution, based on Pacific-North America displacement vectors calculated from the global plate circuit (900 × 20 km at N54°W since 20 Ma; 460 × 20 km at N48°W since 11 Ma). Nevertheless, active seismicity and focal mechanisms show a broad zone of plate boundary deformation within which the inferred stress regime varies locally (Yang & Hauksson 2013 GJI), and fault patterns in some regions suggest ongoing tectonic rotation. Similar behavior is inferred to have occurred in this zone over most of its history. Crustal structure in this region is constrained by surface geology, geophysical experiments (e.g., the 2011 Salton Seismic Imaging Project (SSIP), USGS Imperial Valley 1979, PACE), and interdisciplinary marine and onland studies in Mexico (e.g., NARS-Baja, Cortes, and surveys by PEMEX). Magnetic data (e.g., EMAG-2) aids in the recognition of large-scale crustal provinces and fault boundaries in regions lacking detailed geophysical surveys. Consideration of existing constraints on crustal thickness and architecture, and fault and basin evolution suggests that to reconcile geological deformation with plate motion history, the following additional factors need to be taken into account. 1) Plate boundary displacement via interacting systems of rotating blocks, coeval with slip on steep strike slip faults, and possibly related to slip on low angle extensional faults (e.g, Axen & Fletcher 1998 IGR) may be typical prior to the onset of seafloor spreading. This fault style may have accommodated up to 150 km of plate motion in the Mexican Continental Borderland and north of the Vizcaino Peninsula, likely between 12 and 15 Ma, as well as explaining younger rotations adjacent to the Gulf of California and current deformation southwest of the Salton Sea. 2) Geophysical characteristics suggest that the zone of strike-slip faults related to past plate boundary deformation extends eastward into SW Arizona and beneath the Sonoran coastal plain. 3) 'New' crust and mantle lithosphere at the plate boundary, in the Salton Trough and the non-oceanic part of the northern Gulf of California, varies in seismic velocity structure and dimensions, both within and across extensional segments. Details of within-segment variations imaged by SSIP (e.g., Ma et al., and Han et al., this meeting) are attributed to active fault patterns and small scale variations in hydrothermal activity and magmatism superposed on a more uniform sedimentation. Differences between the Imperial Valley rift segment and the north Gulf of California segments may be due to more involvement of low angle normal faults in the marine basins in the south (Martin et al., 2013, Tectonics), as well as differences in lower crustal or mantle lithospheric flow from the adjacent continental regions.

Deformable and rigid registration of MRI and microPET images for photodynamic therapy of cancer in mice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fei Baowei; Wang Hesheng; Muzic, Raymond F. Jr.

2006-03-15

We are investigating imaging techniques to study the tumor response to photodynamic therapy (PDT). Positron emission tomography (PET) can provide physiological and functional information. High-resolution magnetic resonance imaging (MRI) can provide anatomical and morphological changes. Image registration can combine MRI and PET images for improved tumor monitoring. In this study, we acquired high-resolution MRI and microPET {sup 18}F-fluorodeoxyglucose (FDG) images from C3H mice with RIF-1 tumors that were treated with Pc 4-based PDT. We developed two registration methods for this application. For registration of the whole mouse body, we used an automatic three-dimensional, normalized mutual information algorithm. For tumor registration,more » we developed a finite element model (FEM)-based deformable registration scheme. To assess the quality of whole body registration, we performed slice-by-slice review of both image volumes; manually segmented feature organs, such as the left and right kidneys and the bladder, in each slice; and computed the distance between corresponding centroids. Over 40 volume registration experiments were performed with MRI and microPET images. The distance between corresponding centroids of organs was 1.5{+-}0.4 mm which is about 2 pixels of microPET images. The mean volume overlap ratios for tumors were 94.7% and 86.3% for the deformable and rigid registration methods, respectively. Registration of high-resolution MRI and microPET images combines anatomical and functional information of the tumors and provides a useful tool for evaluating photodynamic therapy.« less

Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

NASA Astrophysics Data System (ADS)

Yin, Yin; Fotin, Sergei V.; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter

2012-02-01

Manual delineation of the prostate is a challenging task for a clinician due to its complex and irregular shape. Furthermore, the need for precisely targeting the prostate boundary continues to grow. Planning for radiation therapy, MR-ultrasound fusion for image-guided biopsy, multi-parametric MRI tissue characterization, and context-based organ retrieval are examples where accurate prostate delineation can play a critical role in a successful patient outcome. Therefore, a robust automated full prostate segmentation system is desired. In this paper, we present an automated prostate segmentation system for 3D MR images. In this system, the prostate is segmented in two steps: the prostate displacement and size are first detected, and then the boundary is refined by a shape model. The detection approach is based on normalized gradient fields cross-correlation. This approach is fast, robust to intensity variation and provides good accuracy to initialize a prostate mean shape model. The refinement model is based on a graph-search based framework, which contains both shape and topology information during deformation. We generated the graph cost using trained classifiers and used coarse-to-fine search and region-specific classifier training. The proposed algorithm was developed using 261 training images and tested on another 290 cases. The segmentation performance using mean DSC ranging from 0.89 to 0.91 depending on the evaluation subset demonstrates state of the art performance. Running time for the system is about 20 to 40 seconds depending on image size and resolution.

Improvements in analysis techniques for segmented mirror arrays

NASA Astrophysics Data System (ADS)

Michels, Gregory J.; Genberg, Victor L.; Bisson, Gary R.

2016-08-01

The employment of actively controlled segmented mirror architectures has become increasingly common in the development of current astronomical telescopes. Optomechanical analysis of such hardware presents unique issues compared to that of monolithic mirror designs. The work presented here is a review of current capabilities and improvements in the methodology of the analysis of mechanically induced surface deformation of such systems. The recent improvements include capability to differentiate surface deformation at the array and segment level. This differentiation allowing surface deformation analysis at each individual segment level offers useful insight into the mechanical behavior of the segments that is unavailable by analysis solely at the parent array level. In addition, capability to characterize the full displacement vector deformation of collections of points allows analysis of mechanical disturbance predictions of assembly interfaces relative to other assembly interfaces. This capability, called racking analysis, allows engineers to develop designs for segment-to-segment phasing performance in assembly integration, 0g release, and thermal stability of operation. The performance predicted by racking has the advantage of being comparable to the measurements used in assembly of hardware. Approaches to all of the above issues are presented and demonstrated by example with SigFit, a commercially available tool integrating mechanical analysis with optical analysis.

Limb flexion-induced axial compression and bending in human femoropopliteal artery segments.

PubMed

Poulson, William; Kamenskiy, Alexey; Seas, Andreas; Deegan, Paul; Lomneth, Carol; MacTaggart, Jason

2018-02-01

High failure rates of femoropopliteal artery (FPA) interventions are often attributed in part to severe mechanical deformations that occur with limb movement. Axial compression and bending of the FPA likely play significant roles in FPA disease development and reconstruction failure, but these deformations are poorly characterized. The goal of this study was to quantify axial compression and bending of human FPAs that are placed in positions commonly assumed during the normal course of daily activities. Retrievable nitinol markers were deployed using a custom-made catheter system into 28 in situ FPAs of 14 human cadavers. Contrast-enhanced, thin-section computed tomography images were acquired with each limb in the standing (180 degrees), walking (110 degrees), sitting (90 degrees), and gardening (60 degrees) postures. Image segmentation and analysis allowed relative comparison of spatial locations of each intra-arterial marker to determine axial compression and bending using the arterial centerlines. Axial compression in the popliteal artery (PA) was greater than in the proximal superficial femoral artery (SFA) or the adductor hiatus (AH) segments in all postures (P = .02). Average compression in the SFA, AH, and PA ranged from 9% to 15%, 11% to 19%, and 13% to 25%, respectively. The FPA experienced significantly more acute bending in the AH and PA segments compared with the proximal SFA (P < .05) in all postures. In the walking, sitting, and gardening postures, average sphere radii in the SFA, AH, and PA ranged from 21 to 27 mm, 10 to 18 mm, and 8 to 19 mm, whereas bending angles ranged from 150 to 157 degrees, 136 to 147 degrees, and 137 to 148 degrees, respectively. The FPA experiences significant axial compression and bending during limb flexion that occur at even modest limb angles. Moreover, different segments of the FPA appear to undergo significantly different degrees of deformation. Understanding the effects of limb flexion on axial compression and bending might assist with reconstructive device selection for patients requiring peripheral arterial disease intervention and may also help guide the development of devices with improved characteristics that can better adapt to the dynamic environment of the lower extremity vasculature. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Clinical application of computer-designed polystyrene models in complex severe spinal deformities: a pilot study

PubMed Central

Mao, Keya; Xiao, Songhua; Liu, Zhengsheng; Zhang, Yonggang; Zhang, Xuesong; Wang, Zheng; Lu, Ning; Shourong, Zhu; Xifeng, Zhang; Geng, Cui; Baowei, Liu

2010-01-01

Surgical treatment of complex severe spinal deformity, involving a scoliosis Cobb angle of more than 90° and kyphosis or vertebral and rib deformity, is challenging. Preoperative two-dimensional images resulting from plain film radiography, computed tomography (CT) and magnetic resonance imaging provide limited morphometric information. Although the three-dimensional (3D) reconstruction CT with special software can view the stereo and rotate the spinal image on the screen, it cannot show the full-scale spine and cannot directly be used on the operation table. This study was conducted to investigate the application of computer-designed polystyrene models in the treatment of complex severe spinal deformity. The study involved 16 cases of complex severe spinal deformity treated in our hospital between 1 May 2004 and 31 December 2007; the mean ± SD preoperative scoliosis Cobb angle was 118° ± 27°. The CT scanning digital imaging and communication in medicine (DICOM) data sets of the affected spinal segments were collected for 3D digital reconstruction and rapid prototyping to prepare computer-designed polystyrene models, which were applied in the treatment of these cases. The computer-designed polystyrene models allowed 3D observation and measurement of the deformities directly, which helped the surgeon to perform morphological assessment and communicate with the patient and colleagues. Furthermore, the models also guided the choice and placement of pedicle screws. Moreover, the models were used to aid in virtual surgery and guide the actual surgical procedure. The mean ± SD postoperative scoliosis Cobb angle was 42° ± 32°, and no serious complications such as spinal cord or major vascular injury occurred. The use of computer-designed polystyrene models could provide more accurate morphometric information and facilitate surgical correction of complex severe spinal deformity. PMID:20213294

Tectonic Deformation Pattern along the Longmen Shan Fault Zone in Eastern Tibet: Insights from Focal Mechanisms of the Wenchuan and Lushan Earthquake Sequences, Southwestern China

NASA Astrophysics Data System (ADS)

Yi, G.; Vallage, A.; Klinger, Y.; Long, F.; Wang, S.

2017-12-01

760 ML≥3.5 aftershocks of the 2008 Wenchuan earthquake, the 2013 Lushan mainshock and its 87 ML≥3.5 aftershocks were selected to obtain focal mechanism solutions from CAP waveform inversion method (Zhu and Helmberger, 1996), along with strain rosette (Amelung and King, 1997) and Areal strain (As) (Vallage et al., 2014), we aimed to analyze the tectonic deformation pattern along the Longmen Shan (LMS) fault zone, southwestern China. The As values show that 93% compressional earthquakes for the Lushan sequence are of pure thrust for the southern segment of the LMS fault zone, while only 50% compressional and nearly 40% of strike-slip and oblique-thrust events for the Wenchuan sequence reflect the strike-slip component increase on the central-northern segment of the LMS fault zone, meaning many different faults responsible for the Wenchuan aftershock activity. The strain rosettes with purely NW-trending compressional white lobe for the entire 87 aftershocks and 4 different classes of magnitudes are very similar to that of the Lushan mainshock. We infer that the geological structures for the southern segment are of thrust faulting under NW compressional deformation. The strain rosettes exhibit self-similarity in terms of orientation and shape for all classes, reflecting that the deformation pattern of the southern segment is independent with earthquake size, and suggesting that each class is representative of the overall deformation for the southern segment. We obtained EW-oriented pure compressional strain rosette of the entire 760 aftershocks and NW-oriented white lobe with small NE-oriented black lobe of the Wenchuan mainshock, and this difference may reflect different tectonic deformation pattern during the co-seismic and post-seismic stages. The deformation segmentation along the Wenchuan coseismic surface rupture is also evidenced from the different orientation of strain rosettes, i.e., NW for the southern area, NE for the central and NNW for the northern parts. The above inferences indicate a very complicated tectonic deformation pattern related to the complex geological structure. The segment of the northern aftershock area without ruptures behaves an oblique compressional deformation.

Registration of organs with sliding interfaces and changing topologies

NASA Astrophysics Data System (ADS)

Berendsen, Floris F.; Kotte, Alexis N. T. J.; Viergever, Max A.; Pluim, Josien P. W.

2014-03-01

Smoothness and continuity assumptions on the deformation field in deformable image registration do not hold for applications where the imaged objects have sliding interfaces. Recent extensions to deformable image registration that accommodate for sliding motion of organs are limited to sliding motion along approximately planar surfaces or cannot model sliding that changes the topological configuration in case of multiple organs. We propose a new extension to free-form image registration that is not limited in this way. Our method uses a transformation model that consists of uniform B-spline transformations for each organ region separately, which is based on segmentation of one image. Since this model can create overlapping regions or gaps between regions, we introduce a penalty term that minimizes this undesired effect. The penalty term acts on the surfaces of the organ regions and is optimized simultaneously with the image similarity. To evaluate our method registrations were performed on publicly available inhale-exhale CT scans for which performances of other methods are known. Target registration errors are computed on dense landmark sets that are available with these datasets. On these data our method outperforms the other methods in terms of target registration error and, where applicable, also in terms of overlap and gap volumes. The approximation of the other methods of sliding motion along planar surfaces is reasonably well suited for the motion present in the lung data. The ability of our method to handle sliding along curved boundaries and for changing region topology configurations was demonstrated on synthetic images.

Using manual prostate contours to enhance deformable registration of endorectal MRI.

PubMed

Cheung, M R; Krishnan, K

2012-10-01

Endorectal MRI provides detailed images of the prostate anatomy and is useful for radiation treatment planning. Here we describe a Demons field-initialized B-spline deformable registration of prostate MRI. T2-weighted endorectal MRIs of five patients were used. The prostate and the tumor of each patient were manually contoured. The planning MRIs and their segmentations were simulated by warping the corresponding endorectal MRIs using thin plate spline (TPS). Deformable registration was initialized using the deformation field generated using Demons algorithm to map the deformed prostate MRI to the non-deformed one. The solution was refined with B-Spline registration. Volume overlap similarity was used to assess the accuracy of registration and to suggest a minimum margin to account for the registration errors. Initialization using Demons algorithm took about 15 min on a computer with 2.8 GHz Intel, 1.3 GB RAM. Refinement B-spline registration (200 iterations) took less than 5 min. Using the synthetic images as the ground truth, at zero margin, the average (S.D.) 98 (±0.4)% for prostate coverage was 97 (±1)% for tumor. The average (±S.D.) treatment margin required to cover the entire prostate was 1.5 (±0.2)mm. The average (± S.D.) treatment margin required to cover the tumor was 0.7 (±0.1)mm. We also demonstrated the challenges in registering an in vivo deformed MRI to an in vivo non-deformed MRI. We here present a deformable registration scheme that can overcome large deformation. This platform is expected to be useful for prostate cancer radiation treatment planning. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Myocardial oedema in acute myocarditis detected by echocardiographic 2D myocardial deformation analysis.

PubMed

Løgstrup, B B; Nielsen, J M; Kim, W Y; Poulsen, S H

2016-09-01

The clinical diagnosis of acute myocarditis is based on symptoms, electrocardiography, elevated myocardial necrosis biomarkers, and echocardiography. Often, conventional echocardiography reveals no obvious changes in global cardiac function and therefore has limited diagnostic value. Myocardial deformation imaging by echocardiography is an evolving method used to characterize quantitatively longitudinal systolic function, which may be affected in acute myocarditis. The aim of our study was to assess the utility of echocardiographic deformation imaging of the left ventricle in patients with diagnosed acute myocarditis in whom cardiovascular magnetic resonance (CMR) evaluation was performed. We included 28 consecutive patients (mean age 32 ± 13 years) with CMR-verified diagnosis of acute myocarditis according to the Lake Louise criteria. Cardiac function was evaluated by a comprehensive assessment of left ventricular (LV) function, including 2D speckle-tracking echocardiography. We found no significant correlation between the peak values of cardiac enzymes and the amount of myocardial oedema assessed by CMR (troponin: r= 0.3; P = 0.05 and CK-MB: r = 0.1; P = 0.3). We found a larger amount of myocardial oedema in the basal part of the left ventricle [American Heart Association (AHA) segments 1-6] in inferolateral and inferior segments, compared with the anterior, anterolateral, anteroseptal, and inferoseptal segments. In the mid LV segments (AHA segments 7-12), this was more pronounced in the anterior, anterolateral, and inferolateral segments. Among conventional echocardiographic parameters, LV function was not found to correlate with the amount of myocardial oedema of the left ventricle. In contrast, we found the wall motion score index to be significantly correlated with the amount of myocardial oedema, but this correlation was only present in patients with an extensive amount of oedema (>11% of the total left ventricle). Global longitudinal systolic myocardial strain correlated significantly with the amount of oedema (r = 0.65; P < 0.001). We found that both the epicardial longitudinal and the endocardial longitudinal systolic strains were significantly correlated with oedema (r = 0.55; P = 0.003 and r = 0.54; P < 0.001). In patients with acute myocarditis, 2D speckle-tracking echocardiography was a useful tool in the diagnostic process of acute myocarditis. Global longitudinal strain adds important information that can support clinical and conventional echocardiographic evaluation, especially in patients with preserved LV ejection fraction in relation to the diagnosis and degree of myocardial dysfunction. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.

Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting.

PubMed

Gao, Shan; van 't Klooster, Ronald; Kitslaar, Pieter H; Coolen, Bram F; van den Berg, Alexandra M; Smits, Loek P; Shahzad, Rahil; Shamonin, Denis P; de Koning, Patrick J H; Nederveen, Aart J; van der Geest, Rob J

2017-10-01

The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI. The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis. For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections. The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers. © 2017 American Association of Physicists in Medicine.

Current deformation in Central Afar and triple junction kinematics deduced from GPS and InSAR measurements

NASA Astrophysics Data System (ADS)

Doubre, Cécile; Déprez, Aline; Masson, Frédéric; Socquet, Anne; Lewi, Elias; Grandin, Raphaël; Nercessian, Alexandre; Ulrich, Patrice; De Chabalier, Jean-Bernard; Saad, Ibrahim; Abayazid, Ahmadine; Peltzer, Gilles; Delorme, Arthur; Calais, Eric; Wright, Tim

2017-02-01

Kinematics of divergent boundaries and Rift-Rift-Rift junctions are classically studied using long-term geodetic observations. Since significant magma-related displacements are expected, short-term deformation provides important constraints on the crustal mechanisms involved both in active rifting and in transfer of extensional deformation between spreading axes. Using InSAR and GPS data, we analyse the surface deformation in the whole Central Afar region in detail, focusing on both the extensional deformation across the Quaternary magmato-tectonic rift segments, and on the zones of deformation transfer between active segments and spreading axes. The largest deformation occurs across the two recently activated Asal-Ghoubbet (AG) and Manda Hararo-Dabbahu (MH-D) magmato-tectonic segments with very high strain rates, whereas the other Quaternary active segments do not concentrate any large strain, suggesting that these rifts are either sealed during interdyking periods or not mature enough to remain a plate boundary. Outside of these segments, the GPS horizontal velocity field shows a regular gradient following a clockwise rotation of the displacements from the Southeast to the East of Afar, with respect to Nubia. Very few shallow creeping structures can be identified as well in the InSAR data. However, using these data together with the strain rate tensor and the rotations rates deduced from GPS baselines, the present-day strain field over Central Afar is consistent with the main tectonic structures, and therefore with the long-term deformation. We investigate the current kinematics of the triple junction included in our GPS data set by building simple block models. The deformation in Central Afar can be described by adding a central microblock evolving separately from the three surrounding plates. In this model, the northern block boundary corresponds to a deep EW-trending trans-tensional dislocation, locked from the surface to 10-13 km and joining at depth the active spreading axes of the Red Sea and the Aden Ridge, from AG to MH-D rift segments. Over the long-term, this plate configuration could explain the presence of the en-échelon magmatic basins and subrifts. However, the transient behaviour of the spreading axes implies that the deformation in Central Afar evolves depending on the availability of magma supply within the well-established segments.

SU-E-J-224: Multimodality Segmentation of Head and Neck Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aristophanous, M; Yang, J; Beadle, B

2014-06-01

Purpose: Develop an algorithm that is able to automatically segment tumor volume in Head and Neck cancer by integrating information from CT, PET and MR imaging simultaneously. Methods: Twenty three patients that were recruited under an adaptive radiotherapy protocol had MR, CT and PET/CT scans within 2 months prior to start of radiotherapy. The patients had unresectable disease and were treated either with chemoradiotherapy or radiation therapy alone. Using the Velocity software, the PET/CT and MR (T1 weighted+contrast) scans were registered to the planning CT using deformable and rigid registration respectively. The PET and MR images were then resampled accordingmore » to the registration to match the planning CT. The resampled images, together with the planning CT, were fed into a multi-channel segmentation algorithm, which is based on Gaussian mixture models and solved with the expectation-maximization algorithm and Markov random fields. A rectangular region of interest (ROI) was manually placed to identify the tumor area and facilitate the segmentation process. The auto-segmented tumor contours were compared with the gross tumor volume (GTV) manually defined by the physician. The volume difference and Dice similarity coefficient (DSC) between the manual and autosegmented GTV contours were calculated as the quantitative evaluation metrics. Results: The multimodality segmentation algorithm was applied to all 23 patients. The volumes of the auto-segmented GTV ranged from 18.4cc to 32.8cc. The average (range) volume difference between the manual and auto-segmented GTV was −42% (−32.8%–63.8%). The average DSC value was 0.62, ranging from 0.39 to 0.78. Conclusion: An algorithm for the automated definition of tumor volume using multiple imaging modalities simultaneously was successfully developed and implemented for Head and Neck cancer. This development along with more accurate registration algorithms can aid physicians in the efforts to interpret the multitude of imaging information available in radiotherapy today. This project was supported by a grant by Varian Medical Systems.« less

Optimal Multiple Surface Segmentation With Shape and Context Priors

PubMed Central

Bai, Junjie; Garvin, Mona K.; Sonka, Milan; Buatti, John M.; Wu, Xiaodong

2014-01-01

Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 ± 1.58 μm) was improved to 5.14 ± 0.99 μm when employing our new method with shape and context priors. PMID:23193309

Automatic and quantitative measurement of collagen gel contraction using model-guided segmentation

NASA Astrophysics Data System (ADS)

Chen, Hsin-Chen; Yang, Tai-Hua; Thoreson, Andrew R.; Zhao, Chunfeng; Amadio, Peter C.; Sun, Yung-Nien; Su, Fong-Chin; An, Kai-Nan

2013-08-01

Quantitative measurement of collagen gel contraction plays a critical role in the field of tissue engineering because it provides spatial-temporal assessment (e.g., changes of gel area and diameter during the contraction process) reflecting the cell behavior and tissue material properties. So far the assessment of collagen gels relies on manual segmentation, which is time-consuming and suffers from serious intra- and inter-observer variability. In this study, we propose an automatic method combining various image processing techniques to resolve these problems. The proposed method first detects the maximal feasible contraction range of circular references (e.g., culture dish) and avoids the interference of irrelevant objects in the given image. Then, a three-step color conversion strategy is applied to normalize and enhance the contrast between the gel and background. We subsequently introduce a deformable circular model which utilizes regional intensity contrast and circular shape constraint to locate the gel boundary. An adaptive weighting scheme was employed to coordinate the model behavior, so that the proposed system can overcome variations of gel boundary appearances at different contraction stages. Two measurements of collagen gels (i.e., area and diameter) can readily be obtained based on the segmentation results. Experimental results, including 120 gel images for accuracy validation, showed high agreement between the proposed method and manual segmentation with an average dice similarity coefficient larger than 0.95. The results also demonstrated obvious improvement in gel contours obtained by the proposed method over two popular, generic segmentation methods.

Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schoot, A. J. A. J. van de, E-mail: a.j.schootvande@amc.uva.nl; Schooneveldt, G.; Wognum, S.

Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used tomore » guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation results significantly (p < 0.01) based on DSC (6.72%) and SD of contour-to-contour distances (0.08 cm) and decreased the 95% confidence intervals of the bladder volume differences. Moreover, expanding the shape model improved the segmentation results significantly (p < 0.01) based on DSC and SD of contour-to-contour distances. Conclusions: This patient-specific shape model based automatic bladder segmentation method on CBCT is accurate and generic. Our segmentation method only needs two pretreatment imaging data sets as prior knowledge, is independent of patient gender and patient treatment position and has the possibility to manually adapt the segmentation locally.« less

Deformable registration of x-ray to MRI for post-implant dosimetry in prostate brachytherapy

NASA Astrophysics Data System (ADS)

Park, Seyoun; Song, Danny Y.; Lee, Junghoon

2016-03-01

Post-implant dosimetric assessment in prostate brachytherapy is typically performed using CT as the standard imaging modality. However, poor soft tissue contrast in CT causes significant variability in target contouring, resulting in incorrect dose calculations for organs of interest. CT-MR fusion-based approach has been advocated taking advantage of the complementary capabilities of CT (seed identification) and MRI (soft tissue visibility), and has proved to provide more accurate dosimetry calculations. However, seed segmentation in CT requires manual review, and the accuracy is limited by the reconstructed voxel resolution. In addition, CT deposits considerable amount of radiation to the patient. In this paper, we propose an X-ray and MRI based post-implant dosimetry approach. Implanted seeds are localized using three X-ray images by solving a combinatorial optimization problem, and the identified seeds are registered to MR images by an intensity-based points-to-volume registration. We pre-process the MR images using geometric and Gaussian filtering. To accommodate potential soft tissue deformation, our registration is performed in two steps, an initial affine transformation and local deformable registration. An evolutionary optimizer in conjunction with a points-to-volume similarity metric is used for the affine registration. Local prostate deformation and seed migration are then adjusted by the deformable registration step with external and internal force constraints. We tested our algorithm on six patient data sets, achieving registration error of (1.2+/-0.8) mm in < 30 sec. Our proposed approach has the potential to be a fast and cost-effective solution for post-implant dosimetry with equivalent accuracy as the CT-MR fusion-based approach.

Kidney segmentation in CT sequences using graph cuts based active contours model and contextual continuity.

PubMed

Zhang, Pin; Liang, Yanmei; Chang, Shengjiang; Fan, Hailun

2013-08-01

Accurate segmentation of renal tissues in abdominal computed tomography (CT) image sequences is an indispensable step for computer-aided diagnosis and pathology detection in clinical applications. In this study, the goal is to develop a radiology tool to extract renal tissues in CT sequences for the management of renal diagnosis and treatments. In this paper, the authors propose a new graph-cuts-based active contours model with an adaptive width of narrow band for kidney extraction in CT image sequences. Based on graph cuts and contextual continuity, the segmentation is carried out slice-by-slice. In the first stage, the middle two adjacent slices in a CT sequence are segmented interactively based on the graph cuts approach. Subsequently, the deformable contour evolves toward the renal boundaries by the proposed model for the kidney extraction of the remaining slices. In this model, the energy function combining boundary with regional information is optimized in the constructed graph and the adaptive search range is determined by contextual continuity and the object size. In addition, in order to reduce the complexity of the min-cut computation, the nodes in the graph only have n-links for fewer edges. The total 30 CT images sequences with normal and pathological renal tissues are used to evaluate the accuracy and effectiveness of our method. The experimental results reveal that the average dice similarity coefficient of these image sequences is from 92.37% to 95.71% and the corresponding standard deviation for each dataset is from 2.18% to 3.87%. In addition, the average automatic segmentation time for one kidney in each slice is about 0.36 s. Integrating the graph-cuts-based active contours model with contextual continuity, the algorithm takes advantages of energy minimization and the characteristics of image sequences. The proposed method achieves effective results for kidney segmentation in CT sequences.

Poster - 32: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mallawi, Abrar; Farrell, TomTom; Diamond, Kevin-Ro

2016-08-15

Atlas based-segmentation has recently been evaluated for use in prostate radiotherapy. In a typical approach, the essential step is the selection of an atlas from a database that the best matches of the target image. This work proposes an atlas selection strategy and evaluate it impacts on final segmentation accuracy. Several anatomical parameters were measured to indicate the overall prostate and body shape, all of these measurements obtained on CT images. A brute force procedure was first performed for a training dataset of 20 patients using image registration to pair subject with similar contours; each subject was served as amore » target image to which all reaming 19 images were affinity registered. The overlap between the prostate and femoral heads was quantified for each pair using the Dice Similarity Coefficient (DSC). Finally, an atlas selection procedure was designed; relying on the computation of a similarity score defined as a weighted sum of differences between the target and atlas subject anatomical measurement. The algorithm ability to predict the most similar atlas was excellent, achieving mean DSCs of 0.78 ± 0.07 and 0.90 ± 0.02 for the CTV and either femoral head. The proposed atlas selection yielded 0.72 ± 0.11 and 0.87 ± 0.03 for CTV and either femoral head. The DSC obtained with the proposed selection method were slightly lower than the maximum established using brute force, but this does not include potential improvements expected with deformable registration. The proposed atlas selection method provides reasonable segmentation accuracy.« less

Analysis of role of bone compliance on mechanics of a lumbar motion segment.

PubMed

Shirazi-Adl, A

1994-11-01

A large deformation elasto-static finite element formulation is developed and used for the determination of the role of bone compliance in mechanics of a lumbar motion segment. This is done by simulating each vertebra as a deformable body with realistic material properties, as a deformable body with stiffer or softer mechanical properties, as a single rigid body, or finally as two rigid bodies attached by deformable beams. The single loadings of axial compression, flexion moment, extension moment, and axial torque are considered. The results indicate the marked effect of alteration in bone material properties on biomechanics of lumbar segments specially under larger loads. The biomechanical studies of the lumbar spine should, therefore, be performed and evaluated in the light of such dependency. A model for bony vertebrae is finally proposed that preserves both the accuracy and the cost-efficiency in nonlinear finite element analyses of spinal multi-motion segment systems.

Computer aided diagnosis of diabetic peripheral neuropathy

NASA Astrophysics Data System (ADS)

Chekh, Viktor; Soliz, Peter; McGrew, Elizabeth; Barriga, Simon; Burge, Mark; Luan, Shuang

2014-03-01

Diabetic peripheral neuropathy (DPN) refers to the nerve damage that can occur in diabetes patients. It most often affects the extremities, such as the feet, and can lead to peripheral vascular disease, deformity, infection, ulceration, and even amputation. The key to managing diabetic foot is prevention and early detection. Unfortunately, current existing diagnostic techniques are mostly based on patient sensations and exhibit significant inter- and intra-observer differences. We have developed a computer aided diagnostic (CAD) system for diabetic peripheral neuropathy. The thermal response of the feet of diabetic patients following cold stimulus is captured using an infrared camera. The plantar foot in the images from a thermal video are segmented and registered for tracking points or specific regions. The temperature recovery of each point on the plantar foot is extracted using our bio-thermal model and analyzed. The regions that exhibit abnormal ability to recover are automatically identified to aid the physicians to recognize problematic areas. The key to our CAD system is the segmentation of infrared video. The main challenges for segmenting infrared video compared to normal digital video are (1) as the foot warms up, it also warms up the surrounding, creating an ever changing contrast; and (2) there may be significant motion during imaging. To overcome this, a hybrid segmentation algorithm was developed based on a number of techniques such as continuous max-flow, model based segmentation, shape preservation, convex hull, and temperature normalization. Verifications of the automatic segmentation and registration using manual segmentation and markers show good agreement.

Wavefront sensorless adaptive optics optical coherence tomography for in vivo retinal imaging in mice

PubMed Central

Jian, Yifan; Xu, Jing; Gradowski, Martin A.; Bonora, Stefano; Zawadzki, Robert J.; Sarunic, Marinko V.

2014-01-01

We present wavefront sensorless adaptive optics (WSAO) Fourier domain optical coherence tomography (FD-OCT) for in vivo small animal retinal imaging. WSAO is attractive especially for mouse retinal imaging because it simplifies optical design and eliminates the need for wavefront sensing, which is difficult in the small animal eye. GPU accelerated processing of the OCT data permitted real-time extraction of image quality metrics (intensity) for arbitrarily selected retinal layers to be optimized. Modal control of a commercially available segmented deformable mirror (IrisAO Inc.) provided rapid convergence using a sequential search algorithm. Image quality improvements with WSAO OCT are presented for both pigmented and albino mouse retinal data, acquired in vivo. PMID:24575347

Computational biomechanics to simulate the femoropopliteal intersection during knee flexion: a preliminary study.

PubMed

Diehm, Nicolas; Sin, Sangmun; Hoppe, Hanno; Baumgartner, Iris; Büchler, Philippe

2011-06-01

To assess if finite element (FE) models can be used to predict deformation of the femoropopliteal segment during knee flexion. Magnetic resonance angiography (MRA) images were acquired on the lower limbs of 8 healthy volunteers (5 men; mean age 28 ± 4 years). Images were taken in 2 natural positions, with the lower limb fully extended and with the knee bent at ~ 40°. Patient-specific FE models were developed and used to simulate the experimental situation. The displacements of the artery during knee bending as predicted by the numerical model were compared to the corresponding positions measured on the MRA images. The numerical predictions showed a good overall agreement between the calculated displacements of the motion measures from MRA images. The average position error comparing the calculated vs. actual displacements of the femoropopliteal intersection measured on the MRA was 8 ± 4 mm. Two of the 8 subjects showed large prediction errors (average 13 ± 5 mm); these 2 volunteers were the tallest subjects involved in the study and had a low body mass index (20.5 kg/m²). The present computational model is able to capture the gross mechanical environment of the femoropopliteal intersection during knee bending and provide a better understanding of the complex biomechanical behavior. However, results suggest that patient-specific mechanical properties and detailed muscle modeling are required to provide accurate patient-specific numerical predictions of arterial displacement. Further adaptation of this model is expected to provide an improved ability to predict the multiaxial deformation of this arterial segment during leg movements and to optimize future stent designs.

Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation.

PubMed

Ben Ayed, Ismail; Punithakumar, Kumaradevan; Garvin, Gregory; Romano, Walter; Li, Shuo

2011-01-01

This study investigates novel object-interaction priors for graph cut image segmentation with application to intervertebral disc delineation in magnetic resonance (MR) lumbar spine images. The algorithm optimizes an original cost function which constrains the solution with learned prior knowledge about the geometric interactions between different objects in the image. Based on a global measure of similarity between distributions, the proposed priors are intrinsically invariant with respect to translation and rotation. We further introduce a scale variable from which we derive an original fixed-point equation (FPE), thereby achieving scale-invariance with only few fast computations. The proposed priors relax the need of costly pose estimation (or registration) procedures and large training sets (we used a single subject for training), and can tolerate shape deformations, unlike template-based priors. Our formulation leads to an NP-hard problem which does not afford a form directly amenable to graph cut optimization. We proceeded to a relaxation of the problem via an auxiliary function, thereby obtaining a nearly real-time solution with few graph cuts. Quantitative evaluations over 60 intervertebral discs acquired from 10 subjects demonstrated that the proposed algorithm yields a high correlation with independent manual segmentations by an expert. We further demonstrate experimentally the invariance of the proposed geometric attributes. This supports the fact that a single subject is sufficient for training our algorithm, and confirms the relevance of the proposed priors to disc segmentation.

A two-stage method for microcalcification cluster segmentation in mammography by deformable models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.

Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods aremore » applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST{sub cluster}, average of minimum distance—AMINDIST{sub cluster}) and the area overlap measure (AOM{sub cluster}). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing tenfold cross-validation methodology. A previously developed B-spline active rays segmentation method was also considered for comparison purposes. Results: Interobserver and intraobserver segmentation agreements (median and [25%, 75%] quartile range) were substantial with respect to the distance metrics HDIST{sub cluster} (2.3 [1.8, 2.9] and 2.5 [2.1, 3.2] pixels) and AMINDIST{sub cluster} (0.8 [0.6, 1.0] and 1.0 [0.8, 1.2] pixels), while moderate with respect to AOM{sub cluster} (0.64 [0.55, 0.71] and 0.59 [0.52, 0.66]). The proposed segmentation method outperformed (0.80 ± 0.04) statistically significantly (Mann-Whitney U-test, p < 0.05) the B-spline active rays segmentation method (0.69 ± 0.04), suggesting the significance of the proposed semiautomated method. Conclusions: Results indicate a reliable semiautomated segmentation method for MC clusters offered by deformable models, which could be utilized in MC cluster quantitative image analysis.« less

Fluid flow simulation and permeability computation in deformed porous carbonate grainstones

NASA Astrophysics Data System (ADS)

Zambrano, Miller; Tondi, Emanuele; Mancini, Lucia; Lanzafame, Gabriele; Trias, F. Xavier; Arzilli, Fabio; Materazzi, Marco; Torrieri, Stefano

2018-05-01

In deformed porous carbonates, the architecture of the pore network may be modified by deformation or diagenetic processes altering the permeability with respect to the pristine rock. The effects of the pore texture and morphology on permeability in porous rocks have been widely investigated due to the importance during the evaluation of geofluid reservoirs. In this study, these effects are assessed by combining synchrotron X-ray computed microtomography (SR micro-CT) and computational fluid dynamics. The studied samples pertain to deformed porous carbonate grainstones highly affected by deformation bands (DBs) exposed in Northwestern Sicily and Abruzzo regions, Italy. The high-resolution SR micro-CT images of the samples, acquired at the SYRMEP beamline of the Elettra - Sincrotrone Trieste laboratory (Italy), were used for simulating a pressure-driven flow by using the lattice-Boltzmann method (LBM). For the experiments, a multiple relaxation time (MRT) model with the D3Q19 scheme was used to avoid viscosity-dependent results of permeability. The permeability was calculated using Darcy's law once steady conditions were reached. After the simulations, the pore-network properties (effective porosity, specific surface area, and geometrical tortuosity) were calculated using 3D images of the velocity fields. These images were segmented considering a velocity threshold value higher than zero. The study showed that DBs may generate significant heterogeneity and anisotropy of the permeability of the evaluated rock samples. Cataclasis and cementation process taking place within the DBs reduce the effective porosity and therefore the permeability. Contrary to this, pressure dissolution and faulting may generate connected channels which contribute to the permeability only parallel to the DB.

In vivo validation of cardiac output assessment in non-standard 3D echocardiographic images

NASA Astrophysics Data System (ADS)

Nillesen, M. M.; Lopata, R. G. P.; de Boode, W. P.; Gerrits, I. H.; Huisman, H. J.; Thijssen, J. M.; Kapusta, L.; de Korte, C. L.

2009-04-01

Automatic segmentation of the endocardial surface in three-dimensional (3D) echocardiographic images is an important tool to assess left ventricular (LV) geometry and cardiac output (CO). The presence of speckle noise as well as the nonisotropic characteristics of the myocardium impose strong demands on the segmentation algorithm. In the analysis of normal heart geometries of standardized (apical) views, it is advantageous to incorporate a priori knowledge about the shape and appearance of the heart. In contrast, when analyzing abnormal heart geometries, for example in children with congenital malformations, this a priori knowledge about the shape and anatomy of the LV might induce erroneous segmentation results. This study describes a fully automated segmentation method for the analysis of non-standard echocardiographic images, without making strong assumptions on the shape and appearance of the heart. The method was validated in vivo in a piglet model. Real-time 3D echocardiographic image sequences of five piglets were acquired in radiofrequency (rf) format. These ECG-gated full volume images were acquired intra-operatively in a non-standard view. Cardiac blood flow was measured simultaneously by an ultrasound transit time flow probe positioned around the common pulmonary artery. Three-dimensional adaptive filtering using the characteristics of speckle was performed on the demodulated rf data to reduce the influence of speckle noise and to optimize the distinction between blood and myocardium. A gradient-based 3D deformable simplex mesh was then used to segment the endocardial surface. A gradient and a speed force were included as external forces of the model. To balance data fitting and mesh regularity, one fixed set of weighting parameters of internal, gradient and speed forces was used for all data sets. End-diastolic and end-systolic volumes were computed from the segmented endocardial surface. The cardiac output derived from this automatic segmentation was validated quantitatively by comparing it with the CO values measured from the volume flow in the pulmonary artery. Relative bias varied between 0 and -17%, where the nominal accuracy of the flow meter is in the order of 10%. Assuming the CO measurements from the flow probe as a gold standard, excellent correlation (r = 0.99) was observed with the CO estimates obtained from image segmentation.

A segmentation editing framework based on shape change statistics

NASA Astrophysics Data System (ADS)

Mostapha, Mahmoud; Vicory, Jared; Styner, Martin; Pizer, Stephen

2017-02-01

Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user.

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Jinzhong; Aristophanous, Michalis, E-mail: MAristophanous@mdanderson.org; Beadle, Beth M.

2015-09-15

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to themore » planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55–0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.« less

Improvement in the medial meniscus posterior shift following anterior cruciate ligament reconstruction.

PubMed

Inoue, Hiroto; Furumatsu, Takayuki; Miyazawa, Shinichi; Fujii, Masataka; Kodama, Yuya; Ozaki, Toshifumi

2018-02-01

Anterior cruciate ligament (ACL) reconstruction can reduce the risk of developing osteoarthritic knees. The goals of ACL reconstruction are to restore knee stability and reduce post-traumatic meniscal tears and cartilage degradation. A chronic ACL insufficiency frequently results in medial meniscus (MM) injury at the posterior segment. How ACL reconstruction can reduce the deformation of the MM posterior segment remains unclear. In this study, we evaluated the form of the MM posterior segment and anterior tibial translation before and after ACL reconstruction using open magnetic resonance imaging (MRI). Seventeen patients who underwent ACL reconstructions without MM injuries were included in this study. MM deformation was evaluated using open MRI before surgery and 3 months after surgery. We measured medial meniscal length (MML), medial meniscal height (MMH), medial meniscal posterior body width (MPBW), MM-femoral condyle contact width (M-FCW) and posterior tibiofemoral distance (PTFD) at knee flexion angles of 10° and 90°. There were no significant pre- and postoperative differences during a flexion angle of 10°. At a flexion angle of 90°, MML decreased from 43.7 ± 4.5 to 41.4 ± 4.5 mm (P < 0.001), MMH from 7.5 ± 1.4 to 6.9 ± 1.4 mm (P = 0.006), MPBW from 13.1 ± 2.0 to 12.2 ± 1.9 mm (P < 0.001) and M-FCW from 10.0 ± 1.5 to 8.5 ± 1.5 mm (P < 0.001) after ACL reconstruction. The PTFD increased from 2.1 ± 2.8 to 2.7 ± 2.4 mm after ACL reconstruction (P = 0.015). ACL reconstruction affects the contact pattern between the MM posterior segment and medial femoral condyle and can reduce the deformation of the MM posterior segment in the knee-flexed position by reducing abnormal anterior tibial translation. It possibly prevents secondary injury to the MM posterior segment and cartilage that progresses to knee osteoarthritis. IV.

Robust visual object tracking with interleaved segmentation

NASA Astrophysics Data System (ADS)

Abel, Peter; Kieritz, Hilke; Becker, Stefan; Arens, Michael

2017-10-01

In this paper we present a new approach for tracking non-rigid, deformable objects by means of merging an on-line boosting-based tracker and a fast foreground background segmentation. We extend an on-line boosting- based tracker, which uses axes-aligned bounding boxes with fixed aspect-ratio as tracking states. By constructing a confidence map from the on-line boosting-based tracker and unifying this map with a confidence map, which is obtained from a foreground background segmentation algorithm, we build a superior confidence map. For constructing a rough confidence map of a new frame based on on-line boosting, we employ the responses of the strong classifier as well as the single weak classifier responses that were built before during the updating step. This confidence map provides a rough estimation of the object's position and dimension. In order to refine this confidence map, we build a fine, pixel-wisely segmented confidence map and merge both maps together. Our segmentation method is color-histogram-based and provides a fine and fast image segmentation. By means of back-projection and the Bayes' rule, we obtain a confidence value for every pixel. The rough and the fine confidence maps are merged together by building an adaptively weighted sum of both maps. The weights are obtained by utilizing the variances of both confidence maps. Further, we apply morphological operators in the merged confidence map in order to reduce the noise. In the resulting map we estimate the object localization and dimension via continuous adaptive mean shift. Our approach provides a rotated rectangle as tracking states, which enables a more precise description of non-rigid, deformable objects than axes-aligned bounding boxes. We evaluate our tracker on the visual object tracking (VOT) benchmark dataset 2016.

Near real-time skin deformation mapping

NASA Astrophysics Data System (ADS)

Kacenjar, Steve; Chen, Suzie; Jafri, Madiha; Wall, Brian; Pedersen, Richard; Bezozo, Richard

2013-02-01

A novel in vivo approach is described that provides large area mapping of the mechanical properties of the skin in human patients. Such information is important in the understanding of skin health, cosmetic surgery[1], aging, and impacts of sun exposure. Currently, several methods have been developed to estimate the local biomechanical properties of the skin, including the use of a physical biopsy of local areas of the skin (in vitro methods) [2, 3, and 4], and also the use of non-invasive methods (in vivo) [5, 6, and 7]. All such methods examine localized areas of the skin. Our approach examines the local elastic properties via the generation of field displacement maps of the skin created using time-sequence imaging [9] with 2D digital imaging correlation (DIC) [10]. In this approach, large areas of the skin are reviewed rapidly, and skin displacement maps are generated showing the contour maps of skin deformation. These maps are then used to precisely register skin images for purposes of diagnostic comparison. This paper reports on our mapping and registration approach, and demonstrates its ability to accurately measure the skin deformation through a described nulling interpolation process. The result of local translational DIC alignment is compared using this interpolation process. The effectiveness of the approach is reported in terms of residual RMS, image entropy measures, and differential segmented regional errors.

Biomechanical deformable image registration of longitudinal lung CT images using vessel information

NASA Astrophysics Data System (ADS)

Cazoulat, Guillaume; Owen, Dawn; Matuszak, Martha M.; Balter, James M.; Brock, Kristy K.

2016-07-01

Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: an initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix’s eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute target registration errors (TRE). The TRE was: 5.8+/- 2.9 , 3.4+/- 2.3 and 1.6+/- 1.3 mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable future plan adaptation strategies.

Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy.

PubMed

Zikmund, T; Kvasnica, L; Týč, M; Křížová, A; Colláková, J; Chmelík, R

2014-11-01

Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The study of the cell is based on extraction of the dynamic data on cell behaviour from the time-lapse sequence of the phase images. However, the phase images are affected by the phase aberrations that make the analysis particularly difficult. This is because the phase deformation is prone to change during long-term experiments. Here, we present a novel algorithm for sequential processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least-squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. All these procedures are performed automatically and applied immediately after obtaining every single phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment by playback of the recorded sequence up to actual time. Such operator's intervention is a forerunner of process automation derived from image analysis. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Automated segmentation of thyroid gland on CT images with multi-atlas label fusion and random classification forest

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Chang, Kevin; Kim, Lauren; Turkbey, Evrim; Lu, Le; Yao, Jianhua; Summers, Ronald

2015-03-01

The thyroid gland plays an important role in clinical practice, especially for radiation therapy treatment planning. For patients with head and neck cancer, radiation therapy requires a precise delineation of the thyroid gland to be spared on the pre-treatment planning CT images to avoid thyroid dysfunction. In the current clinical workflow, the thyroid gland is normally manually delineated by radiologists or radiation oncologists, which is time consuming and error prone. Therefore, a system for automated segmentation of the thyroid is desirable. However, automated segmentation of the thyroid is challenging because the thyroid is inhomogeneous and surrounded by structures that have similar intensities. In this work, the thyroid gland segmentation is initially estimated by multi-atlas label fusion algorithm. The segmentation is refined by supervised statistical learning based voxel labeling with a random forest algorithm. Multiatlas label fusion (MALF) transfers expert-labeled thyroids from atlases to a target image using deformable registration. Errors produced by label transfer are reduced by label fusion that combines the results produced by all atlases into a consensus solution. Then, random forest (RF) employs an ensemble of decision trees that are trained on labeled thyroids to recognize features. The trained forest classifier is then applied to the thyroid estimated from the MALF by voxel scanning to assign the class-conditional probability. Voxels from the expert-labeled thyroids in CT volumes are treated as positive classes; background non-thyroid voxels as negatives. We applied this automated thyroid segmentation system to CT scans of 20 patients. The results showed that the MALF achieved an overall 0.75 Dice Similarity Coefficient (DSC) and the RF classification further improved the DSC to 0.81.

A Nonrigid Kernel-Based Framework for 2D-3D Pose Estimation and 2D Image Segmentation

PubMed Central

Sandhu, Romeil; Dambreville, Samuel; Yezzi, Anthony; Tannenbaum, Allen

2013-01-01

In this work, we present a nonrigid approach to jointly solving the tasks of 2D-3D pose estimation and 2D image segmentation. In general, most frameworks that couple both pose estimation and segmentation assume that one has exact knowledge of the 3D object. However, under nonideal conditions, this assumption may be violated if only a general class to which a given shape belongs is given (e.g., cars, boats, or planes). Thus, we propose to solve the 2D-3D pose estimation and 2D image segmentation via nonlinear manifold learning of 3D embedded shapes for a general class of objects or deformations for which one may not be able to associate a skeleton model. Thus, the novelty of our method is threefold: First, we present and derive a gradient flow for the task of nonrigid pose estimation and segmentation. Second, due to the possible nonlinear structures of one’s training set, we evolve the preimage obtained through kernel PCA for the task of shape analysis. Third, we show that the derivation for shape weights is general. This allows us to use various kernels, as well as other statistical learning methodologies, with only minimal changes needing to be made to the overall shape evolution scheme. In contrast with other techniques, we approach the nonrigid problem, which is an infinite-dimensional task, with a finite-dimensional optimization scheme. More importantly, we do not explicitly need to know the interaction between various shapes such as that needed for skeleton models as this is done implicitly through shape learning. We provide experimental results on several challenging pose estimation and segmentation scenarios. PMID:20733218

Idiopathic dilated cardiomyopathy: computerized anatomic study of relashionship between septal and free left ventricle wall

PubMed Central

Juliani, Paulo Sérgio; da Costa, Éder França; Correia, Aristides Tadeu; Monteiro, Rosangela; Jatene, Fabio Biscegli

2014-01-01

Introduction A feature of dilated cardiomyopathy is the deformation of ventricular cavity, which contributes to systolic dysfunction. Few studies have evaluated this deformation bearing in mind ventricular regions and segments of the ventricle, which could reveal important details of the remodeling process, supporting a better understanding of its role in functional impairment and the development of new therapeutic strategies. Objective To evaluate if, in basal, equatorial and apical regions, increased internal transverse perimeter of left ventricle in idiopathic dilated cardiomyopathy occurs proportionally between the septal and non-septal segment. Methods We performed an anatomical study with 28 adult hearts from human cadavers. One group consisted of 18 hearts with idiopathic dilated cardiomyopathy and another group with 10 normal hearts. After lamination and left ventricle digital image capture, in three different regions (base, equator and apex), the transversal internal perimeter of left ventricle was divided into two segments: septal and not septal. These segments were measured by proper software. It was established an index of proportionality between these segments, called septal and non-septal segment index. Then we determined whether this index was the same in both groups. Results Among patients with normal hearts and idiopathic dilated cardiomyopathy, the index of proportionality between the two segments (septal and non-septal) showed no significant difference in the three regions analyzed. The comparison results of the indices NSS/SS among normal and enlarged hearts were respectively: in base 1.99 versus 1.86 (P=0.46), in equator 2.22 versus 2.18 (P=0.79) and in apex 2.96 versus 3.56 (P=0.11). Conclusion In the idiopathic dilated cardiomyopathy, the transversal dilatation of left ventricular internal perimeter occurs proportionally between the segments corresponding to the septum and free wall at the basal, equatorial and apical regions of this chamber. PMID:25372906

Recent Advances in High-Resolution MEMS DM Fabrication and Integration

NASA Astrophysics Data System (ADS)

Bifano, T.; Cornelissen, S.; Bierden, P.

2010-09-01

Deformable mirrors fabricated using microelectromechanical systems technology (MEMS-DMs) have been studied at Boston University (BU) and developed/commercialized by Boston Micromachines Corporation (BMC) over the past decade. Recent advances that might have an impact on surveillance telescopes include demonstration of 4092 actuator DMs with continuous mirror face-sheets, and segmented DMs capable of frame rates of greater than 20kHz for devices with up to 1020 independent segments. The 4092 actuator DM, developed by BMC for the Gemini Planet Imaging GPI instrument, was recently delivered to the GPI instrument development team. Its packaging and platform development are described, and the performance results for the latest prototype devices are presented.

Active surface model improvement by energy function optimization for 3D segmentation.

PubMed

Azimifar, Zohreh; Mohaddesi, Mahsa

2015-04-01

This paper proposes an optimized and efficient active surface model by improving the energy functions, searching method, neighborhood definition and resampling criterion. Extracting an accurate surface of the desired object from a number of 3D images using active surface and deformable models plays an important role in computer vision especially medical image processing. Different powerful segmentation algorithms have been suggested to address the limitations associated with the model initialization, poor convergence to surface concavities and slow convergence rate. This paper proposes a method to improve one of the strongest and recent segmentation algorithms, namely the Decoupled Active Surface (DAS) method. We consider a gradient of wavelet edge extracted image and local phase coherence as external energy to extract more information from images and we use curvature integral as internal energy to focus on high curvature region extraction. Similarly, we use resampling of points and a line search for point selection to improve the accuracy of the algorithm. We further employ an estimation of the desired object as an initialization for the active surface model. A number of tests and experiments have been done and the results show the improvements with regards to the extracted surface accuracy and computational time of the presented algorithm compared with the best and recent active surface models. Copyright © 2015 Elsevier Ltd. All rights reserved.

Structural Constraints and Earthquake Recurrence Estimates for the West Tahoe-Dollar Point Fault, Lake Tahoe Basin, California

NASA Astrophysics Data System (ADS)

Maloney, J. M.; Driscoll, N. W.; Kent, G.; Brothers, D. S.; Baskin, R. L.; Babcock, J. M.; Noble, P. J.; Karlin, R. E.

2011-12-01

Previous work in the Lake Tahoe Basin (LTB), California, identified the West Tahoe-Dollar Point Fault (WTDPF) as the most hazardous fault in the region. Onshore and offshore geophysical mapping delineated three segments of the WTDPF extending along the western margin of the LTB. The rupture patterns between the three WTDPF segments remain poorly understood. Fallen Leaf Lake (FLL), Cascade Lake, and Emerald Bay are three sub-basins of the LTB, located south of Lake Tahoe, that provide an opportunity to image primary earthquake deformation along the WTDPF and associated landslide deposits. We present results from recent (June 2011) high-resolution seismic CHIRP surveys in FLL and Cascade Lake, as well as complete multibeam swath bathymetry coverage of FLL. Radiocarbon dates obtained from the new piston cores acquired in FLL provide age constraints on the older FLL slide deposits and build on and complement previous work that dated the most recent event (MRE) in Fallen Leaf Lake at ~4.1-4.5 k.y. BP. The CHIRP data beneath FLL image slide deposits that appear to correlate with contemporaneous slide deposits in Emerald Bay and Lake Tahoe. A major slide imaged in FLL CHIRP data is slightly younger than the Tsoyowata ash (7950-7730 cal yrs BP) identified in sediment cores and appears synchronous with a major Lake Tahoe slide deposit (7890-7190 cal yrs BP). The equivalent age of these slides suggests the penultimate earthquake on the WTDPF may have triggered them. If correct, we postulate a recurrence interval of ~3-4 k.y. These results suggest the FLL segment of the WTDPF is near its seismic recurrence cycle. Additionally, CHIRP profiles acquired in Cascade Lake image the WTDPF for the first time in this sub-basin, which is located near the transition zone between the FLL and Rubicon Point Sections of the WTDPF. We observe two fault-strands trending N45°W across southern Cascade Lake for ~450 m. The strands produce scarps of ~5 m and ~2.7 m, respectively, on the lake floor, but offset increases down-section to ~14 m and ~8 m at the acoustic basement. Studying the style and timing of earthquake deformation in Fallen Leaf Lake, Cascade Lake, Emerald Bay and Lake Tahoe will help us to understand how strain is partitioned between adjacent segments and the potential rupture magnitude.

Quantification of esophageal wall thickness in CT using atlas-based segmentation technique

NASA Astrophysics Data System (ADS)

Wang, Jiahui; Kang, Min Kyu; Kligerman, Seth; Lu, Wei

2015-03-01

Esophageal wall thickness is an important predictor of esophageal cancer response to therapy. In this study, we developed a computerized pipeline for quantification of esophageal wall thickness using computerized tomography (CT). We first segmented the esophagus using a multi-atlas-based segmentation scheme. The esophagus in each atlas CT was manually segmented to create a label map. Using image registration, all of the atlases were aligned to the imaging space of the target CT. The deformation field from the registration was applied to the label maps to warp them to the target space. A weighted majority-voting label fusion was employed to create the segmentation of esophagus. Finally, we excluded the lumen from the esophagus using a threshold of -600 HU and measured the esophageal wall thickness. The developed method was tested on a dataset of 30 CT scans, including 15 esophageal cancer patients and 15 normal controls. The mean Dice similarity coefficient (DSC) and mean absolute distance (MAD) between the segmented esophagus and the reference standard were employed to evaluate the segmentation results. Our method achieved a mean Dice coefficient of 65.55 ± 10.48% and mean MAD of 1.40 ± 1.31 mm for all the cases. The mean esophageal wall thickness of cancer patients and normal controls was 6.35 ± 1.19 mm and 6.03 ± 0.51 mm, respectively. We conclude that the proposed method can perform quantitative analysis of esophageal wall thickness and would be useful for tumor detection and tumor response evaluation of esophageal cancer.

Active shape models unleashed

NASA Astrophysics Data System (ADS)

Kirschner, Matthias; Wesarg, Stefan

2011-03-01

Active Shape Models (ASMs) are a popular family of segmentation algorithms which combine local appearance models for boundary detection with a statistical shape model (SSM). They are especially popular in medical imaging due to their ability for fast and accurate segmentation of anatomical structures even in large and noisy 3D images. A well-known limitation of ASMs is that the shape constraints are over-restrictive, because the segmentations are bounded by the Principal Component Analysis (PCA) subspace learned from the training data. To overcome this limitation, we propose a new energy minimization approach which combines an external image energy with an internal shape model energy. Our shape energy uses the Distance From Feature Space (DFFS) concept to allow deviations from the PCA subspace in a theoretically sound and computationally fast way. In contrast to previous approaches, our model does not rely on post-processing with constrained free-form deformation or additional complex local energy models. In addition to the energy minimization approach, we propose a new method for liver detection, a new method for initializing an SSM and an improved k-Nearest Neighbour (kNN)-classifier for boundary detection. Our ASM is evaluated with leave-one-out tests on a data set with 34 tomographic CT scans of the liver and is compared to an ASM with standard shape constraints. The quantitative results of our experiments show that we achieve higher segmentation accuracy with our energy minimization approach than with standard shape constraints.nym

The Afar rift zone deformation dynamics retrieved through phase and amplitude SAR data

NASA Astrophysics Data System (ADS)

Casu, F.; Pagli, C.; Paglia, L.; Wang, H.; Wright, T. J.; Lanari, R.

2011-12-01

The Dabbahu rift segment of the Afar depression has been active since 2005 when a 2.5 km3 dyke intrusion and hundreds of earthquakes marked the onset a rifting episode which continues to date. Since 2003, the Afar depression has been repeatedly imaged by the ENVISAT satellite, generating a large SAR archive which allow us to study the ongoing deformation processes and the dynamics of magma movements. We combine sets of small baseline interferograms through the advanced DInSAR algorithm referred to as Small BAseline Subset (SBAS), and we generate both ground deformation maps and time series along the satellite Line-Of-Sight (LOS), with accuracies on the order of 5 mm. The main limitation of DInSAR applications is that large and rapid deformations, such as those caused by dyke intrusions and eruptions in Afar, cannot be fully measured. The phase information often degrades and some areas of the interferograms are affected by high fringe rates, leading to difficulties in the phase unwrapping, and/or to complete loss of coherence due to significant misregistration errors. This limitation can be overcome by exploiting the SAR image amplitude information instead of the phase, and by calculating the Pixel-Offset (PO) field of a given SAR image pair, for both range and azimuth directions. Moreover, after computing the POs for each image pair, it is possible to combine them, following the same rationale of the SBAS technique, to finally retrieve the offset-based deformation time series. Such technique, named PO-SBAS, permits to retrieve the deformation field in areas affected by very large displacements at an accuracy that, for ENVISAT data, correspond to 30cm and 15 cm for the range and azimuth, respectively. In this work, we study the Afar rift region deformations by using both the phase and amplitude information of several sets of SAR images acquired from ascending and descending ENVISAT tracks. In particular, we use the phase information to construct dense and accurate deformation maps and time series in areas not affected by large displacements. While in areas where the deformation gradient causes loss of coherence, we retrieve the displacement field through the amplitude information. This approach allows us to obtain a spatially detailed deformation map of the study area. In addition, by combining ascending and descending data we reconstruct the vertical and East-West components of deformation field. Furthermore, in areas affected by large deformations, we can also retrieve the full 3D deformation field, by using the North-South displacement component obtained from the azimuth PO information. Distinct sources of deformations interact in Afar. Fault movements and magma chamber deflation have accompanied dyke intrusions but quantifying each contribution to the total deformation has been challenging, also due to loss of coherence in the central part of the rift. Here we combined the phase and amplitude information in order to retrieve the full deformation field of repeated intrusions. This allows us to better constrain the fault movements that occur as the dyke propagates as well as the magma movements from individual magma chambers.

Variable near-surface deformation along the Commerce segment of the Commerce geophysical lineament, southeast Missouri to southern Illinois, USA

USGS Publications Warehouse

Odum, J.K.; Stephenson, W.J.; Williams, R.A.

2003-01-01

Recent studies have demonstrated a plausible link between surface and near-surface tectonic features and the vertical projection of the Commerce geophysical lineament (CGL). The CGL is a 5- to 10-km-wide zone of basement magnetic and gravity anomalies traceable for more than 600 km, extending from Arkansas through southeast Missouri and southern Illinois and into Indiana. Twelve kilometers of high-resolution seismic reflection data, collected at four sites along a 175-km segment of the CGL projection, are interpreted to show varying amounts of deformation involving Tertiary and some Quaternary sediments. Some of the locally anomalous geomorphic features in the northern Mississippi embayment region (i.e., paleoliquefaction features, anomalous directional changes in stream channels, and areas of linear bluff escarpments) overlying the CGL can be correlated with specific faults and/or narrow zones of deformed (faulted and folded) strata that are imaged on high-resolution seismic reflection data. There is an observable change in near-surface deformation style and complexity progressing from the southwest to the northeast along the trace of the CGL. The seismic reflection data collaborate mapping evidence which suggests that this region has undergone a complex history of deformation, some of which is documented to be as young as Quaternary, during multiple episodes of reactivation under varying stress fields. This work, along with that of other studies presented in this volume, points to the existence of at least one major crustal feature outside the currently defined zone of seismic activity (New Madrid Seismic Zone) that should be considered as a significant potential source zone for seismogenic activity within the midcontinent region of the United States. ?? 2003 Elsevier B.V. All rights reserved.

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation

PubMed Central

Wilke, Marko; Altaye, Mekibib; Holland, Scott K.

2017-01-01

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating “unusual” populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php. PMID:28275348

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation.

PubMed

Wilke, Marko; Altaye, Mekibib; Holland, Scott K

2017-01-01

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.

Analysis of bone healing in flail chest injury: do we need to fix both fractures per rib?

PubMed

Marasco, Silvana; Liew, Susan; Edwards, Elton; Varma, Dinesh; Summerhayes, Robyn

2014-09-01

Surgical rib fixation (SRF) for severe rib fracture injuries is generating increasing interest in the medical literature. It is well documented that poorly healed fractured ribs can lead to chronic pain, disability, and deformity. An unanswered question in SRF for flail chest injury is whether it is sufficient to fix one fracture per rib, on successive ribs, thus converting a flail chest injury into simple fractured ribs, or whether both ends of the floating segment of the chest wall should be fixed. This study aimed to analyze SRF in flail chest injury, assessing 3-month outcomes for nonfixed fractured rib ends in the flail segment. This is a retrospective review (2005-2013) of 60 consecutive patients who underwent SRF for flail chest injury admitted to the Alfred Hospital, Melbourne, Australia. Imaging by three-dimensional computed tomography (3D CT) of the chest at admission was compared with follow-up 3D CT at 3 months after injury. The 3-month CT scans were assessed for degree of healing and presence of residual deformity at the fracture fixation site. Follow-up CT was performed in 52 of the 60 patients. At 3 months after surgery, 86.5% of the patients had at least partial healing with good alignment and adequate fracture stabilization. Hardware failure was noted in five patients (9.6%) and occurred with the absorbable prostheses only. Six patients who had preoperative overlapping or displacement showed no improvement in deformity despite fixing the lateral fractures. Callus formation and bony bridging between adjacent ribs was often noted in the rib fractures not fixed (28 of 52 patients, 54%) This retrospective review of 3D CT chest at 3 months after rib fixation indicates that a philosophy of fixing only one fracture per rib in a flail segment does not avoid deformity and displacement, particularly in posterior rib fractures. Therapeutic study, level V; epidemiologic study, level V.

3D Printed Models of Cleft Palate Pathology for Surgical Education.

PubMed

Lioufas, Peter A; Quayle, Michelle R; Leong, James C; McMenamin, Paul G

2016-09-01

To explore the potential viability and limitations of 3D printed models of children with cleft palate deformity. The advantages of 3D printed replicas of normal anatomical specimens have previously been described. The creation of 3D prints displaying patient-specific anatomical pathology for surgical planning and interventions is an emerging field. Here we explored the possibility of taking rare pediatric radiographic data sets to create 3D prints for surgical education. Magnetic resonance imaging data of 2 children (8 and 14 months) were segmented, colored, and anonymized, and stereolothographic files were prepared for 3D printing on either multicolor plastic or powder 3D printers and multimaterial 3D printers. Two models were deemed of sufficient quality and anatomical accuracy to print unamended. One data set was further manipulated digitally to artificially extend the length of the cleft. Thus, 3 models were printed: 1 incomplete soft-palate deformity, 1 incomplete anterior palate deformity, and 1 complete cleft palate. All had cleft lip deformity. The single-material 3D prints are of sufficient quality to accurately identify the nature and extent of the deformities. Multimaterial prints were subsequently created, which could be valuable in surgical training. Improvements in the quality and resolution of radiographic imaging combined with the advent of multicolor multiproperty printer technology will make it feasible in the near future to print 3D replicas in materials that mimic the mechanical properties and color of live human tissue making them potentially suitable for surgical training.

Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases

PubMed Central

Pohl, Kilian M.; Fisher, John; Bouix, Sylvain; Shenton, Martha; McCarley, Robert W.; Grimson, W. Eric L.; Kikinis, Ron; Wells, William M.

2007-01-01

The Logarithm of the Odds ratio (LogOdds) is frequently used in areas such as artificial neural networks, economics, and biology, as an alternative representation of probabilities. Here, we use LogOdds to place probabilistic atlases in a linear vector space. This representation has several useful properties for medical imaging. For example, it not only encodes the shape of multiple anatomical structures but also captures some information concerning uncertainty. We demonstrate that the resulting vector space operations of addition and scalar multiplication have natural probabilistic interpretations. We discuss several examples for placing label maps into the space of LogOdds. First, we relate signed distance maps, a widely used implicit shape representation, to LogOdds and compare it to an alternative that is based on smoothing by spatial Gaussians. We find that the LogOdds approach better preserves shapes in a complex multiple object setting. In the second example, we capture the uncertainty of boundary locations by mapping multiple label maps of the same object into the LogOdds space. Third, we define a framework for non-convex interpolations among atlases that capture different time points in the aging process of a population. We evaluate the accuracy of our representation by generating a deformable shape atlas that captures the variations of anatomical shapes across a population. The deformable atlas is the result of a principal component analysis within the LogOdds space. This atlas is integrated into an existing segmentation approach for MR images. We compare the performance of the resulting implementation in segmenting 20 test cases to a similar approach that uses a more standard shape model that is based on signed distance maps. On this data set, the Bayesian classification model with our new representation outperformed the other approaches in segmenting subcortical structures. PMID:17698403

Technical Note: scuda: A software platform for cumulative dose assessment.

PubMed

Park, Seyoun; McNutt, Todd; Plishker, William; Quon, Harry; Wong, John; Shekhar, Raj; Lee, Junghoon

2016-10-01

Accurate tracking of anatomical changes and computation of actually delivered dose to the patient are critical for successful adaptive radiation therapy (ART). Additionally, efficient data management and fast processing are practically important for the adoption in clinic as ART involves a large amount of image and treatment data. The purpose of this study was to develop an accurate and efficient Software platform for CUmulative Dose Assessment (scuda) that can be seamlessly integrated into the clinical workflow. scuda consists of deformable image registration (DIR), segmentation, dose computation modules, and a graphical user interface. It is connected to our image PACS and radiotherapy informatics databases from which it automatically queries/retrieves patient images, radiotherapy plan, beam data, and daily treatment information, thus providing an efficient and unified workflow. For accurate registration of the planning CT and daily CBCTs, the authors iteratively correct CBCT intensities by matching local intensity histograms during the DIR process. Contours of the target tumor and critical structures are then propagated from the planning CT to daily CBCTs using the computed deformations. The actual delivered daily dose is computed using the registered CT and patient setup information by a superposition/convolution algorithm, and accumulated using the computed deformation fields. Both DIR and dose computation modules are accelerated by a graphics processing unit. The cumulative dose computation process has been validated on 30 head and neck (HN) cancer cases, showing 3.5 ± 5.0 Gy (mean±STD) absolute mean dose differences between the planned and the actually delivered doses in the parotid glands. On average, DIR, dose computation, and segmentation take 20 s/fraction and 17 min for a 35-fraction treatment including additional computation for dose accumulation. The authors developed a unified software platform that provides accurate and efficient monitoring of anatomical changes and computation of actually delivered dose to the patient, thus realizing an efficient cumulative dose computation workflow. Evaluation on HN cases demonstrated the utility of our platform for monitoring the treatment quality and detecting significant dosimetric variations that are keys to successful ART.

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Baochun; Huang, Cheng; Zhou, Shoujun

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-levelmore » active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.« less

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.

PubMed

He, Baochun; Huang, Cheng; Sharp, Gregory; Zhou, Shoujun; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

2016-05-01

A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods-3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration-are used to establish shape correspondence. The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

NASA Astrophysics Data System (ADS)

Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

2018-05-01

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

Automatic real time evaluation of red blood cell elasticity by optical tweezers

NASA Astrophysics Data System (ADS)

Moura, Diógenes S.; Silva, Diego C. N.; Williams, Ajoke J.; Bezerra, Marcos A. C.; Fontes, Adriana; de Araujo, Renato E.

2015-05-01

Optical tweezers have been used to trap, manipulate, and measure individual cell properties. In this work, we show that the association of a computer controlled optical tweezers system with image processing techniques allows rapid and reproducible evaluation of cell deformability. In particular, the deformability of red blood cells (RBCs) plays a key role in the transport of oxygen through the blood microcirculation. The automatic measurement processes consisted of three steps: acquisition, segmentation of images, and measurement of the elasticity of the cells. An optical tweezers system was setup on an upright microscope equipped with a CCD camera and a motorized XYZ stage, computer controlled by a Labview platform. On the optical tweezers setup, the deformation of the captured RBC was obtained by moving the motorized stage. The automatic real-time homemade system was evaluated by measuring RBCs elasticity from normal donors and patients with sickle cell anemia. Approximately 150 erythrocytes were examined, and the elasticity values obtained by using the developed system were compared to the values measured by two experts. With the automatic system, there was a significant time reduction (60 × ) of the erythrocytes elasticity evaluation. Automated system can help to expand the applications of optical tweezers in hematology and hemotherapy.

Segmentation of brain structures in presence of a space-occupying lesion.

PubMed

Pollo, Claudio; Cuadra, Meritxell Bach; Cuisenaire, Olivier; Villemure, Jean-Guy; Thiran, Jean-Philippe

2005-02-15

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

Digital image measurement of specimen deformation based on CCD cameras and Image J software: an application to human pelvic biomechanics

NASA Astrophysics Data System (ADS)

Jia, Yongwei; Cheng, Liming; Yu, Guangrong; Lou, Yongjian; Yu, Yan; Chen, Bo; Ding, Zuquan

2008-03-01

A method of digital image measurement of specimen deformation based on CCD cameras and Image J software was developed. This method was used to measure the biomechanics behavior of human pelvis. Six cadaveric specimens from the third lumbar vertebra to the proximal 1/3 part of femur were tested. The specimens without any structural abnormalities were dissected of all soft tissue, sparing the hip joint capsules and the ligaments of the pelvic ring and floor. Markers with black dot on white background were affixed to the key regions of the pelvis. Axial loading from the proximal lumbar was applied by MTS in the gradient of 0N to 500N, which simulated the double feet standing stance. The anterior and lateral images of the specimen were obtained through two CCD cameras. Based on Image J software, digital image processing software, which can be freely downloaded from the National Institutes of Health, digital 8-bit images were processed. The procedure includes the recognition of digital marker, image invert, sub-pixel reconstruction, image segmentation, center of mass algorithm based on weighted average of pixel gray values. Vertical displacements of S1 (the first sacral vertebrae) in front view and micro-angular rotation of sacroiliac joint in lateral view were calculated according to the marker movement. The results of digital image measurement showed as following: marker image correlation before and after deformation was excellent. The average correlation coefficient was about 0.983. According to the 768 × 576 pixels image (pixel size 0.68mm × 0.68mm), the precision of the displacement detected in our experiment was about 0.018 pixels and the comparatively error could achieve 1.11\\perthou. The average vertical displacement of S1 of the pelvis was 0.8356+/-0.2830mm under vertical load of 500 Newtons and the average micro-angular rotation of sacroiliac joint in lateral view was 0.584+/-0.221°. The load-displacement curves obtained from our optical measure system matched the clinical results. Digital image measurement of specimen deformation based on CCD cameras and Image J software has good perspective for application in biomechanical research, which has the advantage of simple optical setup, no-contact, high precision, and no special requirement of test environment.

Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients.

PubMed

Sjöberg, Carl; Lundmark, Martin; Granberg, Christoffer; Johansson, Silvia; Ahnesjö, Anders; Montelius, Anders

2013-10-03

Semi-automated segmentation using deformable registration of selected atlas cases consisting of expert segmented patient images has been proposed to facilitate the delineation of lymph node regions for three-dimensional conformal and intensity-modulated radiotherapy planning of head and neck and prostate tumours. Our aim is to investigate if fusion of multiple atlases will lead to clinical workload reductions and more accurate segmentation proposals compared to the use of a single atlas segmentation, due to a more complete representation of the anatomical variations. Atlases for lymph node regions were constructed using 11 head and neck patients and 15 prostate patients based on published recommendations for segmentations. A commercial registration software (Velocity AI) was used to create individual segmentations through deformable registration. Ten head and neck patients, and ten prostate patients, all different from the atlas patients, were randomly chosen for the study from retrospective data. Each patient was first delineated three times, (a) manually by a radiation oncologist, (b) automatically using a single atlas segmentation proposal from a chosen atlas and (c) automatically by fusing the atlas proposals from all cases in the database using the probabilistic weighting fusion algorithm. In a subsequent step a radiation oncologist corrected the segmentation proposals achieved from step (b) and (c) without using the result from method (a) as reference. The time spent for editing the segmentations was recorded separately for each method and for each individual structure. Finally, the Dice Similarity Coefficient and the volume of the structures were used to evaluate the similarity between the structures delineated with the different methods. For the single atlas method, the time reduction compared to manual segmentation was 29% and 23% for head and neck and pelvis lymph nodes, respectively, while editing the fused atlas proposal resulted in time reductions of 49% and 34%. The average volume of the fused atlas proposals was only 74% of the manual segmentation for the head and neck cases and 82% for the prostate cases due to a blurring effect from the fusion process. After editing of the proposals the resulting volume differences were no longer statistically significant, although a slight influence by the proposals could be noticed since the average edited volume was still slightly smaller than the manual segmentation, 9% and 5%, respectively. Segmentation based on fusion of multiple atlases reduces the time needed for delineation of lymph node regions compared to the use of a single atlas segmentation. Even though the time saving is large, the quality of the segmentation is maintained compared to manual segmentation.

A Voxel-by-Voxel Comparison of Deformable Vector Fields Obtained by Three Deformable Image Registration Algorithms Applied to 4DCT Lung Studies.

PubMed

Fatyga, Mirek; Dogan, Nesrin; Weiss, Elizabeth; Sleeman, William C; Zhang, Baoshe; Lehman, William J; Williamson, Jeffrey F; Wijesooriya, Krishni; Christensen, Gary E

2015-01-01

Commonly used methods of assessing the accuracy of deformable image registration (DIR) rely on image segmentation or landmark selection. These methods are very labor intensive and thus limited to relatively small number of image pairs. The direct voxel-by-voxel comparison can be automated to examine fluctuations in DIR quality on a long series of image pairs. A voxel-by-voxel comparison of three DIR algorithms applied to lung patients is presented. Registrations are compared by comparing volume histograms formed both with individual DIR maps and with a voxel-by-voxel subtraction of the two maps. When two DIR maps agree one concludes that both maps are interchangeable in treatment planning applications, though one cannot conclude that either one agrees with the ground truth. If two DIR maps significantly disagree one concludes that at least one of the maps deviates from the ground truth. We use the method to compare 3 DIR algorithms applied to peak inhale-peak exhale registrations of 4DFBCT data obtained from 13 patients. All three algorithms appear to be nearly equivalent when compared using DICE similarity coefficients. A comparison based on Jacobian volume histograms shows that all three algorithms measure changes in total volume of the lungs with reasonable accuracy, but show large differences in the variance of Jacobian distribution on contoured structures. Analysis of voxel-by-voxel subtraction of DIR maps shows differences between algorithms that exceed a centimeter for some registrations. Deformation maps produced by DIR algorithms must be treated as mathematical approximations of physical tissue deformation that are not self-consistent and may thus be useful only in applications for which they have been specifically validated. The three algorithms tested in this work perform fairly robustly for the task of contour propagation, but produce potentially unreliable results for the task of DVH accumulation or measurement of local volume change. Performance of DIR algorithms varies significantly from one image pair to the next hence validation efforts, which are exhaustive but performed on a small number of image pairs may not reflect the performance of the same algorithm in practical clinical situations. Such efforts should be supplemented by validation based on a longer series of images of clinical quality.

Structure for identifying, locating and quantifying physical phenomena

DOEpatents

Richardson, John G.

2006-10-24

A method and system for detecting, locating and quantifying a physical phenomena such as strain or a deformation in a structure. A minimum resolvable distance along the structure is selected and a quantity of laterally adjacent conductors is determined. Each conductor includes a plurality of segments coupled in series which define the minimum resolvable distance along the structure. When a deformation occurs, changes in the defined energy transmission characteristics along each conductor are compared to determine which segment contains the deformation.

Method and apparatus for identifying, locating and quantifying physical phenomena and structure including same

DOEpatents

Richardson, John G.

2006-01-24

A method and system for detecting, locating and quantifying a physical phenomena such as strain or a deformation in a structure. A minimum resolvable distance along the structure is selected and a quantity of laterally adjacent conductors is determined. Each conductor includes a plurality of segments coupled in series which define the minimum resolvable distance along the structure. When a deformation occurs, changes in the defined energy transmission characteristics along each conductor are compared to determine which segment contains the deformation.

Toward Intraoperative Image-Guided Transoral Robotic Surgery

PubMed Central

Liu, Wen P.; Reaugamornrat, Sureerat; Deguet, Anton; Sorger, Jonathan M.; Siewerdsen, Jeffrey H.; Richmon, Jeremy; Taylor, Russell H.

2014-01-01

This paper presents the development and evaluation of video augmentation on the stereoscopic da Vinci S system with intraoperative image guidance for base of tongue tumor resection in transoral robotic surgery (TORS). Proposed workflow for image-guided TORS begins by identifying and segmenting critical oropharyngeal structures (e.g., the tumor and adjacent arteries and nerves) from preoperative computed tomography (CT) and/or magnetic resonance (MR) imaging. These preoperative planned data can be deformably registered to the intraoperative endoscopic view using mobile C-arm cone-beam computed tomography (CBCT) [1, 2]. Augmentation of TORS endoscopic video defining surgical targets and critical structures has the potential to improve navigation, spatial orientation, and confidence in tumor resection. Experiments in animal specimens achieved statistically significant improvement in target localization error when comparing the proposed image guidance system to simulated current practice. PMID:25525474

Radiographic-directed local coordinate systems critical in kinematic analysis of walking in diabetes-related medial column foot deformity.

PubMed

Hastings, Mary K; Woodburn, James; Mueller, Michael J; Strube, Michael J; Johnson, Jeffrey E; Beckert, Krista S; Stein, Michelle L; Sinacore, David R

2014-01-01

Diabetic foot deformity onset and progression maybe associated with abnormal foot and ankle motion. The modified Oxford multi-segmental foot model allows kinematic assessment of inter-segmental foot motion. However, there are insufficient anatomical landmarks to accurately representation the alignment of the hindfoot and forefoot segments during model construction. This is most notable for the sagittal plane which is referenced parallel to the floor, allowing comparison of inter-segmental excursion but not capturing important sagittal hind-to-forefoot deformity associated with diabetic foot disease and can potentially underestimate true kinematic differences. The purpose of the study was to compare walking kinematics using local coordinate systems derived from the modified Oxford model and the radiographic directed model which incorporated individual calcaneal and 1st metatarsal declination pitch angles for the hindfoot and forefoot. We studied twelve participants in each of the following groups: (1) diabetes mellitus, peripheral neuropathy and medial column foot deformity (DMPN+), (2) DMPN without medial column deformity (DMPN-) and (3) age- and weight-match controls. The modified Oxford model coordinate system did not identify differences between groups in the initial, peak, final, or excursion hindfoot relative to shank or forefoot relative to hindfoot dorsiflexion/plantarflexion during walking. The radiographic coordinate system identified the DMPN+ group to have an initial, peak and final position of the forefoot relative to hindfoot that was more dorsiflexed (lower arch phenotype) than the DMPN- group (p<.05). Use of radiographic alignment in kinematic modeling of those with foot deformity reveals segmental motion occurring upon alignment indicative of a lower arch. Copyright © 2014 Elsevier B.V. All rights reserved.

Accurate quantification of local changes for carotid arteries in 3D ultrasound images using convex optimization-based deformable registration

NASA Astrophysics Data System (ADS)

Cheng, Jieyu; Qiu, Wu; Yuan, Jing; Fenster, Aaron; Chiu, Bernard

2016-03-01

Registration of longitudinally acquired 3D ultrasound (US) images plays an important role in monitoring and quantifying progression/regression of carotid atherosclerosis. We introduce an image-based non-rigid registration algorithm to align the baseline 3D carotid US with longitudinal images acquired over several follow-up time points. This algorithm minimizes the sum of absolute intensity differences (SAD) under a variational optical-flow perspective within a multi-scale optimization framework to capture local and global deformations. Outer wall and lumen were segmented manually on each image, and the performance of the registration algorithm was quantified by Dice similarity coefficient (DSC) and mean absolute distance (MAD) of the outer wall and lumen surfaces after registration. In this study, images for 5 subjects were registered initially by rigid registration, followed by the proposed algorithm. Mean DSC generated by the proposed algorithm was 79:3+/-3:8% for lumen and 85:9+/-4:0% for outer wall, compared to 73:9+/-3:4% and 84:7+/-3:2% generated by rigid registration. Mean MAD of 0:46+/-0:08mm and 0:52+/-0:13mm were generated for lumen and outer wall respectively by the proposed algorithm, compared to 0:55+/-0:08mm and 0:54+/-0:11mm generated by rigid registration. The mean registration time of our method per image pair was 143+/-23s.

Applications of wavelets in morphometric analysis of medical images

NASA Astrophysics Data System (ADS)

Davatzikos, Christos; Tao, Xiaodong; Shen, Dinggang

2003-11-01

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

Encoding probabilistic brain atlases using Bayesian inference.

PubMed

Van Leemput, Koen

2009-06-01

This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an "averaging" approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard "average" atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise registration and atlas estimation scheme that encompasses standard averaging as a special case. We also use Bayesian inference to compare alternative atlas models in light of the training data, and show how this leads to a data compression problem that is intuitive to interpret and computationally feasible. Using this technique, we automatically determine the optimal amount of spatial blurring, the best deformation field flexibility, and the most compact mesh representation. We demonstrate, using 2-D training datasets, that the resulting models are better at capturing the structure in the training data than conventional probabilistic atlases. We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans.

Deformable anatomical templates for brachytherapy treatment planning in radiotherapy of cervical cancer

NASA Astrophysics Data System (ADS)

Christensen, Gary E.; Williamson, Jeffrey F.; Chao, K. S. C.; Miller, Michael I.; So, F. B.; Vannier, Michael W.

1997-10-01

This paper describes a new method to register serial, volumetric x-ray computed tomography (CT) data sets for tracking soft-tissue deformation caused by insertion of intracavity brachytherapy applicators to treat cervical cancer. 3D CT scans collected from the same patient with and without a brachytherapy applicator are registered to aid in computation of the radiation dose to tumor and normal tissue. The 3D CT image volume of pelvic anatomy with the applicator. Initial registration is accomplished by rigid alignment of the pelvic bones and non-rigid alignment of gray scale CT data and hand segmentations of the vagina, cervix, bladder, and rectum. A viscous fluid transformation model is used for non-rigid registration to allow for local, non-linear registration of the vagina, cervix, bladder, and rectum without disturbing the rigid registration of the bony pelvis and adjacent structures. Results are presented in which two 3D CT data sets of the same patient - imaged with and without a brachytherapy applicator - are registered.

Uncovering the true nature of deformation microstructures using 3D analysis methods

NASA Astrophysics Data System (ADS)

Ferry, M.; Quadir, M. Z.; Afrin, N.; Xu, W.; Loeb, A.; Soe, B.; McMahon, C.; George, C.; Bassman, L.

2015-08-01

Three-dimensional electron backscatter diffraction (3D EBSD) has emerged as a powerful technique for generating 3D crystallographic information in reasonably large volumes of a microstructure. The technique uses a focused ion beam (FIB) as a high precision serial sectioning device for generating consecutive ion milled surfaces of a material, with each milled surface subsequently mapped by EBSD. The successive EBSD maps are combined using a suitable post-processing method to generate a crystallographic volume of the microstructure. The first part of this paper shows the usefulness of 3D EBSD for understanding the origin of various structural features associated with the plastic deformation of metals. The second part describes a new method for automatically identifying the various types of low and high angle boundaries found in deformed and annealed metals, particularly those associated with grains exhibiting subtle and gradual variations in orientation. We have adapted a 2D image segmentation technique, fast multiscale clustering, to 3D EBSD data using a novel variance function to accommodate quaternion data. This adaptation is capable of segmenting based on subtle and gradual variation as well as on sharp boundaries within the data. We demonstrate the excellent capabilities of this technique with application to 3D EBSD data sets generated from a range of cold rolled and annealed metals described in the paper.

Mapping tectonic and anthropogenic processes in central California using satellite and airborne InSAR

NASA Astrophysics Data System (ADS)

Liu, Z.; Lundgren, P.; Liang, C.; Farr, T. G.; Fielding, E. J.

2017-12-01

The improved spatiotemporal resolution of surface deformation from recent satellite and airborne InSAR measurements provides a great opportunity to improve our understanding of both tectonic and non-tectonic processes. In central California the primary plate boundary fault system (San Andreas fault) lies adjacent to the San Joaquin Valley (SJV), a vast structural trough that accounts for about one-sixth of the United Sates' irrigated land and one-fifth of its extracted groundwater. The central San Andreas fault (CSAF) displays a range of fault slip behavior with creeping in its central segment that decreases towards its northwest and southeast ends, where it transitions to being fully locked. Despite much progress, many questions regarding fault and anthropogenic processes in the region still remain. In this study, we combine satellite InSAR and NASA airborne UAVSAR data to image fault and anthropogenic deformation. The UAVSAR data cover fault perpendicular swaths imaged from opposing look directions and fault parallel swaths since 2009. The much finer spatial resolution and optimized viewing geometry provide important constraints on near fault deformation and fault slip at very shallow depth. We performed a synoptic InSAR time series analysis using Sentinel-1, ALOS, and UAVSAR interferograms. We estimate azimuth mis-registration between single look complex (SLC) images of Sentinel-1 in a stack sense to achieve accurate azimuth co-registration between SLC images for low coherence and/or long interval interferometric pairs. We show that it is important to correct large-scale ionosphere features in ALOS-2 ScanSAR data for accurate deformation measurements. Joint analysis of UAVSAR and ALOS interferometry measurements show clear variability in deformation along the fault strike, suggesting variable fault creep and locking at depth and along strike. In addition to fault creep, the L-band ALOS, and especially ALOS-2 ScanSAR interferometry, show large-scale ground subsidence in the SJV due to over-exploitation of groundwater. InSAR time series are compared to GPS and well-water hydraulic head in-situ time series to understand water storage processes and mass loading changes. We present model results to assess the influence of anthropogenic processes on surface deformation and fault mechanics.

Automatic DNA Diagnosis for 1D Gel Electrophoresis Images using Bio-image Processing Technique.

PubMed

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Shaw, Philip J; Ukosakit, Kittipat; Tragoonrung, Somvong; Tongsima, Sissades

2015-01-01

DNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained. We developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists. This work presents an automated genotyping tool from DNA gel electrophoresis images, called GELect, which was written in Java and made available through the imageJ framework. With a novel automated image processing workflow, the tool can accurately segment lanes from a gel matrix, intelligently extract distorted and even doublet bands that are difficult to identify by existing image processing tools. Consequently, genotyping from DNA gel electrophoresis can be performed automatically allowing users to efficiently conduct large scale DNA fingerprinting via DNA gel electrophoresis. The software is freely available from http://www.biotec.or.th/gi/tools/gelect.

Photogrammetric fingerprint unwrapping

NASA Astrophysics Data System (ADS)

Paar, Gerhard; del Pilar Caballo Perucha, Maria; Bauer, Arnold; Nauschnegg, Bernhard

2008-04-01

Fingerprints are important biometric cues. Compared to conventional fingerprint sensors the use of contact-free stereoscopic image acquisition of the front-most finger segment has a set of advantages: Finger deformation is avoided, the entire relevant area for biometric use is covered, some technical aspects like sensor maintenance and cleaning are facilitated, and access to a three-dimensional reconstruction of the covered area is possible. We describe a photogrammetric workflow for nail-to-nail fingerprint reconstruction: A calibrated sensor setup with typically 5 cameras and dedicated illumination acquires adjacent stereo pairs. Using the silhouettes of the segmented finger a raw cylindrical model is generated. After preprocessing (shading correction, dust removal, lens distortion correction), each individual camera texture is projected onto the model. Image-to-image matching on these pseudo ortho images and dense 3D reconstruction obtains a textured cylindrical digital surface model with radial distances around the major axis and a grid size in the range of 25-50 µm. The model allows for objective fingerprint unwrapping and novel fingerprint matching algorithms since 3D relations between fingerprint features are available as additional cues. Moreover, covering the entire region with relevant fingerprint texture is particularly important for establishing a comprehensive forensic database. The workflow has been implemented in portable C and is ready for industrial exploitation. Further improvement issues are code optimization, unwrapping method, illumination strategy to avoid highlights and to improve the initial segmentation, and the comparison of the unwrapping result to conventional fingerprint acquisition technology.

Image fusion for visualization of hepatic vasculature and tumors

NASA Astrophysics Data System (ADS)

Chou, Jin-Shin; Chen, Shiuh-Yung J.; Sudakoff, Gary S.; Hoffmann, Kenneth R.; Chen, Chin-Tu; Dachman, Abraham H.

1995-05-01

We have developed segmentation and simultaneous display techniques to facilitate the visualization of the three-dimensional spatial relationships between organ structures and organ vasculature. We concentrate on the visualization of the liver based on spiral computed tomography images. Surface-based 3-D rendering and maximal intensity projection algorithms are used for data visualization. To extract the liver in the serial of images accurately and efficiently, we have developed a user-friendly interactive program with a deformable-model segmentation. Surface rendering techniques are used to visualize the extracted structures, adjacent contours are aligned and fitted with a Bezier surface to yield a smooth surface. Visualization of the vascular structures, portal and hepatic veins, is achieved by applying a MIP technique to the extracted liver volume. To integrate the extracted structures they are surface-rendered and their MIP images are aligned and a color table is designed for simultaneous display of the combined liver/tumor and vasculature images. By combining the 3-D surface rendering and MIP techniques, portal veins, hepatic veins, and hepatic tumor can be inspected simultaneously and their spatial relationships can be more easily perceived. The proposed technique will be useful for visualization of both hepatic neoplasm and vasculature in surgical planning for tumor resection or living-donor liver transplantation.

High-contrast imager for Complex Aperture Telescopes (HiCAT). 4. Status and wavefront control development

NASA Astrophysics Data System (ADS)

Leboulleux, Lucie; N'Diaye, Mamadou; Riggs, A. J. E.; Egron, Sylvain; Mazoyer, Johan; Pueyo, Laurent; Choquet, Elodie; Perrin, Marshall D.; Kasdin, Jeremy; Sauvage, Jean-François; Fusco, Thierry; Soummer, Rémi

2016-07-01

Segmented telescopes are a possible approach to enable large-aperture space telescopes for the direct imaging and spectroscopy of habitable worlds. However, the increased complexity of their aperture geometry, due to their central obstruction, support structures and segment gaps, makes high-contrast imaging very challenging. The High-contrast imager for Complex Aperture Telescopes (HiCAT) was designed to study and develop solutions for such telescope pupils using wavefront control and starlight suppression. The testbed design has the flexibility to enable studies with increasing complexity for telescope aperture geometries starting with off-axis telescopes, then on-axis telescopes with central obstruction and support structures (e.g. the Wide Field Infrared Survey Telescope [WFIRST]), up to on-axis segmented telescopes e.g. including various concepts for a Large UV, Optical, IR telescope (LUVOIR), such as the High Definition Space Telescope (HDST). We completed optical alignment in the summer of 2014 and a first deformable mirror was successfully integrated in the testbed, with a total wavefront error of 13nm RMS over a 18mm diameter circular pupil in open loop. HiCAT will also be provided with a segmented mirror conjugated with a shaped pupil representing the HDST configuration, to directly study wavefront control in the presence of segment gaps, central obstruction and spider. We recently applied a focal plane wavefront control method combined with a classical Lyot coronagraph on HiCAT, and we found limitations on contrast performance due to vibration effect. In this communication, we analyze this instability and study its impact on the performance of wavefront control algorithms. We present our Speckle Nulling code to control and correct for wavefront errors both in simulation mode and on testbed mode. This routine is first tested in simulation mode without instability to validate our code. We then add simulated vibrations to study the degradation of contrast performance in the presence of these effects.

Sensitivity analysis of brain morphometry based on MRI-derived surface models

NASA Astrophysics Data System (ADS)

Klein, Gregory J.; Teng, Xia; Schoenemann, P. T.; Budinger, Thomas F.

1998-07-01

Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for characterizing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due to shrinkage of the cerebrum after extraction from the skull, deformation of slices during sectioning, and numerous other factors. In vivo imaging studies of brain anatomy avoid these problems and allow repetitive studies following progression of brain structure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh models of the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing technique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosions and openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region- following procedure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiled together to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation of cortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex from the skull, the technique is automatic and requires only modest computation time on modern workstations. Though the use of image data avoids many of the pitfalls of ex vivo and sectioning techniques, our MRI-based technique is still vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies include segmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise and surface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cortical surface area and volume.

Thermal Model Development for an X-Ray Mirror Assembly

NASA Technical Reports Server (NTRS)

Bonafede, Joseph A.

2015-01-01

Space-based x-ray optics require stringent thermal environmental control to achieve the desired image quality. Future x-ray telescopes will employ hundreds of nearly cylindrical, thin mirror shells to maximize effective area, with each shell built from small azimuthal segment pairs for manufacturability. Thermal issues with these thin optics are inevitable because the mirrors must have a near unobstructed view of space while maintaining near uniform 20 C temperature to avoid thermal deformations. NASA Goddard has been investigating the thermal characteristics of a future x-ray telescope with an image requirement of 5 arc-seconds and only 1 arc-second focusing error allocated for thermal distortion. The telescope employs 135 effective mirror shells formed from 7320 individual mirror segments mounted in three rings of 18, 30, and 36 modules each. Thermal requirements demand a complex thermal control system and detailed thermal modeling to verify performance. This presentation introduces innovative modeling efforts used for the conceptual design of the mirror assembly and presents results demonstrating potential feasibility of the thermal requirements.

New method of limb deformities correction in children.

PubMed Central

Atar, D.; Lehman, W. B.; Grant, A. D.; Strongwater, A.; Frankel, V. H.; Posner, M.; Golyakhovsky, V.

1992-01-01

A new "bloodless" technique (Ilizarov) was used to correct 36 limb deformities in 29 children. There were six leg length discrepancies, five achondroplasias, four deformed feet, five joint contractures, one rotational deformity of tibia, and in three the apparatus was used as an external fixator after corrective osteotomy. Lengthening was accomplished in 15 of the 16 procedures (93%). Average increase in femur length was 10 cm (32%), in tibial length 7.5 cm (30%), in humerus 11 cm (40%). Bony union was achieved in two out of five pseudoarthroses. Four deformed feet were fully corrected. Joint contractures were corrected in four out of five. The complication rate is as high as in other methods but with the Ilizarov apparatus, longer segments of bone were lengthened and more complex deformities were treated. Complications lessened as experience was gained. Images Fig. 1a,b Fig. 1 Fig. 1c Fig. 1d Fig. 1e Fig. 2a Fig. 2b Fig. 2c Fig. 2d Fig. 2e Fig. 3a Fig. 3b Fig. 3c Fig. 3d Fig. 4a Fig. 4b Fig. 4c Fig. 4d Fig. 4e Fig. 5a Fig. 5b Fig. 5c Fig. 5d PMID:1490205

SU-F-J-84: Comparison of Quantitative Deformable Image Registration Evaluation Tools: Application to Prostate IGART

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dogan, N; Weiss, E; Sleeman, W

Purpose: Errors in displacement vector fields (DVFs) generated by Deformable Image Registration (DIR) algorithms can give rise to significant uncertainties in contour propagation and dose accumulation in Image-Guided Adaptive Radiotherapy (IGART). The purpose of this work is to assess the accuracy of two DIR algorithms using a variety of quality metrics for prostate IGART. Methods: Pelvic CT images were selected from an anonymized database of nineteen prostate patients who underwent 8–12 serial scans during radiotherapy. Prostate, bladder, and rectum were contoured on 34 image-sets for three patients by the same physician. The planning CT was deformably-registered to daily CT usingmore » three variants of the Small deformation Inverse Consistent Linear Elastic (SICLE) algorithm: Grayscale-driven (G), Contour-driven (C, which utilizes segmented structures to drive DIR), combined (G+C); and also grayscale ITK demons (Gd). The accuracy of G, C, G+C SICLE and Gd registrations were evaluated using a new metric Edge Gradient Distance to Agreement (EGDTA) and other commonly-used metrics such as Pearson Correlation Coefficient (PCC), Dice Similarity Index (DSI) and Hausdorff Distance (HD). Results: C and G+C demonstrated much better performance at organ boundaries, revealing the lowest HD and highest DSI, in prostate, bladder and rectum. G+C demonstrated the lowest mean EGDTA (1.14 mm), which corresponds to highest registration quality, compared to G and C DVFs (1.16 and 2.34 mm). However, demons DIR showed the best overall performance, revealing lowest EGDTA (0.73 mm) and highest PCC (0.85). Conclusion: As expected, both C- and C+G SICLE more accurately reproduce manually-contoured target datasets than G-SICLE or Gd using HD and DSI metrics. In general, the Gd appears to have difficulty reproducing large daily position and shape changes in the rectum and bladder. However, Gd outperforms SICLE in terms of EGDTA and PCC metrics, possibly at the expense of topological quality of the estimated DVFs.« less

Quality Assurance Assessment of Diagnostic and Radiation Therapy–Simulation CT Image Registration for Head and Neck Radiation Therapy: Anatomic Region of Interest–based Comparison of Rigid and Deformable Algorithms

PubMed Central

Mohamed, Abdallah S. R.; Ruangskul, Manee-Naad; Awan, Musaddiq J.; Baron, Charles A.; Kalpathy-Cramer, Jayashree; Castillo, Richard; Castillo, Edward; Guerrero, Thomas M.; Kocak-Uzel, Esengul; Yang, Jinzhong; Court, Laurence E.; Kantor, Michael E.; Gunn, G. Brandon; Colen, Rivka R.; Frank, Steven J.; Garden, Adam S.; Rosenthal, David I.

2015-01-01

Purpose To develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy–simulation computed tomography (CT) with diagnostic CT coregistration. Materials and Methods Radiation therapy–simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison. Results A total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness. Conclusion Development of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation. © RSNA, 2014 Online supplemental material is available for this article. PMID:25380454

3D Printed Models of Cleft Palate Pathology for Surgical Education

PubMed Central

Lioufas, Peter A.; Quayle, Michelle R.; Leong, James C.

2016-01-01

Objective: To explore the potential viability and limitations of 3D printed models of children with cleft palate deformity. Background: The advantages of 3D printed replicas of normal anatomical specimens have previously been described. The creation of 3D prints displaying patient-specific anatomical pathology for surgical planning and interventions is an emerging field. Here we explored the possibility of taking rare pediatric radiographic data sets to create 3D prints for surgical education. Methods: Magnetic resonance imaging data of 2 children (8 and 14 months) were segmented, colored, and anonymized, and stereolothographic files were prepared for 3D printing on either multicolor plastic or powder 3D printers and multimaterial 3D printers. Results: Two models were deemed of sufficient quality and anatomical accuracy to print unamended. One data set was further manipulated digitally to artificially extend the length of the cleft. Thus, 3 models were printed: 1 incomplete soft-palate deformity, 1 incomplete anterior palate deformity, and 1 complete cleft palate. All had cleft lip deformity. The single-material 3D prints are of sufficient quality to accurately identify the nature and extent of the deformities. Multimaterial prints were subsequently created, which could be valuable in surgical training. Conclusion: Improvements in the quality and resolution of radiographic imaging combined with the advent of multicolor multiproperty printer technology will make it feasible in the near future to print 3D replicas in materials that mimic the mechanical properties and color of live human tissue making them potentially suitable for surgical training. PMID:27757345

Development of a customizable software application for medical imaging analysis and visualization.

PubMed

Martinez-Escobar, Marisol; Peloquin, Catherine; Juhnke, Bethany; Peddicord, Joanna; Jose, Sonia; Noon, Christian; Foo, Jung Leng; Winer, Eliot

2011-01-01

Graphics technology has extended medical imaging tools to the hands of surgeons and doctors, beyond the radiology suite. However, a common issue in most medical imaging software is the added complexity for non-radiologists. This paper presents the development of a unique software toolset that is highly customizable and targeted at the general physicians as well as the medical specialists. The core functionality includes features such as viewing medical images in two-and three-dimensional representations, clipping, tissue windowing, and coloring. Additional features can be loaded in the form of 'plug-ins' such as tumor segmentation, tissue deformation, and surgical planning. This allows the software to be lightweight and easy to use while still giving the user the flexibility of adding the necessary features, thus catering to a wide range of user population.

A whole brain morphometric analysis of changes associated with pre-term birth

NASA Astrophysics Data System (ADS)

Thomaz, C. E.; Boardman, J. P.; Counsell, S.; Hill, D. L. G.; Hajnal, J. V.; Edwards, A. D.; Rutherford, M. A.; Gillies, D. F.; Rueckert, D.

2006-03-01

Pre-term birth is strongly associated with subsequent neuropsychiatric impairment. To identify structural differences in preterm infants we have examined a dataset of magnetic resonance (MR) images containing 88 preterm infants and 19 term born controls. We have analyzed these images by combining image registration, deformation based morphometry (DBM), multivariate statistics, and effect size maps (ESM). The methodology described has been performed directly on the MR intensity images rather than on segmented versions of the images. The results indicate that the approach described makes clear the statistical differences between the control and preterm samples, showing a leave-one-out classification accuracy of 94.74% and 95.45% respectively. In addition, finding the most discriminant direction between the groups and using DBM features and ESM we are able to identify not only what are the changes between preterm and term groups but also how relatively relevant they are in terms of volume expansion and contraction.

Hybrid registration of PET/CT in thoracic region with pre-filtering PET sinogram

NASA Astrophysics Data System (ADS)

Mokri, S. S.; Saripan, M. I.; Marhaban, M. H.; Nordin, A. J.; Hashim, S.

2015-11-01

The integration of physiological (PET) and anatomical (CT) images in cancer delineation requires an accurate spatial registration technique. Although hybrid PET/CT scanner is used to co-register these images, significant misregistrations exist due to patient and respiratory/cardiac motions. This paper proposes a hybrid feature-intensity based registration technique for hybrid PET/CT scanner. First, simulated PET sinogram was filtered with a 3D hybrid mean-median before reconstructing the image. The features were then derived from the segmented structures (lung, heart and tumor) from both images. The registration was performed based on modified multi-modality demon registration with multiresolution scheme. Apart from visual observations improvements, the proposed registration technique increased the normalized mutual information index (NMI) between the PET/CT images after registration. All nine tested datasets show marked improvements in mutual information (MI) index than free form deformation (FFD) registration technique with the highest MI increase is 25%.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Markel, D; Levesque, I R; Research Institute of the McGill University Health Centre, Montreal, QC

Segmentation and registration of medical imaging data are two processes that can be integrated (a process termed regmentation) to iteratively reinforce each other, potentially improving efficiency and overall accuracy. A significant challenge is presented when attempting to validate the joint process particularly with regards to minimizing geometric uncertainties associated with the ground truth while maintaining anatomical realism. This work demonstrates a 4D MRI, PET, and CT compatible tissue phantom with a known ground truth for evaluating registration and segmentation accuracy. The phantom consists of a preserved swine lung connected to an air pump via a PVC tube for inflation. Mockmore » tumors were constructed from sea sponges contained within two vacuum-sealed compartments with catheters running into each one for injection of radiotracer solution. The phantom was scanned using a GE Discovery-ST PET/CT scanner and a 0.23T Phillips MRI, and resulted in anatomically realistic images. A bifurcation tracking algorithm was implemented to provide a ground truth for evaluating registration accuracy. This algorithm was validated using known deformations of up to 7.8 cm using a separate CT scan of a human thorax. Using the known deformation vectors to compare against, 76 bifurcation points were selected. The tracking accuracy was found to have maximum mean errors of −0.94, 0.79 and −0.57 voxels in the left-right, anterior-posterior and inferior-superior directions, respectively. A pneumatic control system is under development to match the respiratory profile of the lungs to a breathing trace from an individual patient.« less

Shock waves from non-spherically collapsing cavitation bubbles

NASA Astrophysics Data System (ADS)

Supponen, Outi; Obreschkow, Danail; Farhat, Mohamed

2017-11-01

Combining simultaneous high-speed imaging and hydrophone measurements, we uncover details of the multiple shock wave emission from laser-induced cavitation bubbles collapsing in a non-spherical way. For strongly deformed bubbles collapsing near a free surface, we identify the distinct shock waves caused by the jet impact onto the opposite bubble wall and by the individual collapses of the remaining bubble segments. The energy carried by each of these shocks depends on the level of bubble deformation, quantified by the anisotropy parameter ζ, the dimensionless equivalent of the Kelvin impulse. For jetting bubbles, at ζ < 0.01 , the jet impact hammer pressure is found to be the most energetic shock. Through statistical analysis of the experimental data and theoretical derivations, and by comparing bubbles deformed by different sources (variable gravity achieved on parabolic flights, and neighboring free and rigid surfaces), we find that the shock peak pressure may be approximated as the jet impact-induced water hammer as ph = 0.45 (ρc2 Δp) 1 / 2ζ-1 .

A review of biomechanically informed breast image registration

NASA Astrophysics Data System (ADS)

Hipwell, John H.; Vavourakis, Vasileios; Han, Lianghao; Mertzanidou, Thomy; Eiben, Björn; Hawkes, David J.

2016-01-01

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

Automated extraction of pleural effusion in three-dimensional thoracic CT images

NASA Astrophysics Data System (ADS)

Kido, Shoji; Tsunomori, Akinori

2009-02-01

It is important for diagnosis of pulmonary diseases to measure volume of accumulating pleural effusion in threedimensional thoracic CT images quantitatively. However, automated extraction of pulmonary effusion correctly is difficult. Conventional extraction algorithm using a gray-level based threshold can not extract pleural effusion from thoracic wall or mediastinum correctly, because density of pleural effusion in CT images is similar to those of thoracic wall or mediastinum. So, we have developed an automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion. Our method used a template of lung obtained from a normal lung for segmentation of lungs with pleural effusions. Registration process consisted of two steps. First step was a global matching processing between normal and abnormal lungs of organs such as bronchi, bones (ribs, sternum and vertebrae) and upper surfaces of livers which were extracted using a region-growing algorithm. Second step was a local matching processing between normal and abnormal lungs which were deformed by the parameter obtained from the global matching processing. Finally, we segmented a lung with pleural effusion by use of the template which was deformed by two parameters obtained from the global matching processing and the local matching processing. We compared our method with a conventional extraction method using a gray-level based threshold and two published methods. The extraction rates of pleural effusions obtained from our method were much higher than those obtained from other methods. Automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion is promising for diagnosis of pulmonary diseases by providing quantitative volume of accumulating pleural effusion.

Figure-Ground Segmentation Using Factor Graphs

PubMed Central

Shen, Huiying; Coughlan, James; Ivanchenko, Volodymyr

2009-01-01

Foreground-background segmentation has recently been applied [26,12] to the detection and segmentation of specific objects or structures of interest from the background as an efficient alternative to techniques such as deformable templates [27]. We introduce a graphical model (i.e. Markov random field)-based formulation of structure-specific figure-ground segmentation based on simple geometric features extracted from an image, such as local configurations of linear features, that are characteristic of the desired figure structure. Our formulation is novel in that it is based on factor graphs, which are graphical models that encode interactions among arbitrary numbers of random variables. The ability of factor graphs to express interactions higher than pairwise order (the highest order encountered in most graphical models used in computer vision) is useful for modeling a variety of pattern recognition problems. In particular, we show how this property makes factor graphs a natural framework for performing grouping and segmentation, and demonstrate that the factor graph framework emerges naturally from a simple maximum entropy model of figure-ground segmentation. We cast our approach in a learning framework, in which the contributions of multiple grouping cues are learned from training data, and apply our framework to the problem of finding printed text in natural scenes. Experimental results are described, including a performance analysis that demonstrates the feasibility of the approach. PMID:20160994

A New Perspective on Fault Geometry and Slip Distribution of the 2009 Dachaidan Mw 6.3 Earthquake from InSAR Observations

PubMed Central

Liu, Yang; Xu, Caijun; Wen, Yangmao; Fok, Hok Sum

2015-01-01

On 28 August 2009, the northern margin of the Qaidam basin in the Tibet Plateau was ruptured by an Mw 6.3 earthquake. This study utilizes the Envisat ASAR images from descending Track 319 and ascending Track 455 for capturing the coseismic deformation resulting from this event, indicating that the earthquake fault rupture does not reach to the earth’s surface. We then propose a four-segmented fault model to investigate the coseismic deformation by determining the fault parameters, followed by inverting slip distribution. The preferred fault model shows that the rupture depths for all four fault planes mainly range from 2.0 km to 7.5 km, comparatively shallower than previous results up to ~13 km, and that the slip distribution on the fault plane is complex, exhibiting three slip peaks with a maximum of 2.44 m at a depth between 4.1 km and 4.9 km. The inverted geodetic moment is 3.85 × 1018 Nm (Mw 6.36). The 2009 event may rupture from the northwest to the southeast unilaterally, reaching the maximum at the central segment. PMID:26184210

A New Perspective on Fault Geometry and Slip Distribution of the 2009 Dachaidan Mw 6.3 Earthquake from InSAR Observations.

PubMed

Liu, Yang; Xu, Caijun; Wen, Yangmao; Fok, Hok Sum

2015-07-10

On 28 August 2009, the northern margin of the Qaidam basin in the Tibet Plateau was ruptured by an Mw 6.3 earthquake. This study utilizes the Envisat ASAR images from descending Track 319 and ascending Track 455 for capturing the coseismic deformation resulting from this event, indicating that the earthquake fault rupture does not reach to the earth's surface. We then propose a four-segmented fault model to investigate the coseismic deformation by determining the fault parameters, followed by inverting slip distribution. The preferred fault model shows that the rupture depths for all four fault planes mainly range from 2.0 km to 7.5 km, comparatively shallower than previous results up to ~13 km, and that the slip distribution on the fault plane is complex, exhibiting three slip peaks with a maximum of 2.44 m at a depth between 4.1 km and 4.9 km. The inverted geodetic moment is 3.85 × 10(18) Nm (Mw 6.36). The 2009 event may rupture from the northwest to the southeast unilaterally, reaching the maximum at the central segment.

Characterization of microcracks by application of digital image correlation to SPM images

NASA Astrophysics Data System (ADS)

Keller, Juergen; Gollhardt, Astrid; Vogel, Dietmar; Michel, Bernd

2004-07-01

With the development of micro- and nanotechnological products such as sensors, MEMS/NEMS and their broad application in a variety of market segments new reliability issues will arise. The increasing interface-to-volume ratio in highly integrated systems and nanoparticle filled materials and unsolved questions of size effect of nanomaterials are challenges for experimental reliability evaluation. To fulfill this needs the authors developed the nanoDAC method (nano Deformation Analysis by Correlation), which allows the determination and evaluation of 2D displacement fields based on scanning probe microscopy (SPM) data. In-situ SPM scans of the analyzed object are carried out at different thermo-mechanical load states. The obtained topography-, phase- or error-images are compared utilizing grayscale cross correlation algorithms. This allows the tracking of local image patterns of the analyzed surface structure. The measurement results of the nanoDAC method are full-field displacement and strain fields. Due to the application of SPM equipment deformations in the micro-, nanometer range can be easily detected. The method can be performed on bulk materials, thin films and on devices i.e microelectronic components, sensors or MEMS/NEMS. Furthermore, the characterization and evaluation of micro- and nanocracks or defects in bulk materials, thin layers and at material interfaces can be carried out.

3D displacement time series in the Afar rift zone computed from SAR phase and amplitude information

NASA Astrophysics Data System (ADS)

Casu, Francesco; Manconi, Andrea

2013-04-01

Large and rapid deformations, such as those caused by earthquakes, eruptions, and landslides cannot be fully measured by using standard DInSAR applications. Indeed, the phase information often degrades and some areas of the interferograms are affected by high fringe rates, leading to difficulties in the phase unwrapping, and/or to complete loss of coherence due to significant misregistration errors. This limitation can be overcome by exploiting the SAR image amplitude information instead of the phase, and by calculating the Pixel-Offset (PO) field SAR image pairs, for both range and azimuth directions. Moreover, it is possible to combine the PO results by following the same rationale of the SBAS technique, to finally retrieve the offset-based deformation time series. Such technique, named PO-SBAS, permits to retrieve the deformation field in areas affected by very large displacements at an accuracy that, for ENVISAT data, correspond to 30 cm and 15 cm for the range and azimuth, respectively [1]. Moreover, the combination of SBAS and PO-SBAS time series can help to better study and model deformation phenomena characterized by spatial and temporal heterogeneities [2]. The Dabbahu rift segment of the Afar depression has been active since 2005 when a 2.5 km3 dyke intrusion and hundreds of earthquakes marked the onset a rifting episode which continues to date. The ENVISAT satellite has repeatedly imaged the Afar depression since 2003, generating a large SAR archive. In this work, we study the Afar rift region deformations by using both the phase and amplitude information of several sets of SAR images acquired from ascending and descending ENVISAT tracks. We combined sets of small baseline interferograms through the SBAS algorithm, and we generate both ground deformation maps and time series along the satellite Line-Of-Sight (LOS). In areas where the deformation gradient causes loss of coherence, we retrieve the displacement field through the amplitude information. Furthermore, we could also retrieve the full 3D deformation field, by considering the North-South displacement component obtained from the azimuth PO information. The combination of SBAS and PO-SBAS information permits to better retrieve and constrain the full deformation field due to repeated intrusions, fault movements, as well as the magma movements from individual magma chambers. [1] Casu, F., A. Manconi, A. Pepe and R. Lanari, 2011. Deformation time-series generation in areas characterized by large displacement dynamics: the SAR amplitude Pixel-Offset SBAS technique, IEEE Transaction on Geosciences and Remote Sensing. [2] Manconi, A. and F. Casu, 2012. Joint analysis of displacement time series retrieved from SAR phase and amplitude: impact on the estimation of volcanic source parameters, Geophysical Research Letters, doi:10.1029/2012GL052202.

Evolution of the Puente Hills Thrust Fault

NASA Astrophysics Data System (ADS)

Bergen, K. J.; Shaw, J. H.; Dolan, J. F.

2013-12-01

This study aims to assess the evolution of the blind Puente Hills thrust fault system (PHT) by determining its age of initiation, lateral propagation history, and changes in slip rate over time. The PHT presents one of the largest seismic hazards in the United States, given its location beneath downtown Los Angeles. The PHT is comprised of three fault segments: the Los Angeles (LA), Santa Fe Springs (SFS), and Coyote Hills (CH). The LA and SFS segments are characterized by growth stratigraphy where folds formed by uplift on the fault segments have been continually buried by sediment from the Los Angeles and San Gabriel rivers. The CH segment has developed topography and is characterized by onlapping growth stratigraphy. This depositional setting gives us the unique opportunity to measure uplift on the LA and SFS fault segments, and minimum uplift on the CH fault segment, as the difference in sediment thicknesses across the buried folds. We utilize depth converted oil industry seismic reflection data to image the fold geometries. Identifying time-correlative stratigraphic markers for slip rate determination in the basin has been a problem for researchers in the past, however, as the faunal assemblages observed in wells are time-transgressive by nature. To overcome this, we utilize the sequence stratigraphic model and well picks of Ponti et al. (2007) as a basis for mapping time-correlative sequence boundaries throughout our industry seismic reflection data from the present to the Pleistocene. From the Pleistocene to Miocene we identify additional sequence boundaries in our seismic reflection data from imaged sequence geometries and by correlating industry well formation tops. The sequence and formation top picks are then used to build 3-dimensional surfaces in the modeling program Gocad. From these surfaces we measure the change in thicknesses across the folds to obtain uplift rates between each sequence boundary. Our results show three distinct phases of deformation on the LA and SFS segments: an early period characterized by fault-propagation or structural wedge kinematics that terminates in the early Pleistocene, followed by a period of quiescence. The faults were subsequently reactivated in the middle Pleistocene and propagated upward to detachments, with the deformation characterized by fold-bend folding kinematics. Slip on the LA segment decreases to the West, suggesting lateral growth in that direction. Our work highlights the need to assess along-strike variability in slip rate when assessing the seismic hazard of a compressional fault, as marginal sites may significantly underestimate fault activity. Ponti, D. J. et al. A 3-Dimensional Model of Water-Bearing Sequences in the Dominguez Gap Region, Long Beach, California. US Geological Survey Open-File Report 1013 (2007).

Changes in Right Ventricular Shape and Deformation Following Coronary Artery Bypass Surgery-Insights from Echocardiography with Strain Rate and Magnetic Resonance Imaging.

PubMed

Rösner, Assami; Avenarius, Derk; Malm, Siri; Iqbal, Amjid; Schirmer, Henrik; Bijnens, Bart; Myrmel, Truls

2015-12-01

This study was designed to assess whether altered RV geometry and deformation parameters persisted well into the recovery period after presumably uncomplicated coronary artery bypass grafting (CABG). It was our hypothesis that the altered geometry of and load in the RV following pericardial opening would change both regional and global deformation indices for an extensive period postoperatively. Fifty-seven patients scheduled for CABG underwent preoperative and 8-10 months postoperative magnetic resonance imaging (MRI) for RV volume measurements, and resting echocardiography with assessment of geometry and RV mechanical function determined by tissue Doppler imaging (TDI) based longitudinal strain. Both MRI and echocardiography revealed postoperative dilatation of the RV apex, shortened longitudinal RV length but unchanged RV ejection fraction. Echocardiography parameters associated with filling of the right atrium showed signs of constraint with a reduced systolic filling fraction and increased right atrial size. Right ventricular segmental strain (-20 ± 13% vs. -29 ± 20% preoperatively; mean ±SD, P < 0.0001) was reduced postoperatively in parallel with TAPSE (1.3 ± 0.3 cm vs. 2.2 ± 0.4 cm; P < 0.0001). Post-CABG longitudinal motion of the RV lateral wall is reduced after uneventful CABG despite preserved RV ejection fraction and stroke volume. The discrepancy in various RV systolic performance indicators results from increased sphericity of the RV following opening the pericardium during surgery. Therefore, longitudinal functional parameters may underestimate RV systolic function for at least 8-10 months post-CABG. Changes in deformation parameters should thus always be interpreted in relation to changes in geometry. © 2015, Wiley Periodicals, Inc.

Lagrangian displacement tracking using a polar grid between endocardial and epicardial contours for cardiac strain imaging.

PubMed

Ma, Chi; Varghese, Tomy

2012-04-01

Accurate cardiac deformation analysis for cardiac displacement and strain imaging over time requires Lagrangian description of deformation of myocardial tissue structures. Failure to couple the estimated displacement and strain information with the correct myocardial tissue structures will lead to erroneous result in the displacement and strain distribution over time. Lagrangian based tracking in this paper divides the tissue structure into a fixed number of pixels whose deformation is tracked over the cardiac cycle. An algorithm that utilizes a polar-grid generated between the estimated endocardial and epicardial contours for cardiac short axis images is proposed to ensure Lagrangian description of the pixels. Displacement estimates from consecutive radiofrequency frames were then mapped onto the polar grid to obtain a distribution of the actual displacement that is mapped to the polar grid over time. A finite element based canine heart model coupled with an ultrasound simulation program was used to verify this approach. Segmental analysis of the accumulated displacement and strain over a cardiac cycle demonstrate excellent agreement between the ideal result obtained directly from the finite element model and our Lagrangian approach to strain estimation. Traditional Eulerian based estimation results, on the other hand, show significant deviation from the ideal result. An in vivo comparison of the displacement and strain estimated using parasternal short axis views is also presented. Lagrangian displacement tracking using a polar grid provides accurate tracking of myocardial deformation demonstrated using both finite element and in vivo radiofrequency data acquired on a volunteer. In addition to the cardiac application, this approach can also be utilized for transverse scans of arteries, where a polar grid can be generated between the contours delineating the outer and inner wall of the vessels from the blood flowing though the vessel.

Quantitation of stress echocardiography by tissue Doppler and strain rate imaging: a dream come true?

PubMed

Galderisi, Maurizio; Mele, Donato; Marino, Paolo Nicola

2005-01-01

Tissue Doppler (TD) is an ultrasound tool providing a quantitative agreement of left ventricular regional myocardial function in different modalities. Spectral pulsed wave (PW) TD, performed online during the examination, measures instantaneous myocardial velocities. By means of color TD, velocity images are digitally stored for subsequent off-line analysis and mean myocardial velocities are measured. An implementation of color TD includes strain rate imaging (SRI), based on post-processing conversion of regional velocities in local myocardial deformation rate (strain rate) and percent deformation (strain). These three modalities have been applied to stress echocardiography for quantitative evaluation of regional left ventricular function and detection of ischemia and viability. They present advantages and limitations. PWTD does not permit the simultaneous assessment of multiple walls and therefore is not compatible with clinical stress echocardiography while it could be used in a laboratory setting. Color TD provides a spatial map of velocity throughout the myocardium but its results are strongly affected by the frame rate. Both color TD and PWTD are also influenced by overall cardiac motion and tethering from adjacent segments and require reference velocity values for interpretation of regional left ventricular function. High frame rate (i.e. > 150 ms) post-processing-derived SRI can potentially overcome these limitations, since measurements of myocardial deformation have not any significant apex-to-base gradient. Preliminary studies have shown encouraging results about the ability of SRI to detect ischemia and viability, in terms of both strain rate changes and/or evidence of post-systolic thickening. SRI is, however, Doppler-dependent and time-consuming. Further technical refinements are needed to improve its application and introduce new ultrasound modalities to overcome the limitations of the Doppler-derived deformation analysis.

Surface Creep Rate and Moment Accumulation Rate Along the Aceh Segment of the Sumatran Fault From L-band ALOS-1/PALSAR-1 Observations

NASA Astrophysics Data System (ADS)

Tong, X.; Sandwell, D. T.; Schmidt, D. A.

2018-04-01

We analyzed the interferometric synthetic aperture radar data from the ALOS-1/PALSAR-1 satellite to image the interseismic deformation along the Sumatran fault. The interferometric synthetic aperture radar time series analysis reveals up to 20 mm/year of aseismic creep on the Aceh segment along the Northern Sumatran fault. This is a large fraction of the total slip rate across this fault. The spatial extent of the aseismic creep extends for 100 km. The along-strike variation of the aseismic creep has an inverse "U" shape. An analysis of the moment accumulation rate shows that the central part of the creeping section accumulates moment at approximately 50% of the rate of the surrounding locked segments. An initial analysis of temporal variations suggests that the creep rate may be decelerating with time, suggesting that the creep rate is adjusting to a stress perturbation from nearby seismic activity. Our study has implications to the earthquake hazard along the northern Sumatran fault.

The meter-class carbon fiber reinforced polymer mirror and segmented mirror telescope at the Naval Postgraduate School

NASA Astrophysics Data System (ADS)

Wilcox, Christopher; Fernandez, Bautista; Bagnasco, John; Martinez, Ty; Romeo, Robert; Agrawal, Brij

2015-03-01

The Adaptive Optics Center of Excellence for National Security at the Naval Postgraduate School has implemented a technology testing platform and array of facilities for next-generation space-based telescopes and imaging system development. The Segmented Mirror Telescope is a 3-meter, 6 segment telescope with actuators on its mirrors for system optical correction. Currently, investigation is being conducted in the use of lightweight carbon fiber reinforced polymer structures for large monolithic optics. Advantages of this material include lower manufacturing costs, very low weight, and high durability and survivability compared to its glass counterparts. Design and testing has begun on a 1-meter, optical quality CFRP parabolic mirror for the purpose of injecting collimated laser light through the SMT primary and secondary mirrors as well as the following aft optics that include wavefront sensors and deformable mirrors. This paper will present the design, testing, and usage of this CFRP parabolic mirror and the current path moving forward with this ever-evolving technology.

Correcting for the effects of pupil discontinuities with the ACAD method

NASA Astrophysics Data System (ADS)

Mazoyer, Johan; Pueyo, Laurent; N'Diaye, Mamadou; Mawet, Dimitri; Soummer, Rémi; Norman, Colin

2016-07-01

The current generation of ground-based coronagraphic instruments uses deformable mirrors to correct for phase errors and to improve contrast levels at small angular separations. Improving these techniques, several space and ground based instruments are currently developed using two deformable mirrors to correct for both phase and amplitude errors. However, as wavefront control techniques improve, more complex telescope pupil geometries (support structures, segmentation) will soon be a limiting factor for these next generation coronagraphic instruments. The technique presented in this proceeding, the Active Correction of Aperture Discontinuities method, is taking advantage of the fact that most future coronagraphic instruments will include two deformable mirrors, and is proposing to find the shapes and actuator movements to correct for the effect introduced by these complex pupil geometries. For any coronagraph previously designed for continuous apertures, this technique allow to obtain similar performance in contrast with a complex aperture (with segmented and secondary mirror support structures), with high throughput and flexibility to adapt to changing pupil geometry (e.g. in case of segment failure or maintenance of the segments). We here present the results of the parametric analysis realized on the WFIRST pupil for which we obtained high contrast levels with several deformable mirror setups (size, separation between them), coronagraphs (Vortex charge 2, vortex charge 4, APLC) and spectral bandwidths. However, because contrast levels and separation are not the only metrics to maximize the scientific return of an instrument, we also included in this study the influence of these deformable mirror shapes on the throughput of the instrument and sensitivity to pointing jitters. Finally, we present results obtained on another potential space based telescope segmented aperture. The main result of this proceeding is that we now obtain comparable performance than the coronagraphs previously designed for WFIRST. First result from the parametric analysis strongly suggest that the 2 deformable mirror set up (size and distance between them) have a important impact on the performance in contrast and throughput of the final instrument.

Technical Note: SCUDA: A software platform for cumulative dose assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, Seyoun; McNutt, Todd; Quon, Harry

Purpose: Accurate tracking of anatomical changes and computation of actually delivered dose to the patient are critical for successful adaptive radiation therapy (ART). Additionally, efficient data management and fast processing are practically important for the adoption in clinic as ART involves a large amount of image and treatment data. The purpose of this study was to develop an accurate and efficient Software platform for CUmulative Dose Assessment (SCUDA) that can be seamlessly integrated into the clinical workflow. Methods: SCUDA consists of deformable image registration (DIR), segmentation, dose computation modules, and a graphical user interface. It is connected to our imagemore » PACS and radiotherapy informatics databases from which it automatically queries/retrieves patient images, radiotherapy plan, beam data, and daily treatment information, thus providing an efficient and unified workflow. For accurate registration of the planning CT and daily CBCTs, the authors iteratively correct CBCT intensities by matching local intensity histograms during the DIR process. Contours of the target tumor and critical structures are then propagated from the planning CT to daily CBCTs using the computed deformations. The actual delivered daily dose is computed using the registered CT and patient setup information by a superposition/convolution algorithm, and accumulated using the computed deformation fields. Both DIR and dose computation modules are accelerated by a graphics processing unit. Results: The cumulative dose computation process has been validated on 30 head and neck (HN) cancer cases, showing 3.5 ± 5.0 Gy (mean±STD) absolute mean dose differences between the planned and the actually delivered doses in the parotid glands. On average, DIR, dose computation, and segmentation take 20 s/fraction and 17 min for a 35-fraction treatment including additional computation for dose accumulation. Conclusions: The authors developed a unified software platform that provides accurate and efficient monitoring of anatomical changes and computation of actually delivered dose to the patient, thus realizing an efficient cumulative dose computation workflow. Evaluation on HN cases demonstrated the utility of our platform for monitoring the treatment quality and detecting significant dosimetric variations that are keys to successful ART.« less

Image based cardiac acceleration map using statistical shape and 3D+t myocardial tracking models; in-vitro study on heart phantom

NASA Astrophysics Data System (ADS)

Pashaei, Ali; Piella, Gemma; Planes, Xavier; Duchateau, Nicolas; de Caralt, Teresa M.; Sitges, Marta; Frangi, Alejandro F.

2013-03-01

It has been demonstrated that the acceleration signal has potential to monitor heart function and adaptively optimize Cardiac Resynchronization Therapy (CRT) systems. In this paper, we propose a non-invasive method for computing myocardial acceleration from 3D echocardiographic sequences. Displacement of the myocardium was estimated using a two-step approach: (1) 3D automatic segmentation of the myocardium at end-diastole using 3D Active Shape Models (ASM); (2) propagation of this segmentation along the sequence using non-rigid 3D+t image registration (temporal di eomorphic free-form-deformation, TDFFD). Acceleration was obtained locally at each point of the myocardium from local displacement. The framework has been tested on images from a realistic physical heart phantom (DHP-01, Shelley Medical Imaging Technologies, London, ON, CA) in which the displacement of some control regions was known. Good correlation has been demonstrated between the estimated displacement function from the algorithms and the phantom setup. Due to the limited temporal resolution, the acceleration signals are sparse and highly noisy. The study suggests a non-invasive technique to measure the cardiac acceleration that may be used to improve the monitoring of cardiac mechanics and optimization of CRT.

System Estimates Radius of Curvature of a Segmented Mirror

NASA Technical Reports Server (NTRS)

Rakoczy, John

2008-01-01

A system that estimates the global radius of curvature (GRoC) of a segmented telescope mirror has been developed for use as one of the subsystems of a larger system that exerts precise control over the displacements of the mirror segments. This GRoC-estimating system, when integrated into the overall control system along with a mirror-segment- actuation subsystem and edge sensors (sensors that measure displacements at selected points on the edges of the segments), makes it possible to control the GROC mirror-deformation mode, to which mode contemporary edge sensors are insufficiently sensitive. This system thus makes it possible to control the GRoC of the mirror with sufficient precision to obtain the best possible image quality and/or to impose a required wavefront correction on incoming or outgoing light. In its mathematical aspect, the system utilizes all the information available from the edge-sensor subsystem in a unique manner that yields estimates of all the states of the segmented mirror. The system does this by exploiting a special set of mirror boundary conditions and mirror influence functions in such a way as to sense displacements in degrees of freedom that would otherwise be unobservable by means of an edge-sensor subsystem, all without need to augment the edge-sensor system with additional metrological hardware. Moreover, the accuracy of the estimates increases with the number of mirror segments.

Automatic joint alignment measurements in pre- and post-operative long leg standing radiographs.

PubMed

Goossen, A; Weber, G M; Dries, S P M

2012-01-01

For diagnosis or treatment assessment of knee joint osteoarthritis it is required to measure bone morphometry from radiographic images. We propose a method for automatic measurement of joint alignment from pre-operative as well as post-operative radiographs. In a two step approach we first detect and segment any implants or other artificial objects within the image. We exploit physical characteristics and avoid prior shape information to cope with the vast amount of implant types. Subsequently, we exploit the implant delineations to adapt the initialization and adaptation phase of a dedicated bone segmentation scheme using deformable template models. Implant and bone contours are fused to derive the final joint segmentation and thus the alignment measurements. We evaluated our method on clinical long leg radiographs and compared both the initialization rate, corresponding to the number of images successfully processed by the proposed algorithm, and the accuracy of the alignment measurement. Ground truth has been generated by an experienced orthopedic surgeon. For comparison a second reader reevaluated the measurements. Experiments on two sets of 70 and 120 digital radiographs show that 92% of the joints could be processed automatically and the derived measurements of the automatic method are comparable to a human reader for pre-operative as well as post-operative images with a typical error of 0.7° and correlations of r = 0.82 to r = 0.99 with the ground truth. The proposed method allows deriving objective measures of joint alignment from clinical radiographs. Its accuracy and precision are on par with a human reader for all evaluated measurements.

One-stage posterior-only lumbosacral hemivertebra resection with short segmental fusion: a more than 2-year follow-up.

PubMed

Zhuang, Qianyu; Zhang, Jianguo; Li, Shugang; Wang, Shengru; Guo, Jianwei; Qiu, Guixing

2016-05-01

Lumbosacral hemivertebra poses a unique problem because it can cause gross imbalance and progressive compensatory thoracolumbar deformity. Previous studies have reported lumbosacral hemivertebra resection through a combined anterior and posterior approach, but there have been no reports on the results and complications of hemivertebra resection via a posterior-only procedure and short fusion with large series of patients. This retrospective study of a prospective collected database comprises a consecutive series of 14 congenital scoliosis due to lumbosacral hemivertebra treated by 1-stage posterior hemivertebra resection with short segmental fusion, with at least a 2-year follow-up period (24-144 months). Surgical reports and patient charts were reviewed. Radiographic evaluation included measured changes in segmental scoliosis and lordosis, compensatory scoliosis, thoracic kyphosis, lumbar lordosis, and trunk shift. Quality of life data from Scoliosis Research Society (SRS)-22 questionnaires were also collected. Our results showed that the mean follow-up period was 38.4 months. The mean fusion level was 3.2 segments. Mean operation time was 207.8 min with the average blood loss of 235.7 ml. The mean segmental scoliosis was 30° preoperatively, 5° postoperatively (83 % correction rate), and 4° (87 %) at the latest follow-up. The compensatory coronal curve of 30° was spontaneously corrected to 13° at most recent follow-up. Trunk shift was significantly improved on both coronal (63 %) and sagittal plane (58 %) after the surgery, and kept stable during the follow-up. The total SRS-22 score, the self-image domain score and the satisfaction domain score demonstrated significant improvement compared with preoperative status. Only one intra-operative complication was observed, a pedicle fracture. In summary, our results showed that one-stage HV resection and short segment fusion by a posterior approach can offer excellent scoliosis correction and trunk shift improvement without neurological complications, while saving motion segments as much as possible. This strategy is not only corrective of the deformity but also preventive of compensatory curve progression, thus avoiding long lumbar fusion.

Performance of lead-free versus lead-based hunting ammunition in ballistic soap.

PubMed

Gremse, Felix; Krone, Oliver; Thamm, Mirko; Kiessling, Fabian; Tolba, René Hany; Rieger, Siegfried; Gremse, Carl

2014-01-01

Lead-free hunting bullets are an alternative to lead-containing bullets which cause health risks for humans and endangered scavenging raptors through lead ingestion. However, doubts concerning the effectiveness of lead-free hunting bullets hinder the wide-spread acceptance in the hunting and wildlife management community. We performed terminal ballistic experiments under standardized conditions with ballistic soap as surrogate for game animal tissue to characterize dimensionally stable, partially fragmenting, and deforming lead-free bullets and one commonly used lead-containing bullet. The permanent cavities created in soap blocks are used as a measure for the potential wound damage. The soap blocks were imaged using computed tomography to assess the volume and shape of the cavity and the number of fragments. Shots were performed at different impact speeds, covering a realistic shooting range. Using 3D image segmentation, cavity volume, metal fragment count, deflection angle, and depth of maximum damage were determined. Shots were repeated to investigate the reproducibility of ballistic soap experiments. All bullets showed an increasing cavity volume with increasing deposited energy. The dimensionally stable and fragmenting lead-free bullets achieved a constant conversion ratio while the deforming copper and lead-containing bullets showed a ratio, which increases linearly with the total deposited energy. The lead-containing bullet created hundreds of fragments and significantly more fragments than the lead-free bullets. The deflection angle was significantly higher for the dimensionally stable bullet due to its tumbling behavior and was similarly low for the other bullets. The deforming bullets achieved higher reproducibility than the fragmenting and dimensionally stable bullets. The deforming lead-free bullet closely resembled the deforming lead-containing bullet in terms of energy conversion, deflection angle, cavity shape, and reproducibility, showing that similar terminal ballistic behavior can be achieved. Furthermore, the volumetric image processing allowed superior analysis compared to methods that involve cutting of the soap blocks.

Performance of Lead-Free versus Lead-Based Hunting Ammunition in Ballistic Soap

PubMed Central

Gremse, Felix; Krone, Oliver; Thamm, Mirko; Kiessling, Fabian; Tolba, René Hany; Rieger, Siegfried; Gremse, Carl

2014-01-01

Background Lead-free hunting bullets are an alternative to lead-containing bullets which cause health risks for humans and endangered scavenging raptors through lead ingestion. However, doubts concerning the effectiveness of lead-free hunting bullets hinder the wide-spread acceptance in the hunting and wildlife management community. Methods We performed terminal ballistic experiments under standardized conditions with ballistic soap as surrogate for game animal tissue to characterize dimensionally stable, partially fragmenting, and deforming lead-free bullets and one commonly used lead-containing bullet. The permanent cavities created in soap blocks are used as a measure for the potential wound damage. The soap blocks were imaged using computed tomography to assess the volume and shape of the cavity and the number of fragments. Shots were performed at different impact speeds, covering a realistic shooting range. Using 3D image segmentation, cavity volume, metal fragment count, deflection angle, and depth of maximum damage were determined. Shots were repeated to investigate the reproducibility of ballistic soap experiments. Results All bullets showed an increasing cavity volume with increasing deposited energy. The dimensionally stable and fragmenting lead-free bullets achieved a constant conversion ratio while the deforming copper and lead-containing bullets showed a ratio, which increases linearly with the total deposited energy. The lead-containing bullet created hundreds of fragments and significantly more fragments than the lead-free bullets. The deflection angle was significantly higher for the dimensionally stable bullet due to its tumbling behavior and was similarly low for the other bullets. The deforming bullets achieved higher reproducibility than the fragmenting and dimensionally stable bullets. Conclusion The deforming lead-free bullet closely resembled the deforming lead-containing bullet in terms of energy conversion, deflection angle, cavity shape, and reproducibility, showing that similar terminal ballistic behavior can be achieved. Furthermore, the volumetric image processing allowed superior analysis compared to methods that involve cutting of the soap blocks. PMID:25029572

Image processing and recognition for biological images

PubMed Central

Uchida, Seiichi

2013-01-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. PMID:23560739

Adolescent accessory navicular.

PubMed

Leonard, Zachary C; Fortin, Paul T

2010-06-01

Accessory tarsal navicular is a common anomaly in the human foot. It should be in the differential of medial foot pain. A proper history and physical, along with imaging modalities, can lead to the diagnosis. Often, classification of the ossicle and amount of morbidity guide treatment. Nonsurgical measures can provide relief. A variety of surgical procedures have been used with good results. Our preferred method is excision for small ossicles and segmental fusion after removal of the synchondrosis for large ossicles. In addition, pes planovalgus deformities need to be addressed concomitantly. Copyright 2010 Elsevier Inc. All rights reserved.

Geomorphic Evolution and Slip rate Measurements of the Noushki Segment , Chaman Fault Zone, Pakistan

NASA Astrophysics Data System (ADS)

Abubakar, Y.; Khan, S. D.; Owen, L. A.; Khan, A.

2012-12-01

The Nushki segment of the Chaman fault system is unique in its nature as it records both the imprints of oblique convergence along the western Indian Plate boundary as well as the deformation along the Makran subduction zone. The left-lateral Chaman transform zone has evolved from a subduction zone along the Arabian-Eurasian collision complex to a strike-slip fault system since the collision of the Indian Plate with the Eurasia. The geodetically and geologically constrained displacement rates along the Chaman fault varies from about 18 mm/yr to about 35 mm/yr respectively throughout its total length of ~ 860 km. Two major hypothesis has been proposed by workers for these variations; i) Variations in rates of elastic strain accumulation along the plate boundary and, ii) strain partitioning along the plate boundary. Morphotectonic analysis is a very useful tool in investigations of spatial variations in tectonic activities both regionally and locally. This work uses morphotectonic analysis to investigate the degree of variations in active tectonic deformation, which can be directly related to elastic strain accumulation and other kinematics in the western boundary of the plate margin. Geomorphic mapping was carried out using remotely sensed data. ASTER and RADAR data were used in establishing Quaternary stratigraphy and measurement of geomorphic indices such as stream length gradient index, valley floor width to height ratio and, river/stream longitudinal profile within the study area. High resolution satellite images (e.g., IKONOS imagery) and 30m ASTER DEMs were employed to measure displacement recorded by landforms along individual strands of the fault. Results from geomorphic analysis shows three distinct levels of tectonic deformation. Areas showing high levels of tectonic deformation are characterized by displaced fan surfaces, deflected streams and beheaded streams. Terrestrial Cosmogenic nuclide surface exposure dating of the displaced landforms is being carried out to calculate slip-rates. Slip-rates estimation along this segment of this plate boundary will help in understanding of tectonic evolution of this plate boundary and seismic activity in the region.

Surface displacement based shape analysis of central brain structures in preterm-born children

NASA Astrophysics Data System (ADS)

Garg, Amanmeet; Grunau, Ruth E.; Popuri, Karteek; Miller, Steven; Bjornson, Bruce; Poskitt, Kenneth J.; Beg, Mirza Faisal

2016-03-01

Many studies using T1 magnetic resonance imaging (MRI) data have found associations between changes in global metrics (e.g. volume) of brain structures and preterm birth. In this work, we use the surface displacement feature extracted from the deformations of the surface models of the third ventricle, fourth ventricle and brainstem to capture the variation in shape in these structures at 8 years of age that may be due to differences in the trajectory of brain development as a result of very preterm birth (24-32 weeks gestation). Understanding the spatial patterns of shape alterations in these structures in children who were born very preterm as compared to those who were born at full term may lead to better insights into mechanisms of differing brain development between these two groups. The T1 MRI data for the brain was acquired from children born full term (FT, n=14, 8 males) and preterm (PT, n=51, 22 males) at age 8-years. Accurate segmentation labels for these structures were obtained via a multi-template fusion based segmentation method. A high dimensional non-rigid registration algorithm was utilized to register the target segmentation labels to a set of segmentation labels defined on an average-template. The surface displacement data for the brainstem and the third ventricle were found to be significantly different (p < 0.05) between the PT and FT groups. Further, spatially localized clusters with inward and outward deformation were found to be associated with lower gestational age. The results from this study present a shape analysis method for pediatric MRI data and reveal shape changes that may be due to preterm birth.

Accurate segmentation framework for the left ventricle wall from cardiac cine MRI

NASA Astrophysics Data System (ADS)

Sliman, H.; Khalifa, F.; Elnakib, A.; Soliman, A.; Beache, G. M.; Gimel'farb, G.; Emam, A.; Elmaghraby, A.; El-Baz, A.

2013-10-01

We propose a novel, fast, robust, bi-directional coupled parametric deformable model to segment the left ventricle (LV) wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of LV wall to track the evolution of the parametric deformable models control points. To accurately estimate the marginal density of each deformable model control point, the empirical marginal grey level distributions (first-order appearance) inside and outside the boundary of the deformable model are modeled with adaptive linear combinations of discrete Gaussians (LCDG). The second order visual appearance of the LV wall is accurately modeled with a new rotationally invariant second-order Markov-Gibbs random field (MGRF). We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and AD value of 2.16±0.60 compared to two other level set methods that achieve 0.904±0.033 and 0.885±0.02 for DSC; and 2.86±1.35 and 5.72±4.70 for AD, respectively.

Mw7.7 2013 Balochistan Earthquake. Slip-Distribution and Deformation Field in Oblique Tectonic Context

NASA Astrophysics Data System (ADS)

Klinger, Y.; Vallage, A.; Grandin, R.; Delorme, A.; Rosu, A. M.; Pierro-Deseilligny, M.

2014-12-01

The Mw7.7 2013 Balochistan earthquake ruptured 200 km of the Hoshab fault, the southern end of the Chaman fault. Azimuth of the fault changes by more than 30° along rupture, from a well-oriented strike-slip fault to a more thrust prone direction. We use the MicMac optical image software to correlate pairs of Landsat images taken before and after the earthquake to access to the horizontal displacement field associated with the earthquake. We combine the horizontal displacement with radar image correlation in range and radar interferometry to derive the co-seismic slip on the fault. The combination of these different datasets actually provides the 3D displacement field. We note that although the earthquake was mainly strike-slip all along the rupture length, some vertical motion patches exist, which locations seem to be controlled by kilometric-scale variations of the fault geometry. 5 pairs of SPOT images were also correlated to derive a 2.5m pixel-size horizontal displacement field, providing unique opportunity to look at deformation in the near field and to obtain high-resolution strike-slip and normal slip-distributions. We note a significant difference, especially in the normal component, between the slip localized at depth on the fault plane and the slip localized closer to the surface, with more apparent slip at the surface. A high-resolution map of ground rupture allows us to locate the distribution of the deformation over the whole rupture length. The rupture map also highlights multiple fault geometric complexities where we could quantify details of the slip distribution. At the rupture length-scale, the local azimuth variations between segments have a large impact on the expression of the localized slip at the surface. The combination of those datasets gives an overview of the large distribution of the deformation in the near field, corresponding to the co-seismic damage zone.

Lithospheric buckling and intra-arc stresses: A mechanism for arc segmentation

NASA Technical Reports Server (NTRS)

Nelson, Kerri L.

1989-01-01

Comparison of segment development of a number of arcs has shown that consistent relationships between segmentation, volcanism and variable stresses exists. Researchers successfully modeled these relationships using the conceptual model of lithospheric buckling of Yamaoka et al. (1986; 1987). Lithosphere buckling (deformation) provides the needed mechanism to explain segmentation phenomenon; offsets in volcanic fronts, distribution of calderas within segments, variable segment stresses and the chemical diversity seen between segment boundary and segment interior magmas.

Accurate tracking of tumor volume change during radiotherapy by CT-CBCT registration with intensity correction

NASA Astrophysics Data System (ADS)

Park, Seyoun; Robinson, Adam; Quon, Harry; Kiess, Ana P.; Shen, Colette; Wong, John; Plishker, William; Shekhar, Raj; Lee, Junghoon

2016-03-01

In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician's contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70+/-2.30 (B-spline), 1.25+/-1.78 (demons), 0.93+/-1.14 (optical flow), and 4.39+/-3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.

On the feedback between forearc morphotectonics and megathrust earthquakes in subduction zones

NASA Astrophysics Data System (ADS)

Rosenau, M.; Oncken, O.

2008-12-01

An increasing number of observations suggest an intrinsic relationship between short- and long-term deformation processes in subduction zones. These include the global correlation between megathrust earthquake slip patterns with morphotectonic forearc features, the historical predominance of giant earthquakes (M > 9) along accretionary margins and the occurrence of (slow and shallow) tsunami earthquakes along erosive margins. To gain insight into the interplay between seismogenesis and tectonics in subduction settings we have developed a new modeling technique which joins analog and elastic dislocation approaches. Using elastoplastic wedges overlying a rate- and state-dependent interface, we demonstrate how analog earthquakes drive permanent wedge deformation consistent with the dynamic Coulomb wedge theory and how wedge deformation in turn controls basal "seismicity". During an experimental run, elastoplastic wedges evolve from those comparable to accretionary margins, characterized by plastic wedge shortening, to those mimicking erosive margins, characterized by minor plastic deformation. Permanent shortening localizes at the periphery of the "seismogenic" zone leading to a "morphotectonic" segmentation of the upper plate. Along with the evolving segmentation of the wedge, the magnitude- frequency relationship and recurrence distribution of analog earthquakes develop towards more periodic events of similar size (i.e. characteristic earthquakes). From the experiments we infer a positive feedback between short- and long-term deformation processes which tends to stabilize the spatiotemporal patterns of elastoplastic deformation in subduction settings. We suggest (1) that forearc anatomy reflects the distribution of seismic and aseismic slip at depth, (2) that morphotectonic segmentation assists the occurrence of more characteristic earthquakes, (3) that postseismic near-trench shortening relaxes coseismic compression by megathrust earthquakes and thus reduces tsunami earthquake risk in accretionary settings and (4) that permanent coastal shortening allows adjacent segments to fail more synchronized thus triggering much greater earthquakes in accretionary settings.

Geometry of the Arabia-Somalia Plate Boundary into Afar: Preliminary Results from the Seismic Profile Across the Asal Rift (Djibouti)

NASA Astrophysics Data System (ADS)

Vergne, J.; Doubre, C.; Mohamed, K.; Tiberi, C.; Leroy, S.; Maggi, A.

2010-12-01

In the Afar Depression, the Asal-Ghoubbet Rift in Djibouti is a young segment on land at the propagating tip of the Aden Ridge. This segment represents an ideal laboratory to observe the mechanisms of extension and the structural evolutions involved, from the continental break-up to the first stage of oceanic spreading. However, we lack first order information about the crustal and upper mantle structure in this region, which for example prevent detailed numerical modeling of the deformations observed at the surface from GPS or InSAR. Moreover the current permanent network is not well suited to precisely constrain the ratio of seismic/aseismic deformation and to characterize the active deformation and the rifting dynamics. Since November 2009 we have maintained a temporary network of 25 seismic stations deployed along a 150 km-long profile. Because we expect rapid variations of the lithospheric structure across the 10 km-wide central part of the rift, we gradually decreased the inter-stations spacing to less than 1 km in the middle section of the profile. In order to obtain a continuous image of the plate boundary, from the topographic surface to the upper mantle, several techniques and methods will be applied: P and S wave receiver functions, tomographies based on body waves, surface waves and seismic noise correlation, anisotropy, and finally a gravity-seismic joint inversion. We present some preliminary results deduced from the receiver functions applied to the data acquired during the first months of the experiment. We migrate several sets of receiver functions computed in various frequency bands to resolve both mantle interfaces and fine scale structures within the thin crust in the center of the rift. These first images confirm a rapid variation of the Moho depth on both sides of the rift and a very complex lithospheric structure in the central section with several low velocity zones within the top 50km that might correspond to magma lenses.

Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline.

PubMed

Gao, Shan; van 't Klooster, Ronald; Brandts, Anne; Roes, Stijntje D; Alizadeh Dehnavi, Reza; de Roos, Albert; Westenberg, Jos J M; van der Geest, Rob J

2017-01-01

To develop and evaluate a method that can fully automatically identify the vessel wall boundaries and quantify the wall thickness for both common carotid artery (CCA) and descending aorta (DAO) from axial magnetic resonance (MR) images. 3T MRI data acquired with T 1 -weighted gradient-echo black-blood imaging sequence from carotid (39 subjects) and aorta (39 subjects) were used to develop and test the algorithm. The vessel wall segmentation was achieved by respectively fitting a 3D cylindrical B-spline surface to the boundaries of lumen and outer wall. The tube-fitting was based on the edge detection performed on the signal intensity (SI) profile along the surface normal. To achieve a fully automated process, Hough Transform (HT) was developed to estimate the lumen centerline and radii for the target vessel. Using the outputs of HT, a tube model for lumen segmentation was initialized and deformed to fit the image data. Finally, lumen segmentation was dilated to initiate the adaptation procedure of outer wall tube. The algorithm was validated by determining: 1) its performance against manual tracing; 2) its interscan reproducibility in quantifying vessel wall thickness (VWT); 3) its capability of detecting VWT difference in hypertensive patients compared with healthy controls. Statistical analysis including Bland-Altman analysis, t-test, and sample size calculation were performed for the purpose of algorithm evaluation. The mean distance between the manual and automatically detected lumen/outer wall contours was 0.00 ± 0.23/0.09 ± 0.21 mm for CCA and 0.12 ± 0.24/0.14 ± 0.35 mm for DAO. No significant difference was observed between the interscan VWT assessment using automated segmentation for both CCA (P = 0.19) and DAO (P = 0.94). Both manual and automated segmentation detected significantly higher carotid (P = 0.016 and P = 0.005) and aortic (P < 0.001 and P = 0.021) wall thickness in the hypertensive patients. A reliable and reproducible pipeline for fully automatic vessel wall quantification was developed and validated on healthy volunteers as well as patients with increased vessel wall thickness. This method holds promise for helping in efficient image interpretation for large-scale cohort studies. 4 J. Magn. Reson. Imaging 2017;45:215-228. © 2016 International Society for Magnetic Resonance in Medicine.

A Minimal Path Searching Approach for Active Shape Model (ASM)-based Segmentation of the Lung.

PubMed

Guo, Shengwen; Fei, Baowei

2009-03-27

We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the normal direction. A minimal path (MP) deformable model is applied to drive the searching procedure. A statistical shape prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and manual segmentation is 1.75 ± 0.33 pixels, while the error is 1.99 ± 0.45 pixels for the ASM. Our results demonstrate that our ASM-MP method can accurately segment the lung on digital radiographs.

A minimal path searching approach for active shape model (ASM)-based segmentation of the lung

NASA Astrophysics Data System (ADS)

Guo, Shengwen; Fei, Baowei

2009-02-01

We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the normal direction. A minimal path (MP) deformable model is applied to drive the searching procedure. A statistical shape prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and manual segmentation is 1.75 +/- 0.33 pixels, while the error is 1.99 +/- 0.45 pixels for the ASM. Our results demonstrate that our ASM-MP method can accurately segment the lung on digital radiographs.

A Minimal Path Searching Approach for Active Shape Model (ASM)-based Segmentation of the Lung

PubMed Central

Guo, Shengwen; Fei, Baowei

2013-01-01

We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the normal direction. A minimal path (MP) deformable model is applied to drive the searching procedure. A statistical shape prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and manual segmentation is 1.75 ± 0.33 pixels, while the error is 1.99 ± 0.45 pixels for the ASM. Our results demonstrate that our ASM-MP method can accurately segment the lung on digital radiographs. PMID:24386531

4D strain localisation and fracture propagation in granite: the relative contribution of seismic and aseismic mechanisms to damage evolution during an in-situ triaxial deformation experiment at SOLEIL synchrotron

NASA Astrophysics Data System (ADS)

Cartwright-Taylor, A. L.; Fusseis, F.; Butler, I. B.; Flynn, M.; King, A.

2017-12-01

We present 4D x-ray data documenting strain localisation and fracture propagation in a microgranite, collected during a triaxial deformation experiment on the imaging beamline PSICHE at SOLEIL synchrotron. We loaded to failure a 2.97 mm diameter x 9.46 mm long cylindrical sample of Ailsa Craig microgranite, heat treated to 600 °C. The sample was deformed at 15 MPa confining pressure and 3x10-5 s-1 strain rate in a novel, x-ray transparent triaxial deformation apparatus, designed and built at the University of Edinburgh. 21 microtomographic volumes were acquired in intervals of 5-20 MPa (decreasing as failure approached), including one scan at peak differential stress of 200 MPa and three post-failure scans. A constant stress level was maintained during scanning and individual datasets were collected in 10 minutes using a white beam with an energy maximum at 66 keV in a spiral configuration. Reconstructions yielded image stacks of 1700x1700x4102 voxels with a voxel size of 2.7 μm. We analysed strain localisation and fracture propagation in the time series data. Local changes in volumetric and shear strains between time steps were quantified using 3D digital image correlation [1]. Fractures were segmented using a Multiscale Hessian fracture filter [2] and analysed for their orientations, dimensions and spatial distributions, and changes in these between time steps. In combination, these analyses show the extent and evolution of both local aseismic deformation and microcracking and their relative contributions to the overall damage evolution. Our data provides direct evidence of ongoing deformation processes, complementing the seminal results of Lockner et al. [3], who first imaged fault growth using acoustic emissions locations. Our results provide further insight into the aseismic mechanisms that dissipate >90% of the overall strain energy [4], and the interactions between these mechanisms and the developing microcracks. They also provide experimental verification of models for shear fracture formation whereby pre-existing flaws become connected by en-echelon tensile cracks that extend from their edges. [1] Hall, S. et al., 2010, Geotechnique 60, 315-322. [2] Voorn et al., 2015, J. Petroleum Sci. Eng. 127, 270-285. [3] Lockner, D., et al., 1991, Nature 350, 39-42. [4] Byerlee, J., 1993, Geology 21, 303-306.

Morphotectonic Index Analysis as an Indicator of Neotectonic Segmentation of the Nicoya Peninsula, Costa Rica

NASA Astrophysics Data System (ADS)

Morrish, S.; Marshall, J. S.

2013-12-01

The Nicoya Peninsula lies within the Costa Rican forearc where the Cocos plate subducts under the Caribbean plate at ~8.5 cm/yr. Rapid plate convergence produces frequent large earthquakes (~50yr recurrence interval) and pronounced crustal deformation (0.1-2.0m/ky uplift). Seven uplifted segments have been identified in previous studies using broad geomorphic surfaces (Hare & Gardner 1984) and late Quaternary marine terraces (Marshall et al. 2010). These surfaces suggest long term net uplift and segmentation of the peninsula in response to contrasting domains of subducting seafloor (EPR, CNS-1, CNS-2). In this study, newer 10m contour digital topographic data (CENIGA- Terra Project) will be used to characterize and delineate this segmentation using morphotectonic analysis of drainage basins and correlation of fluvial terrace/ geomorphic surface elevations. The peninsula has six primary watersheds which drain into the Pacific Ocean; the Río Andamojo, Río Tabaco, Río Nosara, Río Ora, Río Bongo, and Río Ario which range in area from 200 km2 to 350 km2. The trunk rivers follow major lineaments that define morphotectonic segment boundaries and in turn their drainage basins are bisected by them. Morphometric analysis of the lower (1st and 2nd) order drainage basins will provide insight into segmented tectonic uplift and deformation by comparing values of drainage basin asymmetry, stream length gradient, and hypsometry with respect to margin segmentation and subducting seafloor domain. A general geomorphic analysis will be conducted alongside the morphometric analysis to map previously recognized (Morrish et al. 2010) but poorly characterized late Quaternary fluvial terraces. Stream capture and drainage divide migration are common processes throughout the peninsula in response to the ongoing deformation. Identification and characterization of basin piracy throughout the peninsula will provide insight into the history of landscape evolution in response to differential uplift. Conducting this morphotectonic analysis of the Nicoya Peninsula will provide further constraints on rates of segment uplift, location of segment boundaries, and advance the understanding of the long term deformation of the region in relation to subduction.

Tension-dependent structural deformation alters single-molecule transition kinetics.

PubMed

Sudhanshu, B; Mihardja, S; Koslover, E F; Mehraeen, S; Bustamante, C; Spakowitz, A J

2011-02-01

We analyze the response of a single nucleosome to tension, which serves as a prototypical biophysical measurement where tension-dependent deformation alters transition kinetics. We develop a statistical-mechanics model of a nucleosome as a wormlike chain bound to a spool, incorporating fluctuations in the number of bases bound, the spool orientation, and the conformations of the unbound polymer segments. With the resulting free-energy surface, we perform dynamic simulations that permit a direct comparison with experiments. This simple approach demonstrates that the experimentally observed structural states at nonzero tension are a consequence of the tension and that these tension-induced states cease to exist at zero tension. The transitions between states exhibit substantial deformation of the unbound polymer segments. The associated deformation energy increases with tension; thus, the application of tension alters the kinetics due to tension-induced deformation of the transition states. This mechanism would arise in any system where the tether molecule is deformed in the transition state under the influence of tension.

Tension-dependent structural deformation alters single-molecule transition kinetics

PubMed Central

Sudhanshu, B.; Mihardja, S.; Koslover, E. F.; Mehraeen, S.; Bustamante, C.; Spakowitz, A. J.

2011-01-01

We analyze the response of a single nucleosome to tension, which serves as a prototypical biophysical measurement where tension-dependent deformation alters transition kinetics. We develop a statistical-mechanics model of a nucleosome as a wormlike chain bound to a spool, incorporating fluctuations in the number of bases bound, the spool orientation, and the conformations of the unbound polymer segments. With the resulting free-energy surface, we perform dynamic simulations that permit a direct comparison with experiments. This simple approach demonstrates that the experimentally observed structural states at nonzero tension are a consequence of the tension and that these tension-induced states cease to exist at zero tension. The transitions between states exhibit substantial deformation of the unbound polymer segments. The associated deformation energy increases with tension; thus, the application of tension alters the kinetics due to tension-induced deformation of the transition states. This mechanism would arise in any system where the tether molecule is deformed in the transition state under the influence of tension. PMID:21245354

A voxel-based finite element model for the prediction of bladder deformation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chai Xiangfei; Herk, Marcel van; Hulshof, Maarten C. C. M.

2012-01-15

Purpose: A finite element (FE) bladder model was previously developed to predict bladder deformation caused by bladder filling change. However, two factors prevent a wide application of FE models: (1) the labor required to construct a FE model with high quality mesh and (2) long computation time needed to construct the FE model and solve the FE equations. In this work, we address these issues by constructing a low-resolution voxel-based FE bladder model directly from the binary segmentation images and compare the accuracy and computational efficiency of the voxel-based model used to simulate bladder deformation with those of a classicalmore » FE model with a tetrahedral mesh. Methods: For ten healthy volunteers, a series of MRI scans of the pelvic region was recorded at regular intervals of 10 min over 1 h. For this series of scans, the bladder volume gradually increased while rectal volume remained constant. All pelvic structures were defined from a reference image for each volunteer, including bladder wall, small bowel, prostate (male), uterus (female), rectum, pelvic bone, spine, and the rest of the body. Four separate FE models were constructed from these structures: one with a tetrahedral mesh (used in previous study), one with a uniform hexahedral mesh, one with a nonuniform hexahedral mesh, and one with a low-resolution nonuniform hexahedral mesh. Appropriate material properties were assigned to all structures and uniform pressure was applied to the inner bladder wall to simulate bladder deformation from urine inflow. Performance of the hexahedral meshes was evaluated against the performance of the standard tetrahedral mesh by comparing the accuracy of bladder shape prediction and computational efficiency. Results: FE model with a hexahedral mesh can be quickly and automatically constructed. No substantial differences were observed between the simulation results of the tetrahedral mesh and hexahedral meshes (<1% difference in mean dice similarity coefficient to manual contours and <0.02 cm difference in mean standard deviation of residual errors). The average equation solving time (without manual intervention) for the first two types of hexahedral meshes increased to 2.3 h and 2.6 h compared to the 1.1 h needed for the tetrahedral mesh, however, the low-resolution nonuniform hexahedral mesh dramatically decreased the equation solving time to 3 min without reducing accuracy. Conclusions: Voxel-based mesh generation allows fast, automatic, and robust creation of finite element bladder models directly from binary segmentation images without user intervention. Even the low-resolution voxel-based hexahedral mesh yields comparable accuracy in bladder shape prediction and more than 20 times faster in computational speed compared to the tetrahedral mesh. This approach makes it more feasible and accessible to apply FE method to model bladder deformation in adaptive radiotherapy.« less

A 4D biomechanical lung phantom for joint segmentation/registration evaluation

NASA Astrophysics Data System (ADS)

Markel, Daniel; Levesque, Ives; Larkin, Joe; Léger, Pierre; El Naqa, Issam

2016-10-01

At present, there exists few openly available methods for evaluation of simultaneous segmentation and registration algorithms. These methods allow for a combination of both techniques to track the tumor in complex settings such as adaptive radiotherapy. We have produced a quality assurance platform for evaluating this specific subset of algorithms using a preserved porcine lung in such that it is multi-modality compatible: positron emission tomography (PET), computer tomography (CT) and magnetic resonance imaging (MRI). A computer controlled respirator was constructed to pneumatically manipulate the lungs in order to replicate human breathing traces. A registration ground truth was provided using an in-house bifurcation tracking pipeline. Segmentation ground truth was provided by synthetic multi-compartment lesions to simulate biologically active tumor, background tissue and a necrotic core. The bifurcation tracking pipeline results were compared to digital deformations and used to evaluate three registration algorithms, Diffeomorphic demons, fast-symmetric forces demons and MiMVista’s deformable registration tool. Three segmentation algorithms the Chan Vese level sets method, a Hybrid technique and the multi-valued level sets algorithm. The respirator was able to replicate three seperate breathing traces with a mean accuracy of 2-2.2%. Bifurcation tracking error was found to be sub-voxel when using human CT data for displacements up to 6.5 cm and approximately 1.5 voxel widths for displacements up to 3.5 cm for the porcine lungs. For the fast-symmetric, diffeomorphic and MiMvista registration algorithms, mean geometric errors were found to be 0.430+/- 0.001 , 0.416+/- 0.001 and 0.605+/- 0.002 voxels widths respectively using the vector field differences and 0.4+/- 0.2 , 0.4+/- 0.2 and 0.6+/- 0.2 voxel widths using the bifurcation tracking pipeline. The proposed phantom was found sufficient for accurate evaluation of registration and segmentation algorithms. The use of automatically generated anatomical landmarks proposed can eliminate the time and potential innacuracy of manual landmark selection using expert observers.

Accurate registration of temporal CT images for pulmonary nodules detection

NASA Astrophysics Data System (ADS)

Yan, Jichao; Jiang, Luan; Li, Qiang

2017-02-01

Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was performed to align the feature points extracted from the registered images in the second step and the reference images. Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital. The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective observation. The subtraction images of the reference images and the rigid and non-rigid registered images could effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This would be useful for the screening of lung cancer in our future study.

Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: a clinical study on right and left ventricles.

PubMed

Angelini, Elsa D; Homma, Shunichi; Pearson, Gregory; Holmes, Jeffrey W; Laine, Andrew F

2005-09-01

Among screening modalities, echocardiography is the fastest, least expensive and least invasive method for imaging the heart. A new generation of three-dimensional (3-D) ultrasound (US) technology has been developed with real-time 3-D (RT3-D) matrix phased-array transducers. These transducers allow interactive 3-D visualization of cardiac anatomy and fast ventricular volume estimation without tomographic interpolation as required with earlier 3-D US acquisition systems. However, real-time acquisition speed is performed at the cost of decreasing spatial resolution, leading to echocardiographic data with poor definition of anatomical structures and high levels of speckle noise. The poor quality of the US signal has limited the acceptance of RT3-D US technology in clinical practice, despite the wealth of information acquired by this system, far greater than with any other existing echocardiography screening modality. We present, in this work, a clinical study for segmentation of right and left ventricular volumes using RT3-D US. A preprocessing of the volumetric data sets was performed using spatiotemporal brushlet denoising, as presented in previous articles Two deformable-model segmentation methods were implemented in 2-D using a parametric formulation and in 3-D using an implicit formulation with a level set implementation for extraction of endocardial surfaces on denoised RT3-D US data. A complete and rigorous validation of the segmentation methods was carried out for quantification of left and right ventricular volumes and ejection fraction, including comparison of measurements with cardiac magnetic resonance imaging as the reference. Results for volume and ejection fraction measurements report good performance of quantification of cardiac function on RT3-D data compared with magnetic resonance imaging with better performance of semiautomatic segmentation methods than with manual tracing on the US data.

Stresses, deformation, and seismic events on scaled experimental faults with heterogeneous fault segments and comparison to numerical modeling

NASA Astrophysics Data System (ADS)

Buijze, Loes; Guo, Yanhuang; Niemeijer, André R.; Ma, Shengli; Spiers, Christopher J.

2017-04-01

Faults in the upper crust cross-cut many different lithologies, which cause the composition of the fault rocks to vary. Each different fault rock segment may have specific mechanical properties, e.g. there may be stronger and weaker segments, and segments prone to unstable slip or creeping. This leads to heterogeneous deformation and stresses along such faults, and a heterogeneous distribution of seismic events. We address the influence of fault variability on stress, deformation, and seismicity using a combination of scaled laboratory fault and numerical modeling. A vertical fault was created along the diagonal of a 30 x 20 x 5 cm block of PMMA, along which a 2 mm thick gouge layer was deposited. Gouge materials of different characteristics were used to create various segments along the fault; quartz (average strength, stable sliding), kaolinite (weak, stable sliding), and gypsum (average strength, unstable sliding). The sample assembly was placed in a horizontal biaxial deformation apparatus, and shear displacement was enforced along the vertical fault. Multiple observations were made: 1) Acoustic emissions were continuously recorded at 3 MHz to observe the occurrence of stick-slips (micro-seismicity), 2) Photo-elastic effects (indicative of the differential stress) were recorded in the transparent set of PMMA wall-rocks using a high-speed camera, and 3) particle tracking was conducted on a speckle painted set of PMMA wall-rocks to study the deformation in the wall-rocks flanking the fault. All three observation methods show how the heterogeneous fault gouge exerts a strong control on the fault behavior. For example, a strong, unstable segment of gypsum flanked by two weaker kaolinite segments show strong stress concentrations develop near the edges of the strong segment, with at the same time most of acoustic emissions being located at the edge of this strong segment. The measurements of differential stress, strain and acoustic emissions provide a strong means to compare the scaled experiment to modeling results. In a finite-element model we reproduce the laboratory experiments, and compare the modeled stresses and strains to the observations and we compare the nucleation of seismic instability to the location of acoustic emissions. The model aids in understanding how the stresses and strains may vary as a result of fault heterogeneity, but also as a result of the boundary conditions inherent to a laboratory setup. The scaled experimental setup and modeling results also provide a means explain and compare with observations made at a larger scale, for example geodetic and seismological measurements along crustal scale faults.

Modeling and Representation of Human Hearts for Volumetric Measurement

PubMed Central

Guan, Qiu; Wang, Wanliang; Wu, Guang

2012-01-01

This paper investigates automatic construction of a three-dimensional heart model from a set of medical images, represents it in a deformable shape, and uses it to perform volumetric measurements. This not only significantly improves its reliability and accuracy but also makes it possible to derive valuable novel information, like various assessment and dynamic volumetric measurements. The method is based on a flexible model trained from hundreds of patient image sets by a genetic algorithm, which takes advantage of complete segmentation of the heart shape to form a geometrical heart model. For an image set of a new patient, an interpretation scheme is used to obtain its shape and evaluate some important parameters. Apart from automatic evaluation of traditional heart functions, some new information of cardiovascular diseases may be recognized from the volumetric analysis. PMID:22162723

Dentalmaps: Automatic Dental Delineation for Radiotherapy Planning in Head-and-Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thariat, Juliette, E-mail: jthariat@hotmail.com; Ramus, Liliane; INRIA

Purpose: To propose an automatic atlas-based segmentation framework of the dental structures, called Dentalmaps, and to assess its accuracy and relevance to guide dental care in the context of intensity-modulated radiotherapy. Methods and Materials: A multi-atlas-based segmentation, less sensitive to artifacts than previously published head-and-neck segmentation methods, was used. The manual segmentations of a 21-patient database were first deformed onto the query using nonlinear registrations with the training images and then fused to estimate the consensus segmentation of the query. Results: The framework was evaluated with a leave-one-out protocol. The maximum doses estimated using manual contours were considered as groundmore » truth and compared with the maximum doses estimated using automatic contours. The dose estimation error was within 2-Gy accuracy in 75% of cases (with a median of 0.9 Gy), whereas it was within 2-Gy accuracy in 30% of cases only with the visual estimation method without any contour, which is the routine practice procedure. Conclusions: Dose estimates using this framework were more accurate than visual estimates without dental contour. Dentalmaps represents a useful documentation and communication tool between radiation oncologists and dentists in routine practice. Prospective multicenter assessment is underway on patients extrinsic to the database.« less

Analysis and correction for measurement error of edge sensors caused by deformation of guide flexure applied in the Thirty Meter Telescope SSA.

PubMed

Cao, Haifeng; Zhang, Jingxu; Yang, Fei; An, Qichang; Zhao, Hongchao; Guo, Peng

2018-05-01

The Thirty Meter Telescope (TMT) project will design and build a 30-m-diameter telescope for research in astronomy in visible and infrared wavelengths. The primary mirror of TMT is made up of 492 hexagonal mirror segments under active control. The highly segmented primary mirror will utilize edge sensors to align and stabilize the relative piston, tip, and tilt degrees of segments. The support system assembly (SSA) of the segmented mirror utilizes a guide flexure to decouple the axial support and lateral support, while its deformation will cause measurement error of the edge sensor. We have analyzed the theoretical relationship between the segment movement and the measurement value of the edge sensor. Further, we have proposed an error correction method with a matrix. The correction process and the simulation results of the edge sensor will be described in this paper.

SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, K; Bayouth, J; Patton, T

2015-06-15

Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitudemore » for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in upper lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.« less

Background feature descriptor for offline handwritten numeral recognition

NASA Astrophysics Data System (ADS)

Ming, Delie; Wang, Hao; Tian, Tian; Jie, Feiran; Lei, Bo

2011-11-01

This paper puts forward an offline handwritten numeral recognition method based on background structural descriptor (sixteen-value numerical background expression). Through encoding the background pixels in the image according to a certain rule, 16 different eigenvalues were generated, which reflected the background condition of every digit, then reflected the structural features of the digits. Through pattern language description of images by these features, automatic segmentation of overlapping digits and numeral recognition can be realized. This method is characterized by great deformation resistant ability, high recognition speed and easy realization. Finally, the experimental results and conclusions are presented. The experimental results of recognizing datasets from various practical application fields reflect that with this method, a good recognition effect can be achieved.

Investigation of the accuracy of breast tissue segmentation methods for the purpose of developing breast deformation models for use in adaptive radiotherapy

NASA Astrophysics Data System (ADS)

Juneja, P.; Harris, E. J.; Evans, P. M.

2014-03-01

Realistic modelling of breast deformation requires the breast tissue to be segmented into fibroglandular and fatty tissue and assigned suitable material properties. There are a number of breast tissue segmentation methods proposed and used in the literature. The purpose of this study was to validate and compare the accuracy of various segmentation methods and to investigate the effect of the tissue distribution on the segmentation accuracy. Computed tomography (CT) data for 24 patients, both in supine and prone positions were segmented into fibroglandular and fatty tissue. The segmentation methods explored were: physical density thresholding; interactive thresholding; fuzzy c-means clustering (FCM) with three classes (FCM3) and four classes (FCM4); and k-means clustering. Validation was done in two-stages: firstly, a new approach, supine-prone validation based on the assumption that the breast composition should appear the same in the supine and prone scans was used. Secondly, outlines from three experts were used for validation. This study found that FCM3 gave the most accurate segmentation of breast tissue from CT data and that the segmentation accuracy is adversely affected by the sparseness of the fibroglandular tissue distribution.

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

NASA Astrophysics Data System (ADS)

Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V.; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L.; Beauchemin, Steven S.; Rodrigues, George; Gaede, Stewart

2015-02-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.

PubMed

Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L; Beauchemin, Steven S; Rodrigues, George; Gaede, Stewart

2015-02-21

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

James Webb Space Telescope Optical Simulation Testbed I: overview and first results

NASA Astrophysics Data System (ADS)

Perrin, Marshall D.; Soummer, Rémi; Choquet, Élodie; N'Diaye, Mamadou; Levecq, Olivier; Lajoie, Charles-Philippe; Ygouf, Marie; Leboulleux, Lucie; Egron, Sylvain; Anderson, Rachel; Long, Chris; Elliott, Erin; Hartig, George; Pueyo, Laurent; van der Marel, Roeland; Mountain, Matt

2014-08-01

The James Webb Space Telescope (JWST) Optical Simulation Testbed (JOST) is a tabletop workbench to study aspects of wavefront sensing and control for a segmented space telescope, including both commissioning and maintenance activities. JOST is complementary to existing optomechanical testbeds for JWST (e.g. the Ball Aerospace Testbed Telescope, TBT) given its compact scale and flexibility, ease of use, and colocation at the JWST Science & Operations Center. We have developed an optical design that reproduces the physics of JWST's three-mirror anastigmat using three aspheric lenses; it provides similar image quality as JWST (80% Strehl ratio) over a field equivalent to a NIRCam module, but at HeNe wavelength. A segmented deformable mirror stands in for the segmented primary mirror and allows control of the 18 segments in piston, tip, and tilt, while the secondary can be controlled in tip, tilt and x, y, z position. This will be sufficient to model many commissioning activities, to investigate field dependence and multiple field point sensing & control, to evaluate alternate sensing algorithms, and develop contingency plans. Testbed data will also be usable for cross-checking of the WFS&C Software Subsystem, and for staff training and development during JWST's five- to ten-year mission.

Image-guided feedback for ophthalmic microsurgery using multimodal intraoperative swept-source spectrally encoded scanning laser ophthalmoscopy and optical coherence tomography

NASA Astrophysics Data System (ADS)

Li, Jianwei D.; Malone, Joseph D.; El-Haddad, Mohamed T.; Arquitola, Amber M.; Joos, Karen M.; Patel, Shriji N.; Tao, Yuankai K.

2017-02-01

Surgical interventions for ocular diseases involve manipulations of semi-transparent structures in the eye, but limited visualization of these tissue layers remains a critical barrier to developing novel surgical techniques and improving clinical outcomes. We addressed limitations in image-guided ophthalmic microsurgery by using microscope-integrated multimodal intraoperative swept-source spectrally encoded scanning laser ophthalmoscopy and optical coherence tomography (iSS-SESLO-OCT). We previously demonstrated in vivo human ophthalmic imaging using SS-SESLO-OCT, which enabled simultaneous acquisition of en face SESLO images with every OCT cross-section. Here, we integrated our new 400 kHz iSS-SESLO-OCT, which used a buffered Axsun 1060 nm swept-source, with a surgical microscope and TrueVision stereoscopic viewing system to provide image-based feedback. In vivo human imaging performance was demonstrated on a healthy volunteer, and simulated surgical maneuvers were performed in ex vivo porcine eyes. Denselysampled static volumes and volumes subsampled at 10 volumes-per-second were used to visualize tissue deformations and surgical dynamics during corneal sweeps, compressions, and dissections, and retinal sweeps, compressions, and elevations. En face SESLO images enabled orientation and co-registration with the widefield surgical microscope view while OCT imaging enabled depth-resolved visualization of surgical instrument positions relative to anatomic structures-of-interest. TrueVision heads-up display allowed for side-by-side viewing of the surgical field with SESLO and OCT previews for real-time feedback, and we demonstrated novel integrated segmentation overlays for augmented-reality surgical guidance. Integration of these complementary imaging modalities may benefit surgical outcomes by enabling real-time intraoperative visualization of surgical plans, instrument positions, tissue deformations, and image-based surrogate biomarkers correlated with completion of surgical goals.

Two-Dimensional Boundary Element Method Application for Surface Deformation Modeling around Lembang and Cimandiri Fault, West Java

NASA Astrophysics Data System (ADS)

Mahya, M. J.; Sanny, T. A.

2017-04-01

Lembang and Cimandiri fault are active faults in West Java that thread people near the faults with earthquake and surface deformation risk. To determine the deformation, GPS measurements around Lembang and Cimandiri fault was conducted then the data was processed to get the horizontal velocity at each GPS stations by Graduate Research of Earthquake and Active Tectonics (GREAT) Department of Geodesy and Geomatics Engineering Study Program, ITB. The purpose of this study is to model the displacement distribution as deformation parameter in the area along Lembang and Cimandiri fault using 2-dimensional boundary element method (BEM) using the horizontal velocity that has been corrected by the effect of Sunda plate horizontal movement as the input. The assumptions that used at the modeling stage are the deformation occurs in homogeneous and isotropic medium, and the stresses that acted on faults are in elastostatic condition. The results of modeling show that Lembang fault had left-lateral slip component and divided into two segments. A lineament oriented in southwest-northeast direction is observed near Tangkuban Perahu Mountain separating the eastern and the western segments of Lembang fault. The displacement pattern of Cimandiri fault shows that Cimandiri fault is divided into the eastern segment with right-lateral slip component and the western segment with left-lateral slip component separated by a northwest-southeast oriented lineament at the western part of Gede Pangrango Mountain. The displacement value between Lembang and Cimandiri fault is nearly zero indicating that Lembang and Cimandiri fault are not connected each other and this area is relatively safe for infrastructure development.

Coupled dictionary learning for joint MR image restoration and segmentation

NASA Astrophysics Data System (ADS)

Yang, Xuesong; Fan, Yong

2018-03-01

To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

Remote sensing image segmentation based on Hadoop cloud platform

NASA Astrophysics Data System (ADS)

Li, Jie; Zhu, Lingling; Cao, Fubin

2018-01-01

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

WE-H-202-04: Advanced Medical Image Registration Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christensen, G.

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed tomore » tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.« less

Seismic sources in El Salvador. A geological and geodetic contribution

NASA Astrophysics Data System (ADS)

Alonso-Henar, J.; Martínez-Díaz, J. J.; Benito, B.; Alvarez-Gomez, J. A.; Canora, C.; Capote, R.; Staller, A.; Tectónica Activa, Paleosismicidad y. Riesgos Asociados UCM-910368

2013-05-01

El Salvador Fault Zone is a deformation band of 150 km long and 20 km wide within the Salvadorian volcanic arc. This shear band distributes the deformation between main strike-slip faults trending N90°-100°E and around 30 km long, and secondary normal faults trending between N120°E and N170°E. The ESFZ continues westward and is relieved by the Jalpatagua Fault. Eastward ESFZ becomes less clear disappearing at Golfo de Fonseca. The ESFZ deforms and offsets quaternary deposits with a right lateral movement in its main segments. Five segments have been proposed for the whole fault zone, from the Jalpatagua Fault to the Golfo de Fonseca. Paleoseismic studies in the Berlin and San Vicente Segments reveal an important amount of quaternary deformation. In fact, the San Vicente Segment was the source of the February 13, 2001 destructive earthquake. In this work we propose 18 capable seismic sources within El Salvador. The slip rate of each source has been obtained through out the combination of GPS data and paleoseismic data when it has been possible. We also have calculated maximum theoretical intensities produced by the maximum earthquakes related with each fault. We have taken into account several scenarios considering different possible surface rupture lengths up to 50 km and Mw 7.6 in some of the strike slip faults within ESFZ.

Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.

PubMed

Yang, Xiaofeng; Wu, Ning; Cheng, Guanghui; Zhou, Zhengyang; Yu, David S; Beitler, Jonathan J; Curran, Walter J; Liu, Tian

2014-12-01

To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy. Copyright © 2014 Elsevier Inc. All rights reserved.

Active Correction of Aperture Discontinuities-Optimized Stroke Minimization. II. Optimization for Future Missions

NASA Astrophysics Data System (ADS)

Mazoyer, J.; Pueyo, L.; N'Diaye, M.; Fogarty, K.; Zimmerman, N.; Soummer, R.; Shaklan, S.; Norman, C.

2018-01-01

High-contrast imaging and spectroscopy provide unique constraints for exoplanet formation models as well as for planetary atmosphere models. Instrumentation techniques in this field have greatly improved over the last two decades, with the development of stellar coronagraphy, in parallel with specific methods of wavefront sensing and control. Next generation space- and ground-based telescopes will enable the characterization of cold solar-system-like planets for the first time and maybe even in situ detection of bio-markers. However, the growth of primary mirror diameters, necessary for these detections, comes with an increase of their complexity (segmentation, secondary mirror features). These discontinuities in the aperture can greatly limit the performance of coronagraphic instruments. In this context, we introduced a new technique, Active Correction of Aperture Discontinuities-Optimized Stroke Minimization (ACAD-OSM), to correct for the diffractive effects of aperture discontinuities in the final image plane of a coronagraph, using deformable mirrors. In this paper, we present several tools that can be used to optimize the performance of this technique for its application to future large missions. In particular, we analyzed the influence of the deformable setup (size and separating distance) and found that there is an optimal point for this setup, optimizing the performance of the instrument in contrast and throughput while minimizing the strokes applied to the deformable mirrors. These results will help us design future coronagraphic instruments to obtain the best performance.

Surface deformation monitored on the south eastern part of Uttarakhand State of India by the Persistent Scatterer Interferometry (PSI) techniques

NASA Astrophysics Data System (ADS)

Yhokha, A.; Chang, C.; Yen, J.; Goswami, P. K.; Ching, K.

2013-12-01

Persistent Scatterer Interferometry (PSI) is a useful tool in gathering the first basic information about the surface deformation, despite of different natural terrains, forested or mountainous region. This technique has been applied successfully by various worker in different field in extracting surface information in variety of terranes. The advantage of this techniques is that it has the ability of taking into account of only those return radar signal which are the brightest or the strongest in the surrounding background signal. Moreover, PS algorithms operate on a time series of interferograms all formed with respect to a single master SAR image that the noise terms of displacement for each PS pixel are much reduced. Keeping all these points in mind, we applied this technique in the Himalayan mountain, covering the south eastern part of the Uttarakhand state of India. So far lots of different work has been carried out in the Himalayan region, but less work has been done in regards to its surface deformation. The Himalayan mountain are well know for its segmented nature, different region undergoing different tectonic activity. In the similar manner, our PSI result in our study area also reveal two different set of deformation, with its eastern part revealing subsidence and the western part undergoing uplift, these two set of deformation is separated by a right later strike slip fault called, the Garampani-Kathgodam fault (G-KF). Apart from this obvious deformation, the western part also reveal differential deformation. Based on our result we have also tried to create a deformation model, to understand and to get better knowledge of the tectonic deformation setting.

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures.

PubMed

Lim, Issel Anne L; Faria, Andreia V; Li, Xu; Hsu, Johnny T C; Airan, Raag D; Mori, Susumu; van Zijl, Peter C M

2013-11-15

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a "deep gray matter parcellation map" (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established "white matter parcellation map" (WMPM) from the same subject's T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the "Everything Parcellation Map in Eve Space," also known as the "EvePM." It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting "almost perfect" agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM. Copyright © 2013 Elsevier Inc. All rights reserved.

Shape regularized active contour based on dynamic programming for anatomical structure segmentation

NASA Astrophysics Data System (ADS)

Yu, Tianli; Luo, Jiebo; Singhal, Amit; Ahuja, Narendra

2005-04-01

We present a method to incorporate nonlinear shape prior constraints into segmenting different anatomical structures in medical images. Kernel space density estimation (KSDE) is used to derive the nonlinear shape statistics and enable building a single model for a class of objects with nonlinearly varying shapes. The object contour is coerced by image-based energy into the correct shape sub-distribution (e.g., left or right lung), without the need for model selection. In contrast to an earlier algorithm that uses a local gradient-descent search (susceptible to local minima), we propose an algorithm that iterates between dynamic programming (DP) and shape regularization. DP is capable of finding an optimal contour in the search space that maximizes a cost function related to the difference between the interior and exterior of the object. To enforce the nonlinear shape prior, we propose two shape regularization methods, global and local regularization. Global regularization is applied after each DP search to move the entire shape vector in the shape space in a gradient descent fashion to the position of probable shapes learned from training. The regularized shape is used as the starting shape for the next iteration. Local regularization is accomplished through modifying the search space of the DP. The modified search space only allows a certain amount of deformation of the local shape from the starting shape. Both regularization methods ensure the consistency between the resulted shape with the training shapes, while still preserving DP"s ability to search over a large range and avoid local minima. Our algorithm was applied to two different segmentation tasks for radiographic images: lung field and clavicle segmentation. Both applications have shown that our method is effective and versatile in segmenting various anatomical structures under prior shape constraints; and it is robust to noise and local minima caused by clutter (e.g., blood vessels) and other similar structures (e.g., ribs). We believe that the proposed algorithm represents a major step in the paradigm shift to object segmentation under nonlinear shape constraints.

Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures

PubMed Central

Lim, Issel Anne L.; Faria, Andreia V.; Li, Xu; Hsu, Johnny T.C.; Airan, Raag D.; Mori, Susumu; van Zijl, Peter C. M.

2013-01-01

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a “deep gray matter parcellation map” (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established “white matter parcellation map” (WMPM) from the same subject’s T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the “Everything Parcellation Map in Eve Space,” also known as the “EvePM.” It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33 years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting “almost perfect” agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM. PMID:23769915

PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration

PubMed Central

Niethammer, Marc; Akbari, Hamed; Bilello, Michel; Davatzikos, Christos; Pohl, Kilian M.

2014-01-01

We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches. PMID:24595340

Equivalent strike-slip earthquake cycles in half-space and lithosphere-asthenosphere earth models

USGS Publications Warehouse

Savage, J.C.

1990-01-01

By virtue of the images used in the dislocation solution, the deformation at the free surface produced throughout the earthquake cycle by slippage on a long strike-slip fault in an Earth model consisting of an elastic plate (lithosphere) overlying a viscoelastic half-space (asthenosphere) can be duplicated by prescribed slip on a vertical fault embedded in an elastic half-space. Inversion of 1973-1988 geodetic measurements of deformation across the segment of the San Andreas fault in the Transverse Ranges north of Los Angeles for the half-space equivalent slip distribution suggests no significant slip on the fault above 30 km and a uniform slip rate of 36 mm/yr below 30 km. One equivalent lithosphere-asthenosphere model would have a 30-km thick lithosphere and an asthenosphere relaxation time greater than 33 years, but other models are possible. -from Author

Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images

PubMed Central

Du, Jia; Younes, Laurent; Qiu, Anqi

2011-01-01

This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are simultaneously carried from one subject to another through a flow of diffeomorphisms. To the best of our knowledge, this is the first time that the diffeomorphic metric from one brain to another is derived in a shape space of intensity images and point sets (such as curves and surfaces) in a unified manner. We describe the Euler–Lagrange equation associated with this algorithm with respect to momentum, a linear transformation of the velocity vector field of the diffeomorphic flow. The numerical implementation for solving this variational problem, which involves large-scale kernel convolution in an irregular grid, is made feasible by introducing a class of computationally friendly kernels. We apply this algorithm to align magnetic resonance brain data. Our whole brain mapping results show that our algorithm outperforms the image-based LDDMM algorithm in terms of the mapping accuracy of gyral/sulcal curves, sulcal regions, and cortical and subcortical segmentation. Moreover, our algorithm provides better whole brain alignment than combined volumetric and surface registration (Postelnicu et al., 2009) and hierarchical attribute matching mechanism for elastic registration (HAMMER) (Shen and Davatzikos, 2002) in terms of cortical and subcortical volume segmentation. PMID:21281722

[The need of transforming the health system in Mexico].

PubMed

López-Cervantes, Malaquías; Durán Arenas, Juan Luis; Villanueva Lozano, Marcia

2011-01-01

In this article we review the need for the transformation of the Mexican health care system given the deformities that the system developed in the last 60 years. We start by the discussion of two main deformities: the segmented answer to the health right, and the development of a segmented health care system based on the method of payment (formal workers contributions); and the development of a health care model based on specialties and hospital care. These deformities have resulted in a health care system characterized by high costs and low effectiveness. Even though the correction of the deformities imply complex modifications that involve political economic and legal aspects, in the short term we have the conditions in Mexico for the creation of a universal primary health care system, given the human and financial resources available in the country.

Highly deformable bones: unusual deformation mechanisms of seahorse armor.

PubMed

Porter, Michael M; Novitskaya, Ekaterina; Castro-Ceseña, Ana Bertha; Meyers, Marc A; McKittrick, Joanna

2013-06-01

Multifunctional materials and devices found in nature serve as inspiration for advanced synthetic materials, structures and robotics. Here, we elucidate the architecture and unusual deformation mechanisms of seahorse tails that provide prehension as well as protection against predators. The seahorse tail is composed of subdermal bony plates arranged in articulating ring-like segments that overlap for controlled ventral bending and twisting. The bony plates are highly deformable materials designed to slide past one another and buckle when compressed. This complex plate and segment motion, along with the unique hardness distribution and structural hierarchy of each plate, provide seahorses with joint flexibility while shielding them against impact and crushing. Mimicking seahorse armor may lead to novel bio-inspired technologies, such as flexible armor, fracture-resistant structures or prehensile robotics. Copyright © 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Time dependent deformation of Kilauea Volcano, Hawaii

NASA Astrophysics Data System (ADS)

Montgomery-Brown, Emily Kvietka Desmarais

In 1997 the continuous Global Positioning System (GPS) network was completed on Kilauea, providing the first network of daily position measurements during eruptions and earthquakes on Kilauea. Kilauea has been studied for many decades with continuous seismic and tilt instruments. Other geodetic data (e.g., campaign GPS, leveling, electronic distance measurements) are also available although they contain only sparse data. Data analysis methods used here include inverting multiple data sets for optimal source parameters and the spatio-temporal distribution of magma volume and fault slip, and combining GPS and seismic observations to understand flank tectonics. The field area for this study, Kilauea Volcano, was chosen because of its frequent activity and potential hazards. The 1997 East Rift Zone eruption (Episode 54) was the first major event to occur after the completion of the continuous GPS network. The event lasted 2 days, but transient deformation continued for six months. This long-duration transient allowed the first spatio-temporal study of transient dike deformation on Kilauea from daily GPS positions. Slow-slip events were discovered on Kilauea during which the southern flank of the volcano would accelerate seaward for approximately 2 days. The discovery was made possible because of the continuously operating GPS network. These slip events were also observed to correlate with small swarms of microearthquakes found to follow temporal pattern consistent with them being co- and aftershocks of the slow-slip event (Segall, 2006). Half-space models of geodetic data favor a shallow fault plane (˜ 5 km), which is much too shallow to have increased the Coulomb stress at the depths of the co- and aftershocks. However, optimizations for the slow-slip source parameters including a layered elastic structure and a topographic correction favor deeper models within the range of the co- and aftershocks. Additionally, the spatial distribution of seaward fault slip, fixed to a decollement structure 8 km under the south flank, and the locations of the microearthquakes suggest that both occur on the same structure. In 2007, Episode 56 of the Pu'u 'O'o-Kupianaha eruption occurred. This episode was exciting both because it was the largest intrusion in the last decade, and because it occurred concurrently with a flank slow-slip event. The intrusion started on Father's day (June 17th), 2007 with increased seismicity and abrupt tilts at the summit and rift zones. Quasi-static models of the total deformation determined from GPS, tilt, and InSAR indicate that the intrusion occurred on two en echelon dike segments in the upper East Rift Zone along with deformation consistent with slow-slip in the same areas of previous events. The ˜ 2 m maximum opening occurred on the eastern segment near Makaopui crater. Unlike previous intrusions in 1997, 1999, and 2000, the dike model was not sufficient to explain deformation on the western flank. Additionally, a coastal tiltmeter installed in anticipation of a slow-slip event recorded tilts consistent with those observed during the 2005 slow-slip event. These observations led to the conclusion that a concurrent slow-slip event occurred. Geodetic models indicate a similar amount of decollement slip occurred as in previous slow-slip events. Sub-daily GPS positions were used to study the spatio-temporal distribution of the dike intrusion. The time-dependent intrusion model shows that the intrusion began on the western en echelon segment before jumping to the eastern segment, which accumulated the majority of the 2 m of opening. Sub-daily GPS positions limit the number of stations available since there are very few continuous stations north of the East Rift Zone, where coverage is critical for separating the intrusion from the slow-slip. However, an ENVISAT interferogram at 08:22 on June 18, 2007 provides additional spatial coverage of deformation up to that point. Combining this image with the GPS and tilt data up to that point, we perform a quasi-static inversion for the intrusion source. The residual deformation indicates that slow-slip had not significantly progressed by the ENVISAT image. The slow-slip event occurred therefore at least 20 hours after the initiation of the dike intrusion. (Abstract shortened by UMI.)

Image processing and recognition for biological images.

PubMed

Uchida, Seiichi

2013-05-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Histological techniques for study of photoreceptor orientation.

PubMed

Laties, A M

1969-01-01

An histological method for the study of photoreceptor orientation in primate eyes is described. To preserve photoreceptor orientation it is necessary to protect the fragile rod and cone outer segments to the maximum extent possible from mechanical deformation and from injury by solvent extraction. To prevent mechanical deformation the eyes are freeze-dried and embedded in plastic with or without prior vapor fixation. Solvent extraction from the lipid-rich outer segment is limited by avoidance or restriction of organic solvents. When large segments of primate eyes are so treated, it is possible to section the plastic blocks along the visual axis, polish the block surface, and view photoreceptor orientation by epi-illumination microscopy. In such specimens a differential orientation of photoreceptors exists with the long axis of photoreceptor inner and outer segments in line with incoming light rays.

Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms.

PubMed

Rahmani Seryasat, Omid; Haddadnia, Javad

2018-06-01

Mammography is the most common screening method for diagnosis of breast cancer. In this study, a computer-aided system for diagnosis of benignity and malignity of the masses was implemented in mammogram images. In the computer aided diagnosis system, we first reduce the noise in the mammograms using an effective noise removal technique. After the noise removal, the mass in the region of interest must be segmented and this segmentation is done using a deformable model. After the mass segmentation, a number of features are extracted from it. These features include: features of the mass shape and border, tissue properties, and the fractal dimension. After extracting a large number of features, a proper subset must be chosen from among them. In this study, we make use of a new method on the basis of a genetic algorithm for selection of a proper set of features. After determining the proper features, a classifier is trained. To classify the samples, a new architecture for combination of the classifiers is proposed. In this architecture, easy and difficult samples are identified and trained using different classifiers. Finally, the proposed mass diagnosis system was also tested on mini-Mammographic Image Analysis Society and digital database for screening mammography databases. The obtained results indicate that the proposed system can compete with the state-of-the-art methods in terms of accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

Coseismic Slip Deficit of the 2017 Mw 6.5 Ormoc Earthquake That Occurred Along a Creeping Segment and Geothermal Field of the Philippine Fault

NASA Astrophysics Data System (ADS)

Yang, Ying-Hui; Tsai, Min-Chien; Hu, Jyr-Ching; Aurelio, Mario A.; Hashimoto, Manabu; Escudero, John Agustin P.; Su, Zhe; Chen, Qiang

2018-03-01

Coseismic surface deformation imaged through interferometric synthetic aperture radar (InSAR) measurements was used to estimate the fault geometry and slip distribution of the 2017 Mw 6.5 Ormoc earthquake along a creeping segment of the Philippine Fault on Leyte Island. Our best fitting faulting model suggests that the coseismic rupture occurred on a fault plane with high dip angle of 78.5° and strike angle of 325.8°, and the estimated maximum fault slip of 2.3 m is located at 6.5 km east-northeast of the town of Kananga. The recognized insignificant slip in the Tongonan geothermal field zone implies that the plastic behavior caused by high geothermal gradient underneath the Tongonan geothermal field could prevent the coseismic failure in heated rock mass in this zone. The predicted Coulomb failure stress change shows that a significant positive Coulomb failure stress change occurred along the SE segment of central Philippine Fault with insignificant coseismic slip and infrequent aftershocks, which suggests an increasing risk for future seismic hazard.

3D reconstruction of synapses with deep learning based on EM Images

NASA Astrophysics Data System (ADS)

Xiao, Chi; Rao, Qiang; Zhang, Dandan; Chen, Xi; Han, Hua; Xie, Qiwei

2017-03-01

Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tapecollecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.

Three-dimensional lung tumor segmentation from x-ray computed tomography using sparse field active models.

PubMed

Awad, Joseph; Owrangi, Amir; Villemaire, Lauren; O'Riordan, Elaine; Parraga, Grace; Fenster, Aaron

2012-02-01

Manual segmentation of lung tumors is observer dependent and time-consuming but an important component of radiology and radiation oncology workflow. The objective of this study was to generate an automated lung tumor measurement tool for segmentation of pulmonary metastatic tumors from x-ray computed tomography (CT) images to improve reproducibility and decrease the time required to segment tumor boundaries. The authors developed an automated lung tumor segmentation algorithm for volumetric image analysis of chest CT images using shape constrained Otsu multithresholding (SCOMT) and sparse field active surface (SFAS) algorithms. The observer was required to select the tumor center and the SCOMT algorithm subsequently created an initial surface that was deformed using level set SFAS to minimize the total energy consisting of mean separation, edge, partial volume, rolling, distribution, background, shape, volume, smoothness, and curvature energies. The proposed segmentation algorithm was compared to manual segmentation whereby 21 tumors were evaluated using one-dimensional (1D) response evaluation criteria in solid tumors (RECIST), two-dimensional (2D) World Health Organization (WHO), and 3D volume measurements. Linear regression goodness-of-fit measures (r(2) = 0.63, p < 0.0001; r(2) = 0.87, p < 0.0001; and r(2) = 0.96, p < 0.0001), and Pearson correlation coefficients (r = 0.79, p < 0.0001; r = 0.93, p < 0.0001; and r = 0.98, p < 0.0001) for 1D, 2D, and 3D measurements, respectively, showed significant correlations between manual and algorithm results. Intra-observer intraclass correlation coefficients (ICC) demonstrated high reproducibility for algorithm (0.989-0.995, 0.996-0.997, and 0.999-0.999) and manual measurements (0.975-0.993, 0.985-0.993, and 0.980-0.992) for 1D, 2D, and 3D measurements, respectively. The intra-observer coefficient of variation (CV%) was low for algorithm (3.09%-4.67%, 4.85%-5.84%, and 5.65%-5.88%) and manual observers (4.20%-6.61%, 8.14%-9.57%, and 14.57%-21.61%) for 1D, 2D, and 3D measurements, respectively. The authors developed an automated segmentation algorithm requiring only that the operator select the tumor to measure pulmonary metastatic tumors in 1D, 2D, and 3D. Algorithm and manual measurements were significantly correlated. Since the algorithm segmentation involves selection of a single seed point, it resulted in reduced intra-observer variability and decreased time, for making the measurements.

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed tomore » tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.« less

WE-H-202-03: Accounting for Large Geometric Changes During Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hugo, G.

2016-06-15

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed tomore » tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.« less

WE-H-202-02: Biomechanical Modeling of Anatomical Response Over the Course of Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brock, K.

2016-06-15

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed tomore » tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.« less

WE-H-202-01: Memorial Introduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirby, N.

2016-06-15

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed tomore » tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.« less

Role of coupled cataclasis-pressure solution deformation in microearthquake activity along the creeping segment of the SAF: Inferences from studies of the SAFOD core samples

NASA Astrophysics Data System (ADS)

Hadizadeh, J.; Gratier, J.; Renard, F.; Mittempregher, S.; di Toro, G.

2009-12-01

Rocks encountered in the SAFOD drill hole represent deformation in the southern-most extent of the creeping segment of the SAF north of the Parkfield. At the site and toward the northwest the SAF is characterized by aseismic creep as well as strain release through repeating microearthquakes M<3. The activity is shown to be mostly distributed as clusters aligned in the slip direction, and occurring at depths of between 3 to 5 kilometers. It has been suggested that the events are due to frequent moment release from high strength asperities constituting only about 1% or less of the total fault surface area within an otherwise weak fault gouge. We studied samples selected from the SAFOD phase 3 cores (3142m -3296m MD) using high resolution scanning electron microscopy, cathodoluminescence imaging, X-ray fluorescence mapping, and energy dispersive X-ray spectroscopy. The observed microstructural deformation that is apparently relevant to the seismological data includes clear evidence of cyclic deformation events, cataclastic flow, and pressure solution creep with attendant vein sealing and fracture healing fabrics. Friction testing of drill cuttings and modeling by others suggest that the overall creep behavior in shale-siltstone gouge may be due to low bulk friction coefficient of 0.2-0.4 for the fault rock. Furthermore, the low resistivity zone extending to about 5km beneath the SAFOD-Middle Mountain area is believed to consist of a pod of fluid-filled fractured and porous rocks. Our microstructural data indicate that the foliated shale-siltstone cataclasites are, in a highly heterogeneous way, more porous and permeable than the host rock and therefore provide for structurally controlled enhanced fluid-rock interactions. This is consistent with the observed pressure solution deformation and the microstructural indications of transiently high fluid pressures. We hypothesize that while the friction laws defining stable sliding are prevalent in bulk deformation of the creeping segment, there exist the possibility of steady conditions for repetitive healing, dilation, and rupture of populations of stress-oriented patches due to operation of pressure solution creep along the fault zone. The limitation on the total area of the locked patches at any given time would be controlled primarily by the imposed tectonic and near field rates of slip and fluid flux within the local permeability structure. The available geophysical data for the creeping section of the SAF including hypocenter cluster distribution, moment release rate, seismic rupture area (∝ healed patch size), stress drop and return time characteristics point to a highly heterogeneous internal structure at the SAFOD site, and could be used to test the proposed coupled cataclasis-pressure solution microstructural model.

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: a comparison of CT and CT-MRI based tissue segmentation on simulated temperature.

PubMed

Verhaart, René F; Fortunati, Valerio; Verduijn, Gerda M; van der Lugt, Aad; van Walsum, Theo; Veenland, Jifke F; Paulides, Margarethus M

2014-12-01

In current clinical practice, head and neck (H&N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors' study is to investigate the relevance of using MRI in addition to CT for patient modeling in H&N HTP. CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H&N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRIdb). To quantify the relevance of MRI based segmentation for H&N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (Tmax) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRIdb. In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (Tmax: 38.0 °C) and CT and MRI (Tmax: 38.1 °C) result in similar simulated temperatures, while CT and MRIdb (Tmax: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.

Correcting for deformation in skin-based marker systems.

PubMed

Alexander, E J; Andriacchi, T P

2001-03-01

A new technique is described that reduces error due to skin movement artifact in the opto-electronic measurement of in vivo skeletal motion. This work builds on a previously described point cluster technique marker set and estimation algorithm by extending the transformation equations to the general deformation case using a set of activity-dependent deformation models. Skin deformation during activities of daily living are modeled as consisting of a functional form defined over the observation interval (the deformation model) plus additive noise (modeling error). The method is described as an interval deformation technique. The method was tested using simulation trials with systematic and random components of deformation error introduced into marker position vectors. The technique was found to substantially outperform methods that require rigid-body assumptions. The method was tested in vivo on a patient fitted with an external fixation device (Ilizarov). Simultaneous measurements from markers placed on the Ilizarov device (fixed to bone) were compared to measurements derived from skin-based markers. The interval deformation technique reduced the errors in limb segment pose estimate by 33 and 25% compared to the classic rigid-body technique for position and orientation, respectively. This newly developed method has demonstrated that by accounting for the changing shape of the limb segment, a substantial improvement in the estimates of in vivo skeletal movement can be achieved.

Development of Chinese reference man deformable surface phantom and its application to the influence of physique on electromagnetic dosimetry

NASA Astrophysics Data System (ADS)

Yu, D.; Wang, M.; Liu, Q.

2015-09-01

A reference man is a theoretical individual that represents the average anatomical structure and physiological and metabolic features of a specific group of people and has been widely used in radiation safety research. With the help of an advantage in deformation, the present work proposed a Chinese reference man adult-male polygon-mesh surface phantom based on the Visible Chinese Human segment image dataset by surface rendering and deforming. To investigate the influence of physique on electromagnetic dosimetry in humans, a series of human phantoms with 10th, 50th and 90th body mass index and body circumference percentile physiques for Chinese adult males were further constructed by deforming the Chinese reference man surface phantom. All the surface phantoms were then voxelized to perform electromagnetic field simulation in a frequency range of 20 MHz to 3 GHz using the finite-difference time-domain method and evaluate the whole-body average and organ average specific absorption rate and the ratios of absorbed energy in skin, fat and muscle to the whole body. The results indicate thinner physique leads to higher WBSAR and the volume of subcutaneous fat, the penetration depth of the electromagnetic field in tissues and standing-wave occurrence may be the influence factors of physique on electromagnetic dosimetry.

Comparison between isotropic linear-elastic law and isotropic hyperelastic law in the finite element modeling of the brachial plexus.

PubMed

Perruisseau-Carrier, A; Bahlouli, N; Bierry, G; Vernet, P; Facca, S; Liverneaux, P

2017-12-01

Augmented reality could help the identification of nerve structures in brachial plexus surgery. The goal of this study was to determine which law of mechanical behavior was more adapted by comparing the results of Hooke's isotropic linear elastic law to those of Ogden's isotropic hyperelastic law, applied to a biomechanical model of the brachial plexus. A model of finite elements was created using the ABAQUS ® from a 3D model of the brachial plexus acquired by segmentation and meshing of MRI images at 0°, 45° and 135° of shoulder abduction of a healthy subject. The offset between the reconstructed model and the deformed model was evaluated quantitatively by the Hausdorff distance and qualitatively by the identification of 3 anatomical landmarks. In every case the Hausdorff distance was shorter with Ogden's law compared to Hooke's law. On a qualitative aspect, the model deformed by Ogden's law followed the concavity of the reconstructed model whereas the model deformed by Hooke's law remained convex. In conclusion, the results of this study demonstrate that the behavior of Ogden's isotropic hyperelastic mechanical model was more adapted to the modeling of the deformations of the brachial plexus. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Use of controlled dynamic impacts on hierarchically structured seismically hazardous faults for seismically safe relaxation of shear stresses

NASA Astrophysics Data System (ADS)

Ruzhich, Valery V.; Psakhie, Sergey G.; Levina, Elena A.; Shilko, Evgeny V.; Grigoriev, Alexandr S.

2017-12-01

In the paper we briefly outline the experience in forecasting catastrophic earthquakes and the general problems in ensuring seismic safety. The purpose of our long-term research is the development and improvement of the methods of man-caused impacts on large-scale fault segments to safely reduce the negative effect of seismodynamic failure. Various laboratory and large-scale field experiments were carried out in the segments of tectonic faults in Baikal rift zone and in main cracks in block-structured ice cove of Lake Baikal using the developed measuring systems and special software for identification and treatment of deformation response of faulty segments to man-caused impacts. The results of the study let us to ground the necessity of development of servo-controlled technologies, which are able to provide changing the shear resistance and deformation regime of fault zone segments by applying vibrational and pulse triggering impacts. We suppose that the use of triggering impacts in highly stressed segments of active faults will promote transferring the geodynamic state of these segments from a metastable to a more stable and safe state.

The deformation behavior of the cervical spine segment

NASA Astrophysics Data System (ADS)

Kolmakova, T. V.; Rikun, Yu. A.

2017-09-01

The paper describes the model of the cervical spine segment (C3-C4) and the calculation results of its deformation behavior at flexion. The segment model was built based on the experimental literature data taking into account the presence of the cortical and cancellous bone tissue of vertebral bodies. Degenerative changes of the intervertebral disk (IVD) were simulated through a reduction of the disc height and an increase of Young's modulus. The construction of the geometric model of the cervical spine segment and the calculations of the stress-strain state were carried out in the ANSYS software complex. The calculation results show that the biggest protrusion of the IVD in bending direction of segment is observed when IVD height is reduced. The disc protrusion is reduced with an increase of Young's modulus. The largest protrusion in the direction of flexion of the segment is the intervertebral disk with height of 4.3 mm and elastic modulus of 2.5 MPa. The results of the study can be useful to specialists in the field of biomechanics, medical materials science and prosthetics.

Validating a new methodology for strain estimation from cardiac cine MRI

NASA Astrophysics Data System (ADS)

Elnakib, Ahmed; Beache, Garth M.; Gimel'farb, Georgy; Inanc, Tamer; El-Baz, Ayman

2013-10-01

This paper focuses on validating a novel framework for estimating the functional strain from cine cardiac magnetic resonance imaging (CMRI). The framework consists of three processing steps. First, the left ventricle (LV) wall borders are segmented using a level-set based deformable model. Second, the points on the wall borders are tracked during the cardiac cycle based on solving the Laplace equation between the LV edges. Finally, the circumferential and radial strains are estimated at the inner, mid-wall, and outer borders of the LV wall. The proposed framework is validated using synthetic phantoms of the material strains that account for the physiological features and the LV response during the cardiac cycle. Experimental results on simulated phantom images confirm the accuracy and robustness of our method.

Navigation system for minimally invasive esophagectomy: experimental study in a porcine model.

PubMed

Nickel, Felix; Kenngott, Hannes G; Neuhaus, Jochen; Sommer, Christof M; Gehrig, Tobias; Kolb, Armin; Gondan, Matthias; Radeleff, Boris A; Schaible, Anja; Meinzer, Hans-Peter; Gutt, Carsten N; Müller-Stich, Beat-Peter

2013-10-01

Navigation systems potentially facilitate minimally invasive esophagectomy and improve patient outcome by improving intraoperative orientation, position estimation of instruments, and identification of lymph nodes and resection margins. The authors' self-developed navigation system is highly accurate in static environments. This study aimed to test the overall accuracy of the navigation system in a realistic operating room scenario and to identify the different sources of error altering accuracy. To simulate a realistic environment, a porcine model (n = 5) was used with endoscopic clips in the esophagus as navigation targets. Computed tomography imaging was followed by image segmentation and target definition with the medical imaging interaction toolkit software. Optical tracking was used for registration and localization of animals and navigation instruments. Intraoperatively, the instrument was displayed relative to segmented organs in real time. The target registration error (TRE) of the navigation system was defined as the distance between the target and the navigation instrument tip. The TRE was measured on skin targets with the animal in the 0° supine and 25° anti-Trendelenburg position and on the esophagus during laparoscopic transhiatal preparation. On skin targets, the TRE was significantly higher in the 25° position, at 14.6 ± 2.7 mm, compared with the 0° position, at 3.2 ± 1.3 mm. The TRE on the esophagus was 11.2 ± 2.4 mm. The main source of error was soft tissue deformation caused by intraoperative positioning, pneumoperitoneum, surgical manipulation, and tissue dissection. The navigation system obtained acceptable accuracy with a minimally invasive transhiatal approach to the esophagus in a realistic experimental model. Thus the system has the potential to improve intraoperative orientation, identification of lymph nodes and adequate resection margins, and visualization of risk structures. Compensation methods for soft tissue deformation may lead to an even more accurate navigation system in the future.

Brain MR image segmentation using NAMS in pseudo-color.

PubMed

Li, Hua; Chen, Chuanbo; Fang, Shaohong; Zhao, Shengrong

2017-12-01

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

Detecting the transition to failure: wavelet analysis of multi-scale crack patterns at different confining pressures

NASA Astrophysics Data System (ADS)

Rizzo, R. E.; Healy, D.; Farrell, N. J.

2017-12-01

Numerous laboratory brittle deformation experiments have shown that a rapid transition exists in the behaviour of porous materials under stress: at a certain point, early formed tensile cracks interact and coalesce into a `single' narrow zone, the shear plane, rather than remaining distributed throughout the material. In this work, we present and apply a novel image processing tool which is able to quantify this transition between distributed (`stable') damage accumulation and localised (`unstable') deformation, in terms of size, density, and orientation of cracks at the point of failure. Our technique, based on a two-dimensional (2D) continuous Morlet wavelet analysis, can recognise, extract and visually separate the multi-scale changes occurring in the fracture network during the deformation process. We have analysed high-resolution SEM-BSE images of thin sections of Hopeman Sandstone (Scotland, UK) taken from core plugs deformed under triaxial conditions, with increasing confining pressure. Through this analysis, we can determine the relationship between the initial orientation of tensile microcracks and the final geometry of the through-going shear fault, exploiting the total areal coverage of the analysed image. In addition, by comparing patterns of fractures in thin sections derived from triaxial (σ1>σ2=σ3=Pc) laboratory experiments conducted at different confining pressures (Pc), we can quantitatively explore the relationship between the observed geometry and the inferred mechanical processes. The methodology presented here can have important implications for larger-scale mechanical problems related to major fault propagation. Just as a core plug scale fault localises through extension and coalescence of microcracks, larger faults also grow by extension and coalescence of segments in a multi-scale process by which microscopic cracks can ultimately lead to macroscopic faulting. Consequently, wavelet analysis represents a useful tool for fracture pattern recognition, applicable to the detection of the transitions occurring at the time of catastrophic rupture.

Identification of the optic nerve head with genetic algorithms.

PubMed

Carmona, Enrique J; Rincón, Mariano; García-Feijoó, Julián; Martínez-de-la-Casa, José M

2008-07-01

This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels. Next, a genetic algorithm was used to find an ellipse containing the maximum number of hypothesis points in an offset of its perimeter, considering some constraints. The ellipse thus obtained is the approximation to the ONH. The segmentation method is tested in a sample of 110 eye fundus images, belonging to 55 patients with glaucoma (23.1%) and eye hypertension (76.9%) and random selected from an eye fundus image base belonging to the Ophthalmology Service at Miguel Servet Hospital, Saragossa (Spain). The results obtained are competitive with those in the literature. The method's generalization capability is reinforced when it is applied to a different image base from the one used in our study and a discrepancy curve is obtained very similar to the one obtained in our image base. In addition, the robustness of the method proposed can be seen in the high percentage of images obtained with a discrepancy delta<5 (96% and 99% in our and a different image base, respectively). The results also confirm the hypothesis that the ONH contour can be properly approached with a non-deformable ellipse. Another important aspect of the method is that it directly provides the parameters characterising the shape of the papilla: lengths of its major and minor axes, its centre of location and its orientation with regard to the horizontal position.

Along strike variation of active fault arrays and their effect on landscape morphology of the northwestern Himalaya

NASA Astrophysics Data System (ADS)

Nennewitz, Markus; Thiede, Rasmus; Bookhagen, Bodo

2017-04-01

The location and magnitude of the active deformation of the Himalaya has been debated for decades, but several aspects remain unknown. For instance, the spatial distribution of the deformation and the shortening that ultimately sustains Himalayan topography and the activity of major fault zones are not well constrained neither for the present day and nor for Holocene and Quarternary timescales. Because of these weakly constrained factors, many previous studies have assumed that the structural setting and the fault geometry of the Himalaya is continuous along strike and similar to fault geometries of central Nepal. Thus, the sub-surface structural information from central Nepal have been projected along strike, but have not been verified at other locations. In this study we use digital topographic analysis of the NW Himalaya. We obtained catchment-averaged, normalized steepness indexes of longitudinal river profiles with drainage basins ranging between 5 and 250km2 and analyzed the relative change in their spatial distribution both along and across strike. More specific, we analyzed the relative changes of basins located in the footwall and in the hanging wall of major fault zones. Under the assumption that along strike changes in the normalized steepness index are primarily controlled by the activity of thrust segments, we revealed new insights in the tectonic deformation and uplift pattern. Our results show three different segments along the northwest Himalaya, which are located, from east to west, in Garwhal, Chamba and Kashmir Himalaya. These have formed independent orogenic segments characterized by significant changes in their structural architecture and fault geometry. Moreover, their topographic changes indicate strong variations on fault displacement rates across first-order fault zones. With the help of along- and across-strike profiles, we were able to identify fault segments of pronounced fault activity across MFT, MBT, and the PT2 and identify the location of along strike changes which are interpreted as their segment boundaries. In addition to the steepness indices we use the accumulation of elevation data as a proxy for the strain that has been accumulated over a specific distance. Thus, despite the changes in topography, structural setting, and kinematics along the NW Himalaya we observe that the topography of the orogen is in good agreement with recently measured convergence rates obtained from GPS campaigns. These data suggest reduced crustal shortening towards the northwest. Deformation in the Central Himalaya has been explained either by in-sequence thrusting along the MFT that localize the entire Holocene shortening or a combination of this with out-of-sequence thrusting in the vicinity of the PT2. In contrast to these conceptual models, we propose that the segmented NW Himalaya is a product of the synchronous activity of different fault segments, accommodating the crustal shortening along three independently deforming organic segments. The lateral discontinuity of these segments is responsible for the accommodation of the variation in the deformation and the maintenance of the topography of the Himalaya in NW India.

New High-Resolution 3D Imagery of Fault Deformation and Segmentation of the San Onofre and San Mateo Trends in the Inner California Borderlands

NASA Astrophysics Data System (ADS)

Holmes, J. J.; Driscoll, N. W.; Kent, G. M.; Bormann, J. M.; Harding, A. J.

2015-12-01

The Inner California Borderlands (ICB) is situated off the coast of southern California and northern Baja. The structural and geomorphic characteristics of the area record a middle Oligocene transition from subduction to microplate capture along the California coast. Marine stratigraphic evidence shows large-scale extension and rotation overprinted by modern strike-slip deformation. Geodetic and geologic observations indicate that approximately 6-8 mm/yr of Pacific-North American relative plate motion is accommodated by offshore strike-slip faulting in the ICB. The farthest inshore fault system, the Newport-Inglewood Rose Canyon (NIRC) fault complex is a dextral strike-slip system that extends primarily offshore approximately 120 km from San Diego to the San Joaquin Hills near Newport Beach, California. Based on trenching and well data, the NIRC fault system Holocene slip rate is 1.5-2.0 mm/yr to the south and 0.5-1.0 mm/yr along its northern extent. An earthquake rupturing the entire length of the system could produce an Mw 7.0 earthquake or larger. West of the main segments of the NIRC fault complex are the San Mateo and San Onofre fault trends along the continental slope. Previous work concluded that these were part of a strike-slip system that eventually merged with the NIRC complex. Others have interpreted these trends as deformation associated with the Oceanside Blind Thrust fault purported to underlie most of the region. In late 2013, we acquired the first high-resolution 3D P-Cable seismic surveys (3.125 m bin resolution) of the San Mateo and San Onofre trends as part of the Southern California Regional Fault Mapping project aboard the R/V New Horizon. Analysis of these volumes provides important new insights and constraints on the fault segmentation and transfer of deformation. Based on the new 3D sparker seismic data, our preferred interpretation for the San Mateo and San Onofre fault trends is they are transpressional features associated with westward jogs along right lateral fault strands splaying off the NIRC fault. Such a scenario also is consistent with observations from the 3D boomer volume along the shelf and upper slope that images westward stepping faults splaying off the NIRC system.

Using digital photogrammetry to constrain the segmentation of Paleocene volcanic marker horizons within the Nuussuaq basin

NASA Astrophysics Data System (ADS)

Vest Sørensen, Erik; Pedersen, Asger Ken

2017-04-01

Digital photogrammetry is used to map important volcanic marker horizons within the Nuussuaq Basin, West Greenland. We use a combination of oblique stereo images acquired from helicopter using handheld cameras and traditional aerial photographs. The oblique imagery consists of scanned stereo photographs acquired with analogue cameras in the 90´ties and newer digital images acquired with high resolution digital consumer cameras. Photogrammetric software packages SOCET SET and 3D Stereo Blend are used for controlling the seamless movement between stereo-models at different scales and viewing angles and the mapping is done stereoscopically using 3d monitors and the human stereopsis. The approach allows us to map in three dimensions three characteristic marker horizons (Tunoqqu, Kûgánguaq and Qordlortorssuaq Members) within the picritic Vaigat Formation. They formed toward the end of the same volcanic episode and are believed to be closely related in time. They formed an approximately coherent sub-horizontal surface, the Tunoqqu Surface that at the time of formation covered more than 3100 km2 on Disko and Nuussuaq. Our mapping shows that the Tunoqqu Surface is now segmented into areas of different elevation and structural trend as a result of later tectonic deformation. This is most notable on Nuussuaq where the western part is elevated and in parts highly faulted. In western Nuussuaq the surface has been uplifted and faulted so that it now forms an asymmetric anticline. The flanks of the anticline are coincident with two N-S oriented pre-Tunoqqu extensional faults. The deformation of the Tunoqqu surface could be explained by inversion of older extensional faults due to an overall E-W directed compressive regime in the late Paleocene.

Joint Segmentation and Deformable Registration of Brain Scans Guided by a Tumor Growth Model

PubMed Central

Gooya, Ali; Pohl, Kilian M.; Bilello, Michel; Biros, George; Davatzikos, Christos

2011-01-01

This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR ) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth. PMID:21995070

Joint segmentation and deformable registration of brain scans guided by a tumor growth model.

PubMed

Gooya, Ali; Pohl, Kilian M; Bilello, Michel; Biros, George; Davatzikos, Christos

2011-01-01

This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

PubMed

Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

2012-01-01

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images.

PubMed

Pachiyappan, Arulmozhivarman; Das, Undurti N; Murthy, Tatavarti Vsp; Tatavarti, Rao

2012-06-13

We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.

Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images

PubMed Central

2012-01-01

We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized. PMID:22695250

SU-E-J-126: An Online Replanning Method for FFF Beams Without Couch Shift

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahunbay, E; Ates, O; Li, X

2015-06-15

Purpose: In a situation that couch shift for patient positioning is not preferred or prohibited (e.g., MR-Linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening filter free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here we propose a new 2-step process to address both the translational effect of FFF beams and the target deformation. Methods: The replanning method consists of an offline and an online steps. The offline step is to create a series of pre-shifted plans (PSP) obtained by a so calledmore » “warm start” optimization (starting optimization from the original plan, rather from scratch) at a series of isocenter shifts with fixed distance (e.g. 2 cm, at x,y,z = 2,0,0 ; 2,2,0 ; 0,2,0; …;− 2,0,0). The PSPs all have the same number of segments with very similar shapes, since the warm-start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two-step process is completely automated, and instantaneously fast (no optimization or dose calculation needed). The previously-developed SAM algorithm is then applied for daily deformation. We tested the method on sample prostate and pancreas cases. Results: The two-step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. The whole process takes the same time as the previously reported SAM process (5–10 min). Conclusion: The new two-step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation requiring no additional time from the SAM process. This research was supported by Elekta inc. (Crawley, UK)« less

Right ventricular mechanics in hypertrophic cardiomyopathy using feature tracking

PubMed Central

Badran, Hala Mahfouz; Soliman, Mahmood; Hassan, Hesham; Abdelfatah, Raed; Saadan, Haythem; Yacoub, Magdi

2013-01-01

Objectives: Right ventricular (RV) mechanics in hypertrophic cardiomyopathy (HCM) are poorly understood. We investigate global and regional deformation of the RV in HCM and its relationship to LV phenotype, using 2D strain vector velocity imaging (VVI). Methods: 100 HCM patients (42% females, 41 ± 19 years) and 30 control patients were studied using VVI. Longitudinal peak systolic strain (ϵsys), strain rate (SR), time to peak (ϵ) (TTP), displacement of RV free wall (RVFW) and septal wall were analyzed. Similar parameters were quantified in LV septal, lateral, anterior and inferior segments. Intra-V-delay was defined as SD of TTP. Inter-V-delay was estimated from TTP difference between the most delayed LV segment & RVFW. Results: ϵsys and SR of both RV & LV, showed loss of base to apex gradient and significant decline in HCM (p < 0.001). Deformation variables estimated from RVFW were strongly correlated with each other (r = 0.93, p < 0.0001). Both were directly related to LV ϵsys, SRsys, SRe, ejection fraction (EF)%, RVFW displacement (P < 0.001) and inversely related to age, positive family history (p < 0.004, 0.005), RV wall thickness, maximum wall thickness (MWT), intra-V-delay, LA volume (P < 0.0001), LVOT gradient (p < 0.02, 0.005) respectively. ROC curves were constructed to explore the cut-off point that discriminates RV dysfunction. Global and RVFW ϵsys: − 19.5% shows 77, 70% sensitivity & 97% specificity, SRsys: − 1.3s− 1 shows 82, 70% sensitivity & 30% specificity. Multivariate analyses revealed that RVFW displacement (β = − 0.9, p < 0.0001) and global LV SRsys (β = 5.9, p < 0.0001) are independent predictors of global RV deformation. Conclusions: Impairment of RV deformation is evident in HCM using feature tracking. It is independently influenced by LV mechanics and correlated to the severity of LV phenotype. RVFW deformation analysis and global RV assessment are comparable. PMID:24689019

An online replanning method using warm start optimization and aperture morphing for flattening-filter-free beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahunbay, Ergun E., E-mail: eahunbay@mcw.edu; Ates,

Purpose: In a situation where a couch shift for patient positioning is not preferred or prohibited (e.g., MR-linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening-filter-free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here the authors propose a new two-step process to address both the translational effect of FFF beams and the target deformation. Methods: The replanning method consists of an offline and an online step. The offline step is to create a series of preshifted-plans (PSPs) obtained by a so-called “warm start”more » optimization (starting optimization from the original plan, rather than from scratch) at a series of isocenter shifts. The PSPs all have the same number of segments with very similar shapes, since the warm start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by picking the closest PSP or linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two-step process is completely automated and almost instantaneous (no optimization or dose calculation needed). The previously developed SAM algorithm is then applied for daily deformation. The authors tested the method on sample prostate and pancreas cases. Results: The two-step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. Plan interpolation method is effective in diminishing the unflat beam effect and may allow reducing the required number of PSPs. The whole process takes the same time as the previously reported SAM process (5–10 min). Conclusions: The new two-step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation except the delineation of target contour required by the SAM process.« less

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

NASA Astrophysics Data System (ADS)

Robins, Marthony; Solomon, Justin; Sahbaee, Pooyan; Sedlmair, Martin; Choudhury, Kingshuk Roy; Pezeshk, Aria; Sahiner, Berkman; Samei, Ehsan

2017-09-01

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (Mea{{n}RHD} , ST{{D}RHD} and C{{V}RHD}{) }~ of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the (C{{V}RHD} ) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

PubMed Central

Robins, Marthony; Solomon, Justin; Sahbaee, Pooyan; Sedlmair, Martin; Choudhury, Kingshuk Roy; Pezeshk, Aria; Sahiner, Berkman; Samei, Ehsan

2017-01-01

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (MeanRHD, and STDRHD CVRHD) of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the (CVRHD) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology. PMID:28786399

Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

PubMed

Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

2014-01-01

Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection -A First Study

PubMed Central

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Orthaus, Sandra; Achilefu, Samuel; Nussinov, Zohar

2014-01-01

Inspired by a multi-resolution community detection (MCD) based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Further, using the proposed method, the mean-square error (MSE) in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The MCD method appeared to perform better than a popular spectral clustering based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in MSE with increasing resolution. PMID:24251410

BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

NASA Astrophysics Data System (ADS)

Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

2006-01-01

Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

Technical report on semiautomatic segmentation using the Adobe Photoshop.

PubMed

Park, Jin Seo; Chung, Min Suk; Hwang, Sung Bae; Lee, Yong Sook; Har, Dong-Hwan

2005-12-01

The purpose of this research is to enable users to semiautomatically segment the anatomical structures in magnetic resonance images (MRIs), computerized tomographs (CTs), and other medical images on a personal computer. The segmented images are used for making 3D images, which are helpful to medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was scanned to make 557 MRIs. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL and manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a similar manner, 13 anatomical structures in 8,590 anatomical images were segmented. Proper segmentation was verified by making 3D images from the segmented images. Semiautomatic segmentation using Adobe Photoshop is expected to be widely used for segmentation of anatomical structures in various medical images.

Experimental comparison of landmark-based methods for 3D elastic registration of pre- and postoperative liver CT data

NASA Astrophysics Data System (ADS)

Lange, Thomas; Wörz, Stefan; Rohr, Karl; Schlag, Peter M.

2009-02-01

The qualitative and quantitative comparison of pre- and postoperative image data is an important possibility to validate surgical procedures, in particular, if computer assisted planning and/or navigation is performed. Due to deformations after surgery, partially caused by the removal of tissue, a non-rigid registration scheme is a prerequisite for a precise comparison. Interactive landmark-based schemes are a suitable approach, if high accuracy and reliability is difficult to achieve by automatic registration approaches. Incorporation of a priori knowledge about the anatomical structures to be registered may help to reduce interaction time and improve accuracy. Concerning pre- and postoperative CT data of oncological liver resections the intrahepatic vessels are suitable anatomical structures. In addition to using branching landmarks for registration, we here introduce quasi landmarks at vessel segments with high localization precision perpendicular to the vessels and low precision along the vessels. A comparison of interpolating thin-plate splines (TPS), interpolating Gaussian elastic body splines (GEBS) and approximating GEBS on landmarks at vessel branchings as well as approximating GEBS on the introduced vessel segment landmarks is performed. It turns out that the segment landmarks provide registration accuracies as good as branching landmarks and can improve accuracy if combined with branching landmarks. For a low number of landmarks segment landmarks are even superior.

Improve accuracy for automatic acetabulum segmentation in CT images.

PubMed

Liu, Hao; Zhao, Jianning; Dai, Ning; Qian, Hongbo; Tang, Yuehong

2014-01-01

Separation of the femur head and acetabulum is one of main difficulties in the diseased hip joint due to deformed shapes and extreme narrowness of the joint space. To improve the segmentation accuracy is the key point of existing automatic or semi-automatic segmentation methods. In this paper, we propose a new method to improve the accuracy of the segmented acetabulum using surface fitting techniques, which essentially consists of three parts: (1) design a surface iterative process to obtain an optimization surface; (2) change the ellipsoid fitting to two-phase quadric surface fitting; (3) bring in a normal matching method and an optimization region method to capture edge points for the fitting quadric surface. Furthermore, this paper cited vivo CT data sets of 40 actual patients (with 79 hip joints). Test results for these clinical cases show that: (1) the average error of the quadric surface fitting method is 2.3 (mm); (2) the accuracy ratio of automatically recognized contours is larger than 89.4%; (3) the error ratio of section contours is less than 10% for acetabulums without severe malformation and less than 30% for acetabulums with severe malformation. Compared with similar methods, the accuracy of our method, which is applied in a software system, is significantly enhanced.

Technical Note: Radiological properties of tissue surrogates used in a multimodality deformable pelvic phantom for MR-guided radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niebuhr, Nina I., E-mail: n.niebuhr@dkfz.de; Johnen, Wibke; Güldaglar, Timur

Purpose: Phantom surrogates were developed to allow multimodal [computed tomography (CT), magnetic resonance imaging (MRI), and teletherapy] and anthropomorphic tissue simulation as well as materials and methods to construct deformable organ shapes and anthropomorphic bone models. Methods: Agarose gels of variable concentrations and loadings were investigated to simulate various soft tissue types. Oils, fats, and Vaseline were investigated as surrogates for adipose tissue and bone marrow. Anthropomorphic shapes of bone and organs were realized using 3D-printing techniques based on segmentations of patient CT-scans. All materials were characterized in dual energy CT and MRI to adapt CT numbers, electron density, effectivemore » atomic number, as well as T1- and T2-relaxation times to patient and literature values. Results: Soft tissue simulation could be achieved with agarose gels in combination with a gadolinium-based contrast agent and NaF to simulate muscle, prostate, and tumor tissues. Vegetable oils were shown to be a good representation for adipose tissue in all modalities. Inner bone was realized using a mixture of Vaseline and K{sub 2}HPO{sub 4}, resulting in both a fatty bone marrow signal in MRI and inhomogeneous areas of low and high attenuation in CT. The high attenuation of outer bone was additionally adapted by applying gypsum bandages to the 3D-printed hollow bone case with values up to 1200 HU. Deformable hollow organs were manufactured using silicone. Signal loss in the MR images based on the conductivity of the gels needs to be further investigated. Conclusions: The presented surrogates and techniques allow the customized construction of multimodality, anthropomorphic, and deformable phantoms as exemplarily shown for a pelvic phantom, which is intended to study adaptive treatment scenarios in MR-guided radiation therapy.« less

Technical Note: Radiological properties of tissue surrogates used in a multimodality deformable pelvic phantom for MR-guided radiotherapy.

PubMed

Niebuhr, Nina I; Johnen, Wibke; Güldaglar, Timur; Runz, Armin; Echner, Gernot; Mann, Philipp; Möhler, Christian; Pfaffenberger, Asja; Jäkel, Oliver; Greilich, Steffen

2016-02-01

Phantom surrogates were developed to allow multimodal [computed tomography (CT), magnetic resonance imaging (MRI), and teletherapy] and anthropomorphic tissue simulation as well as materials and methods to construct deformable organ shapes and anthropomorphic bone models. Agarose gels of variable concentrations and loadings were investigated to simulate various soft tissue types. Oils, fats, and Vaseline were investigated as surrogates for adipose tissue and bone marrow. Anthropomorphic shapes of bone and organs were realized using 3D-printing techniques based on segmentations of patient CT-scans. All materials were characterized in dual energy CT and MRI to adapt CT numbers, electron density, effective atomic number, as well as T1- and T2-relaxation times to patient and literature values. Soft tissue simulation could be achieved with agarose gels in combination with a gadolinium-based contrast agent and NaF to simulate muscle, prostate, and tumor tissues. Vegetable oils were shown to be a good representation for adipose tissue in all modalities. Inner bone was realized using a mixture of Vaseline and K2HPO4, resulting in both a fatty bone marrow signal in MRI and inhomogeneous areas of low and high attenuation in CT. The high attenuation of outer bone was additionally adapted by applying gypsum bandages to the 3D-printed hollow bone case with values up to 1200 HU. Deformable hollow organs were manufactured using silicone. Signal loss in the MR images based on the conductivity of the gels needs to be further investigated. The presented surrogates and techniques allow the customized construction of multimodality, anthropomorphic, and deformable phantoms as exemplarily shown for a pelvic phantom, which is intended to study adaptive treatment scenarios in MR-guided radiation therapy.

Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy

NASA Astrophysics Data System (ADS)

Acosta, Oscar; Dowling, Jason; Cazoulat, Guillaume; Simon, Antoine; Salvado, Olivier; de Crevoisier, Renaud; Haigron, Pascal

The prediction of toxicity is crucial to managing prostate cancer radiotherapy (RT). This prediction is classically organ wise and based on the dose volume histograms (DVH) computed during the planning step, and using for example the mathematical Lyman Normal Tissue Complication Probability (NTCP) model. However, these models lack spatial accuracy, do not take into account deformations and may be inappropiate to explain toxicity events related with the distribution of the delivered dose. Producing voxel wise statistical models of toxicity might help to explain the risks linked to the dose spatial distribution but is challenging due to the difficulties lying on the mapping of organs and dose in a common template. In this paper we investigate the use of atlas based methods to perform the non-rigid mapping and segmentation of the individuals' organs at risk (OAR) from CT scans. To build a labeled atlas, 19 CT scans were selected from a population of patients treated for prostate cancer by radiotherapy. The prostate and the OAR (Rectum, Bladder, Bones) were then manually delineated by an expert and constituted the training data. After a number of affine and non rigid registration iterations, an average image (template) representing the whole population was obtained. The amount of consensus between labels was used to generate probabilistic maps for each organ. We validated the accuracy of the approach by segmenting the organs using the training data in a leave one out scheme. The agreement between the volumes after deformable registration and the manually segmented organs was on average above 60% for the organs at risk. The proposed methodology provides a way to map the organs from a whole population on a single template and sets the stage to perform further voxel wise analysis. With this method new and accurate predictive models of toxicity will be built.

Image-Guided Radiation Therapy: the potential for imaging science research to improve cancer treatment outcomes

NASA Astrophysics Data System (ADS)

Williamson, Jeffrey

2008-03-01

The role of medical imaging in the planning and delivery of radiation therapy (RT) is rapidly expanding. This is being driven by two developments: Image-guided radiation therapy (IGRT) and biological image-based planning (BIBP). IGRT is the systematic use of serial treatment-position imaging to improve geometric targeting accuracy and/or to refine target definition. The enabling technology is the integration of high-performance three-dimensional (3D) imaging systems, e.g., onboard kilovoltage x-ray cone-beam CT, into RT delivery systems. IGRT seeks to adapt the patient's treatment to weekly, daily, or even real-time changes in organ position and shape. BIBP uses non-anatomic imaging (PET, MR spectroscopy, functional MR, etc.) to visualize abnormal tissue biology (angiogenesis, proliferation, metabolism, etc.) leading to more accurate clinical target volume (CTV) delineation and more accurate targeting of high doses to tissue with the highest tumor cell burden. In both cases, the goal is to reduce both systematic and random tissue localization errors (2-5 mm for conventional RT) conformality so that planning target volume (PTV) margins (varying from 8 to 20 mm in conventional RT) used to ensure target volume coverage in the presence of geometric error, can be substantially reduced. Reduced PTV expansion allows more conformal treatment of the target volume, increased avoidance of normal tissue and potential for safe delivery of more aggressive dose regimens. This presentation will focus on the imaging science challenges posed by the IGRT and BIBP. These issues include: Development of robust and accurate nonrigid image-registration (NIR) tools: Extracting locally nonlinear mappings that relate, voxel-by-voxel, one 3D anatomic representation of the patient to differently deformed anatomies acquired at different time points, is essential if IGRT is to move beyond simple translational treatment plan adaptations. NIR is needed to map segmented and labeled anatomy from the pretreatment planning images to each daily treatment position image and to deformably map delivered dose distributions computed on each time instance of deformed anatomy, back to the reference 3D anatomy. Because biological imaging must be performed offline, NIR is needed to deformably map these images onto CT images acquired during treatment. Reducing target and organ contouring errors: As IGRT significantly reduces impact of differences between planning and treatment anatomy, RT targeting accuracy becomes increasingly dominated by the remaining systematic treatment-preparation errors, chiefly error in delineating the clinical target volume (CTV) and organs-at-risk. These delineation errors range from 1 mm to 5 mm. No single solution to this problem exists. For BIBP, a better understanding of tumor cell density vs. signal intensity is required. For anatomic CT imaging, improved image reconstruction techniques that improve contrast-to-noise ratio, reduce artifacts due to limited projection data, and incorporate prior information are promising. More sophisticated alternatives to the current concept fixed boundary anatomic structures are needed, e.g., probabilistic CTV representations that incorporate delineation uncertainties. Quantifying four-dimensional (4D) anatomy: For adaptive treatment planning to produce an optimal time sequence of delivery parameters, a 4D anatomic representation, the spatial trajectory through time of each tissue voxel, is needed. One approach is to use sequences of deformation vector fields derived by non-rigidly registering each treatment image to the reference planning CT. One problem to be solved is prediction of future deformed anatomies from past behavior so that time delays inherent in any adaptive replanning feedback loop can be overcome. Another unsolved problem is quantification 4D anatomy uncertainties and how to incorporate such uncertainties into the treatment planning process to avoid geometric ``miss'' of the target tissue.

A Novel Junctional Tether Weave Technique for Adult Spinal Deformity: 2-Dimensional Operative Video.

PubMed

Buell, Thomas J; Mullin, Jeffrey P; Nguyen, James H; Taylor, Davis G; Garces, Juanita; Mazur, Marcus D; Buchholz, Avery L; Shaffrey, Mark E; Yen, Chun-Po; Shaffrey, Christopher I; Smith, Justin S

2018-06-05

Proximal junctional kyphosis (PJK) is a common problem after multilevel spine instrumentation for adult spinal deformity. Various anti-PJK techniques such as junctional tethers for ligamentous augmentation have been proposed. We present an operative video demonstrating technical nuances of junctional tether "weave" application. A 70-yr-old male with prior L2-S1 instrumented fusion presented with worsening back pain and posture. Imaging demonstrated pathological loss of lumbar lordosis (flat back deformity), proximal junctional failure, and pseudarthrosis. The patient had severe global and segmental sagittal malalignment, with sagittal vertical axis (SVA, C7-plumbline) measuring 22.3 cm, pelvic incidence (PI) 55°, lumbar lordosis (LL) 8° in kyphosis, pelvic tilt (PT) 30°, and thoracic kyphosis (TK) 6°. The patient gave informed consent for surgery and use of imaging for medical publication. Briefly, surgery first involved re-instrumentation with bilateral pedicle screws from T10 to S1. After right-sided iliac screw fixation (left-sided iliac screw fixation was not performed due to extensive prior iliac crest bone graft harvesting), we then completed a L2-3 Smith-Petersen osteotomy, extended L4 pedicle subtraction osteotomy, and L3-4 interbody arthrodesis with a 12° lordotic cage (9 × 14 × 40 mm). Cobalt Chromium rods were placed spanning the instrumentation bilaterally, and accessory supplemental rods spanning the PSO were attached. An anti-PJK junctional tether "weave" was then implemented using 4.5 mm polyethylene tape (Mersilene tape [Ethicon, Somerville, New Jersey]). Postoperative imaging demonstrated improved alignment (SVA 2.8 cm, PI 55°, LL 53°, PT 25°, TK 45°) and no significant neurological complications occurred during convalescence or at 6 mo postop.

Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning.

PubMed

Arabi, Hossein; Koutsouvelis, Nikolaos; Rouzaud, Michel; Miralbell, Raymond; Zaidi, Habib

2016-09-07

Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of  -1.5  ±  5.0% (mean  ±  SD) in bony structures compared to  -19.9  ±  11.8% and  -8.1  ±  8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40  ±  7.56%, 96.00  ±  4.11% and 97.67  ±  3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.

Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning

NASA Astrophysics Data System (ADS)

Arabi, Hossein; Koutsouvelis, Nikolaos; Rouzaud, Michel; Miralbell, Raymond; Zaidi, Habib

2016-09-01

Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of  -1.5  ±  5.0% (mean  ±  SD) in bony structures compared to  -19.9  ±  11.8% and  -8.1  ±  8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40  ±  7.56%, 96.00  ±  4.11% and 97.67  ±  3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.

A fast, model-independent method for cerebral cortical thickness estimation using MRI.

PubMed

Scott, M L J; Bromiley, P A; Thacker, N A; Hutchinson, C E; Jackson, A

2009-04-01

Several algorithms for measuring the cortical thickness in the human brain from MR image volumes have been described in the literature, the majority of which rely on fitting deformable models to the inner and outer cortical surfaces. However, the constraints applied during the model fitting process in order to enforce spherical topology and to fit the outer cortical surface in narrow sulci, where the cerebrospinal fluid (CSF) channel may be obscured by partial voluming, may introduce bias in some circumstances, and greatly increase the processor time required. In this paper we describe an alternative, voxel based technique that measures the cortical thickness using inversion recovery anatomical MR images. Grey matter, white matter and CSF are identified through segmentation, and edge detection is used to identify the boundaries between these tissues. The cortical thickness is then measured along the local 3D surface normal at every voxel on the inner cortical surface. The method was applied to 119 normal volunteers, and validated through extensive comparisons with published measurements of both cortical thickness and rate of thickness change with age. We conclude that the proposed technique is generally faster than deformable model-based alternatives, and free from the possibility of model bias, but suffers no reduction in accuracy. In particular, it will be applicable in data sets showing severe cortical atrophy, where thinning of the gyri leads to points of high curvature, and so the fitting of deformable models is problematic.

Optimization of myocardial deformation imaging in term and preterm infants.

PubMed

Poon, Chuen Y; Edwards, Julie M; Joshi, Suchita; Kotecha, Sailesh; Fraser, Alan G

2011-03-01

Myocardial deformation imaging is now used to assess regional ventricular function in infants but their small size presents particular technical challenges. We therefore investigated the determinants of reproducibility of myocardial longitudinal strain (ε) in term and preterm infants, in order to determine optimal technical settings. Repeated longitudinal ε measurements of the mid-segments of the septum, and the left and right ventricular free walls, were performed using five different computation distances (CDs; also called strain length) in 20 infants. The coefficients of variation (CV) were calculated for each CD. Overall, ε measurements were most reproducible with a CD of 6 mm (CV 11.7%). In preterm infants (<34 weeks gestation; mean ± SD diastolic LV length, 20.3 ± 3.5 mm), ε measurements were most reproducible with CD of 6 mm (CV 7.2%); in term infants (>37 weeks gestation; mean ± SD diastolic LV length, 29.6 ± 3.0 mm), ε measurements were most reproducible with CD of 10 mm (CV 13.2%). The reproducibility of measuring ε increased with higher frame rates, from CV of 17.3% at frame rates <180 per s to 11.7% for frame rates >180 per s and 9.6% for rates >248 per s. In newborn infants, tissue Doppler loops should be acquired at frame rates above 180 per s. Myocardial deformation analysis of preterm infants should be performed using a CD of 6 mm, whereas a CD of 10 mm is more reproducible in term infants.

In vitro flow assessment: from PC-MRI to computational fluid dynamics including fluid-structure interaction

NASA Astrophysics Data System (ADS)

Kratzke, Jonas; Rengier, Fabian; Weis, Christian; Beller, Carsten J.; Heuveline, Vincent

2016-04-01

Initiation and development of cardiovascular diseases can be highly correlated to specific biomechanical parameters. To examine and assess biomechanical parameters, numerical simulation of cardiovascular dynamics has the potential to complement and enhance medical measurement and imaging techniques. As such, computational fluid dynamics (CFD) have shown to be suitable to evaluate blood velocity and pressure in scenarios, where vessel wall deformation plays a minor role. However, there is a need for further validation studies and the inclusion of vessel wall elasticity for morphologies being subject to large displacement. In this work, we consider a fluid-structure interaction (FSI) model including the full elasticity equation to take the deformability of aortic wall soft tissue into account. We present a numerical framework, in which either a CFD study can be performed for less deformable aortic segments or an FSI simulation for regions of large displacement such as the aortic root and arch. Both of the methods are validated by means of an aortic phantom experiment. The computational results are in good agreement with 2D phase-contrast magnetic resonance imaging (PC-MRI) velocity measurements as well as catheter-based pressure measurements. The FSI simulation shows a characteristic vessel compliance effect on the flow field induced by the elasticity of the vessel wall, which the CFD model is not capable of. The in vitro validated FSI simulation framework can enable the computation of complementary biomechanical parameters such as the stress distribution within the vessel wall.

Performance of U-net based pyramidal lucas-kanade registration on free-breathing multi-b-value diffusion MRI of the kidney.

PubMed

Lv, Jun; Huang, Wenjian; Zhang, Jue; Wang, Xiaoying

2018-06-01

In free-breathing multi-b-value diffusion-weighted imaging (DWI), a series of images typically requires several minutes to collect. During respiration the kidney is routinely displaced and may also undergo deformation. These respiratory motion effects generate artifacts and these are the main sources of error in the quantification of intravoxel incoherent motion (IVIM) derived parameters. This work proposes a fully automated framework that combines a kidney segmentation to improve the registration accuracy. 10 healthy subjects were recruited to participate in this experiment. For the segmentation, U-net was adopted to acquire the kidney's contour. The segmented kidney then served as a region of interest (ROI) for the registration method, known as pyramidal Lucas-Kanade. Our proposed framework confines the kidney's solution range, thus increasing the pyramidal Lucas-Kanade's accuracy. To demonstrate the feasibility of our presented framework, eight regions of interest were selected in the cortex and medulla, and data stability was estimated by comparing the normalized root-mean-square error (NRMSE) values of the fitted data from the bi-exponential intravoxel incoherent motion model pre- and post- registration. The results show that the NRMSE was significantly lower after registration both in the cortex (p < 0.05) and medulla (p < 0.01) during free-breathing measurements. In addition, expert visual scoring of the derived apparent diffusion coefficient (ADC), f, D and D* maps indicated there were significant improvements in the alignment of the kidney in the post-registered image. The proposed framework can effectively reduce the motion artifacts of misaligned multi-b-value DWIs and the inaccuracies of the ADC, f, D and D* estimations. Advances in knowledge: This study demonstrates the feasibility of our proposed fully automated framework combining U-net based segmentation and pyramidal Lucas-Kanade registration method for improving the alignment of multi-b-value diffusion-weighted MRIs and reducing the inaccuracy of parameter estimation during free-breathing.

Review methods for image segmentation from computed tomography images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik

Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affectmore » the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan.« less

Application of image flow cytometry for the characterization of red blood cell morphology

NASA Astrophysics Data System (ADS)

Pinto, Ruben N.; Sebastian, Joseph A.; Parsons, Michael; Chang, Tim C.; Acker, Jason P.; Kolios, Michael C.

2017-02-01

Red blood cells (RBCs) stored in hypothermic environments for the purpose of transfusion have been documented to undergo structural and functional changes over time. One sign of the so-called RBC storage lesion is irreversible damage to the cell membrane. Consequently, RBCs undergo a morphological transformation from regular, deformable biconcave discocytes to rigid spheroechinocytes. The spherically shaped RBCs lack the deformability to efficiently enter microvasculature, thereby reducing the capacity of RBCs to oxygenate tissue. Blood banks currently rely on microscope techniques that include fixing, staining and cell counting in order to morphologically characterize RBC samples; these methods are labor intensive and highly subjective. This study presents a novel, high-throughput RBC morphology characterization technique using image flow cytometry (IFC). An image segmentation template was developed to process 100,000 images acquired from the IFC system and output the relative spheroechinocyte percentage. The technique was applied on samples extracted from two blood bags to monitor the morphological changes of the RBCs during in vitro hypothermic storage. The study found that, for a given sample of RBCs, the IFC method was twice as fast in data acquisition, and analyzed 250-350 times more RBCs than the conventional method. Over the lifespan of the blood bags, the mean spheroechinocyte population increased by 37%. Future work will focus on expanding the template to segregate RBC images into more subpopulations for the validation of the IFC method against conventional techniques; the expanded template will aid in establishing quantitative links between spheroechinocyte increase and other RBC storage lesion characteristics.

Marine forearc tectonics in the unbroken segment of the Northern Chile seismic gap

NASA Astrophysics Data System (ADS)

Geersen, J.; Behrmann, J.; Ranero, C. R.; Klaucke, I.; Kopp, H.; Lange, D.; Barckhausen, U.; Reichert, C. J.; Diaz-Naveas, J.

2016-12-01

While clearly occurring within the well-defined Northern Chile seismic gap, the 2014 Mw. 8.1 Iquique Earthquake only ruptured part of this gap, leaving large and possibly highly coupled areas untouched. These non-ruptured areas now may pose an elevated seismic hazard due to the transfer of stresses resulting from the 2014 rupture. Here we use recently collected multibeam bathymetric data, covering 90% of the North Chilean marine forearc, in combination with unpublished seismic reflection images to derive a tectonic map of the marine forearc in the unbroken segment of the seismic gap. In the entire study area we find evidence for widespread normal faulting. Seaward dipping normal faults locally extend close to the deformation front at the deep-sea trench under 8 km of water. Similar normal faults on the lower slope are neither observed further north (2014 Iquique earthquake area) nor further south (2007 Tocopilla earthquake area). On the upper continental slope, some of the normal faults dip towards the continent, defining N-S trending ridges that can be traced over tens of kilometers. The spatial variations in normal faulting do not correlate with obvious changes in the structural and tectonic setting of the subduction zone (e.g. plate convergence rate and direction, trench sediment thickness, subducting plate roughness). Thus, the permanent deformation recorded in the spatial distribution of faults may hold crucial information about the long-term seismic behavior of the Northern Chile seismic gap over multiple earthquake cycles. Although the structural interpretations cannot directly be translated into seismic hazard, the tectonic map serves to better understand deformation in the marine forearc in relation to the seismic cycle, historic seismicity, and the spatial distribution of plate-coupling.

Segmentation Fusion Techniques with Application to Plenoptic Images: A Survey.

NASA Astrophysics Data System (ADS)

Evin, D.; Hadad, A.; Solano, A.; Drozdowicz, B.

2016-04-01

The segmentation of anatomical and pathological structures plays a key role in the characterization of clinically relevant evidence from digital images. Recently, plenoptic imaging has emerged as a new promise to enrich the diagnostic potential of conventional photography. Since the plenoptic images comprises a set of slightly different versions of the target scene, we propose to make use of those images to improve the segmentation quality in relation to the scenario of a single image segmentation. The problem of finding a segmentation solution from multiple images of a single scene, is called segmentation fusion. This paper reviews the issue of segmentation fusion in order to find solutions that can be applied to plenoptic images, particularly images from the ophthalmological domain.

Cam impingement of the posterior femoral condyle in medial meniscal tears.

PubMed

Suganuma, Jun; Mochizuki, Ryuta; Yamaguchi, Kenji; Inoue, Yutaka; Yamabe, Eikou; Ueda, Yoshiyuki; Fujinaka, Tarou

2010-02-01

The aim of this study was to compare the results of meniscal repair of the medial meniscus with or without decompression of the posterior segment of the medial meniscus for the treatment of posteromedial tibiofemoral incongruence at full flexion (PMTFI), which induces deformation of the posterior segment on sagittal magnetic resonance imaging (MRI). For more than 2 years, we followed up 27 patients with PMTFI who were classified into the following 2 groups. Group 1 included 8 patients (5 male joints and 3 female joints) with a medial meniscal tear with instability at the site of the tear who underwent meniscal repair. The mean age was 23.6 years. Group 2 included 19 patients (16 male joints and 3 female joints) who had a meniscal tear with instability at the site of the tear and underwent meniscal repair and decompression. The mean age was 26.5 years. In decompression of the posterior segment, redundant bone tissue on the most proximal part of the medial femoral condyle was excised. The patients were assessed by use of the Lysholm score, sagittal MRI at full flexion, and arthroscopic examination. There were no statistical differences in mean Lysholm score between the 2 groups before surgery, but the mean score in group 2 was significantly higher than that in group 1 after surgery. Meniscal deformation of the posterior segment at full flexion on MRI disappeared in all cases after decompression. On second-look arthroscopy, the rate of complete healing at the site of the tear was 0% in group 1 but 57% in group 2, and it was significantly different between these groups. The addition of decompression of the posterior segment of the medial meniscus to meniscal repair of knee joints with PMTFI allowed more room for the medial meniscus to accommodate and improved both function of the knee joint and the rate of success of repair of isolated medial meniscal tears in patients who regularly performed full knee flexion. (c) 2010 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Late Pleistocene-Holocene Activity of the Strike-slip Xianshuihe Fault Zone, Tibetan Plateau, Inferred from Tectonic Landforms

NASA Astrophysics Data System (ADS)

Lin, A.; Yan, B.

2017-12-01

Knowledges on the activity of the strike-slip fault zones on the Tibetan Plateau have been promoted greatly by the interpretation of remote sensing images (Molnar and Tapponnier, 1975; Tapponnier and Molnar, 1977). The active strike-slip Xianshuihe-Xiaojiang Fault System (XXFS), with the geometry of an arc projecting northeastwards, plays an important role in the crustal deformation of the Tibetan Plateau caused by the continental collision between the Indian and Eurasian plates. The Xianshuihe Fault Zone (XFZ) is located in the central segment of the XXFS and extends for 370 km, with a maximum sinistral offset of 60 km since 13‒5 Ma. In this study, we investigated the tectonic landforms and slip rate along the central segment of the left-lateral strike-slip XFZ. Field investigations and analysis of ttectonic landforms show that horizontal offset has been accumulated on the topographical markers of different scales that developed since the Last Glacial Maximum (LGM). The central segment of the XFZ is composed of three major faults: Yalahe, Selaha, and Zheduotang faults showing a right-stepping echelon pattern, that is characterized by systematical offset of drainages, alluvial fans and terrace risers with typical scissoring structures, indicating a structural feature of left-lateral strike-slip fault. Based on the offset glacial morphology and radiocarbon dating ages, we estimate the Late Pleistocene-Holocene slip rate to be 10 mm/yr for the central segment of the XFZ, which is consistent with that estimated from the GPS observations and geological evidence as reported previously. Across the central segment of the XFZ, the major Selaha and Zheduotang faults participate a slip rate of 5.8 mm/yr and 3.4 mm/yr, respectively. Detailed investigations of tectonic landforms are essential for the understanding the activity of active faults. Our findings suggest that the left-lateral slipping of the XFZ partitions the deformation of eastward extrusion and northeastward shortening of the central Tibetan Plateau to accommodate the continuing penetration of the Indian plate into the Eurasian plate.

3D reconstruction and analysis of wing deformation in free-flying dragonflies.

PubMed

Koehler, Christopher; Liang, Zongxian; Gaston, Zachary; Wan, Hui; Dong, Haibo

2012-09-01

Insect wings demonstrate elaborate three-dimensional deformations and kinematics. These deformations are key to understanding many aspects of insect flight including aerodynamics, structural dynamics and control. In this paper, we propose a template-based subdivision surface reconstruction method that is capable of reconstructing the wing deformations and kinematics of free-flying insects based on the output of a high-speed camera system. The reconstruction method makes no rigid wing assumptions and allows for an arbitrary arrangement of marker points on the interior and edges of each wing. The resulting wing surfaces are projected back into image space and compared with expert segmentations to validate reconstruction accuracy. A least squares plane is then proposed as a universal reference to aid in making repeatable measurements of the reconstructed wing deformations. Using an Eastern pondhawk (Erythimus simplicicollis) dragonfly for demonstration, we quantify and visualize the wing twist and camber in both the chord-wise and span-wise directions, and discuss the implications of the results. In particular, a detailed analysis of the subtle deformation in the dragonfly's right hindwing suggests that the muscles near the wing root could be used to induce chord-wise camber in the portion of the wing nearest the specimen's body. We conclude by proposing a novel technique for modeling wing corrugation in the reconstructed flapping wings. In this method, displacement mapping is used to combine wing surface details measured from static wings with the reconstructed flapping wings, while not requiring any additional information be tracked in the high speed camera output.

Heart deformation analysis: measuring regional myocardial velocity with MR imaging.

PubMed

Lin, Kai; Collins, Jeremy D; Chowdhary, Varun; Markl, Michael; Carr, James C

2016-07-01

The aim of the present study was to test the hypothesis that heart deformation analysis (HDA) may serve as an alternative for the quantification of regional myocardial velocity. Nineteen healthy volunteers (14 male and 5 female) without documented cardiovascular diseases were recruited following the approval of the institutional review board (IRB). For each participant, cine images (at base, mid and apex levels of the left ventricle [LV]) and tissue phase mapping (TPM, at same short-axis slices of the LV) were acquired within a single magnetic resonance (MR) scan. Regional myocardial velocities in radial and circumferential directions acquired with HDA (Vrr and Vcc) and TPM (Vr and VФ) were measured during the cardiac cycle. HDA required shorter processing time compared to TPM (2.3 ± 1.1 min/case vs. 9.5 ± 3.7 min/case, p < 0.001). Moderate to good correlations between velocity components measured with HDA and TPM could be found on multiple myocardial segments (r = 0.460-0.774) and slices (r = 0.409-0.814) with statistical significance (p < 0.05). However, significant biases of velocity measures at regional myocardial areas between HDA and TPM were also noticed. By providing comparable velocity measures as TPM does, HDA may serve as an alternative for measuring regional myocardial velocity with a faster image processing procedure.

Strain localization along micro-boudinage

NASA Astrophysics Data System (ADS)

Chatziioannou, Eleftheria; Rogowitz, Anna; Grasemann, Bernhard; Habler, Gerlinde; Soukis, Konstantinos; Schneider, David

2016-04-01

The progressive development of boudinage strongly depends on the kinematic framework and the mechanical properties of the boudinaged layer and host rock. A common type of boudin, which can often be observed in natural examples, is the domino boudinage. This boudin type typically reflects a strong competency contrast of the interlayered rock sequences. Numerical models have shown that a relatively high amount of strain is necessary in order to develop separated boudin segments. With ongoing deformation and consequent rotation of the individual segments into the shear direction, the terminal sectors tend to experience a higher rotation rate, progressively resulting in isoclinal folding. Whereas most investigations of domino boudinage are cm- to dm-scale examples, we examined one order of magnitude smaller examples, where the deformation mechanism between the segments and the matrix could be directly investigated. The samples are from Kalymnos Island located in the southeastern Aegean Sea (Dodecanese islands-Greece). The analysed sample belongs to the upper unit of the pre-Alpidic basement, which consists of a succession of marbles, which were deformed under lower-greenschist facies conditions during the Variscan orogeny. 40Ar/39Ar geochronological dating on white micas in the adjacent upper quartz-mica schists unit yielded deformation ages between 240 and 334 Ma. The calcitic marble comprises boudinaged dolomite layers with thickness varying between 1 and 20 mm. Progressive deformation of the boudinaged layers resulted in the development of ptygmatic folds with fold axes parallel to the stretching lineation. The grain size from the host rock marbles (10 μm) decreases towards the boudinaged dolomite layer (5 μm) indicating strain localization adjacent to the dolomite layers. Furthermore, strain is localized within micro shear zones which nucleate in the necks of rotated boudin segments. Crystallographic preferred orientations (CPO) derived from electron backscatter diffraction analysis show a distinct variation in CPO between the coarser and finer grained calcite next to the boudinaged dolomite. Detailed microstructural analysis revealed that strain is strongly partitioned parallel to the boudin segments and to the almost oblique inter-boudin surfaces.

Controls of repeating earthquakes' location from a- and b- values imaging

NASA Astrophysics Data System (ADS)

Chen, K. H.; Kawamura, M.

2017-12-01

The locations where creeping and locked fault areas abut have commonly found to be delineated by the foci of small repeating earthquakes (REs). REs not only represent the finer structure of high creep-rate location, they also function as fault slip-rate indicators. Knowledge of the expected location of REs therefore, is crucial for fault deformation monitoring and assessment of earthquake potential. However, a precise description of factors determining REs locations is lacking. To explore where earthquakes tend to recur, we statistically investigated repeating earthquake catalogs and background seismicity from different regions including six fault segments in California and Taiwan. We show that the location of repeating earthquakes can be mapped using the spatial distribution of the seismic a- and b-values obtained from the background seismicity. Molchan's error diagram statistically confirmed that repeating earthquakes occur within areas with high a-values (2.8-3.8) and high b-values (0.9-1.1) on both strike-slip and thrust fault segments. However, no significant association held true for fault segments with more complicated geometry or for wider areas with a complex fault network. The productivity of small earthquakes responsible for high a- and b-values may thus be the most important factor controlling the location of repeating earthquakes. We hypothesize that, given that the deformation conditions within a fault zone are suitable for a planar fault plane, the location of repeating earthquakes can be best described by a-value 3 and b-value 1. This feature of a- and b-values may be useful for foresee the location of REs for measuring creep rate at depth. Further investigation of REs-rich areas may allow testing of this hypothesis.

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

NASA Astrophysics Data System (ADS)

Yin, Xin; Liu, Aiping; Thornburg, Kent L.; Wang, Ruikang K.; Rugonyi, Sandra

2012-09-01

Recent advances in optical coherence tomography (OCT), and the development of image reconstruction algorithms, enabled four-dimensional (4-D) (three-dimensional imaging over time) imaging of the embryonic heart. To further analyze and quantify the dynamics of cardiac beating, segmentation procedures that can extract the shape of the heart and its motion are needed. Most previous studies analyzed cardiac image sequences using manually extracted shapes and measurements. However, this is time consuming and subject to inter-operator variability. Automated or semi-automated analyses of 4-D cardiac OCT images, although very desirable, are also extremely challenging. This work proposes a robust algorithm to semi automatically detect and track cardiac tissue layers from 4-D OCT images of early (tubular) embryonic hearts. Our algorithm uses a two-dimensional (2-D) deformable double-line model (DLM) to detect target cardiac tissues. The detection algorithm uses a maximum-likelihood estimator and was successfully applied to 4-D in vivo OCT images of the heart outflow tract of day three chicken embryos. The extracted shapes captured the dynamics of the chick embryonic heart outflow tract wall, enabling further analysis of cardiac motion.

New approach based on tetrahedral-mesh geometry for accurate 4D Monte Carlo patient-dose calculation

NASA Astrophysics Data System (ADS)

Han, Min Cheol; Yeom, Yeon Soo; Kim, Chan Hyeong; Kim, Seonghoon; Sohn, Jason W.

2015-02-01

In the present study, to achieve accurate 4D Monte Carlo dose calculation in radiation therapy, we devised a new approach that combines (1) modeling of the patient body using tetrahedral-mesh geometry based on the patient’s 4D CT data, (2) continuous movement/deformation of the tetrahedral patient model by interpolation of deformation vector fields acquired through deformable image registration, and (3) direct transportation of radiation particles during the movement and deformation of the tetrahedral patient model. The results of our feasibility study show that it is certainly possible to construct 4D patient models (= phantoms) with sufficient accuracy using the tetrahedral-mesh geometry and to directly transport radiation particles during continuous movement and deformation of the tetrahedral patient model. This new approach not only produces more accurate dose distribution in the patient but also replaces the current practice of using multiple 3D voxel phantoms and combining multiple dose distributions after Monte Carlo simulations. For routine clinical application of our new approach, the use of fast automatic segmentation algorithms is a must. In order to achieve, simultaneously, both dose accuracy and computation speed, the number of tetrahedrons for the lungs should be optimized. Although the current computation speed of our new 4D Monte Carlo simulation approach is slow (i.e. ~40 times slower than that of the conventional dose accumulation approach), this problem is resolvable by developing, in Geant4, a dedicated navigation class optimized for particle transportation in tetrahedral-mesh geometry.

SU-E-J-89: Comparative Analysis of MIM and Velocity’s Image Deformation Algorithm Using Simulated KV-CBCT Images for Quality Assurance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cline, K; Narayanasamy, G; Obediat, M

Purpose: Deformable image registration (DIR) is used routinely in the clinic without a formalized quality assurance (QA) process. Using simulated deformations to digitally deform images in a known way and comparing to DIR algorithm predictions is a powerful technique for DIR QA. This technique must also simulate realistic image noise and artifacts, especially between modalities. This study developed an algorithm to create simulated daily kV cone-beam computed-tomography (CBCT) images from CT images for DIR QA between these modalities. Methods: A Catphan and physical head-and-neck phantom, with known deformations, were used. CT and kV-CBCT images of the Catphan were utilized tomore » characterize the changes in Hounsfield units, noise, and image cupping that occur between these imaging modalities. The algorithm then imprinted these changes onto a CT image of the deformed head-and-neck phantom, thereby creating a simulated-CBCT image. CT and kV-CBCT images of the undeformed and deformed head-and-neck phantom were also acquired. The Velocity and MIM DIR algorithms were applied between the undeformed CT image and each of the deformed CT, CBCT, and simulated-CBCT images to obtain predicted deformations. The error between the known and predicted deformations was used as a metric to evaluate the quality of the simulated-CBCT image. Ideally, the simulated-CBCT image registration would produce the same accuracy as the deformed CBCT image registration. Results: For Velocity, the mean error was 1.4 mm for the CT-CT registration, 1.7 mm for the CT-CBCT registration, and 1.4 mm for the CT-simulated-CBCT registration. These same numbers were 1.5, 4.5, and 5.9 mm, respectively, for MIM. Conclusion: All cases produced similar accuracy for Velocity. MIM produced similar values of accuracy for CT-CT registration, but was not as accurate for CT-CBCT registrations. The MIM simulated-CBCT registration followed this same trend, but overestimated MIM DIR errors relative to the CT-CBCT registration.« less

A new combined surface and volume registration

NASA Astrophysics Data System (ADS)

Lepore, Natasha; Joshi, Anand A.; Leahy, Richard M.; Brun, Caroline; Chou, Yi-Yu; Pennec, Xavier; Lee, Agatha D.; Barysheva, Marina; De Zubicaray, Greig I.; Wright, Margaret J.; McMahon, Katie L.; Toga, Arthur W.; Thompson, Paul M.

2010-03-01

3D registration of brain MRI data is vital for many medical imaging applications. However, purely intensitybased approaches for inter-subject matching of brain structure are generally inaccurate in cortical regions, due to the highly complex network of sulci and gyri, which vary widely across subjects. Here we combine a surfacebased cortical registration with a 3D fluid one for the first time, enabling precise matching of cortical folds, but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms. This greatly improves the matching of anatomy in cortical areas. The cortices are segmented and registered with the software Freesurfer. The deformation field is initially extended to the full 3D brain volume using a 3D harmonic mapping that preserves the matching between cortical surfaces. Finally, these deformation fields are used to initialize a 3D Riemannian fluid registration algorithm, that improves the alignment of subcortical brain regions. We validate this method on an MRI dataset from 92 healthy adult twins. Results are compared to those based on volumetric registration without surface constraints; the resulting mean templates resolve consistent anatomical features both subcortically and at the cortex, suggesting that the approach is well-suited for cross-subject integration of functional and anatomic data.

Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiaofeng; Wu, Ning; Cheng, Guanghui

Purpose: To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). Methods and Materials: The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RTmore » MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Results: Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. Conclusions: We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy.« less

An interactive medical image segmentation framework using iterative refinement.

PubMed

Kalshetti, Pratik; Bundele, Manas; Rahangdale, Parag; Jangra, Dinesh; Chattopadhyay, Chiranjoy; Harit, Gaurav; Elhence, Abhay

2017-04-01

Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Segmentation of stereo terrain images

NASA Astrophysics Data System (ADS)

George, Debra A.; Privitera, Claudio M.; Blackmon, Theodore T.; Zbinden, Eric; Stark, Lawrence W.

2000-06-01

We have studied four approaches to segmentation of images: three automatic ones using image processing algorithms and a fourth approach, human manual segmentation. We were motivated toward helping with an important NASA Mars rover mission task -- replacing laborious manual path planning with automatic navigation of the rover on the Mars terrain. The goal of the automatic segmentations was to identify an obstacle map on the Mars terrain to enable automatic path planning for the rover. The automatic segmentation was first explored with two different segmentation methods: one based on pixel luminance, and the other based on pixel altitude generated through stereo image processing. The third automatic segmentation was achieved by combining these two types of image segmentation. Human manual segmentation of Martian terrain images was used for evaluating the effectiveness of the combined automatic segmentation as well as for determining how different humans segment the same images. Comparisons between two different segmentations, manual or automatic, were measured using a similarity metric, SAB. Based on this metric, the combined automatic segmentation did fairly well in agreeing with the manual segmentation. This was a demonstration of a positive step towards automatically creating the accurate obstacle maps necessary for automatic path planning and rover navigation.

2D/3D fetal cardiac dataset segmentation using a deformable model.

PubMed

Dindoyal, Irving; Lambrou, Tryphon; Deng, Jing; Todd-Pokropek, Andrew

2011-07-01

To segment the fetal heart in order to facilitate the 3D assessment of the cardiac function and structure. Ultrasound acquisition typically results in drop-out artifacts of the chamber walls. The authors outline a level set deformable model to automatically delineate the small fetal cardiac chambers. The level set is penalized from growing into an adjacent cardiac compartment using a novel collision detection term. The region based model allows simultaneous segmentation of all four cardiac chambers from a user defined seed point placed in each chamber. The segmented boundaries are automatically penalized from intersecting at walls with signal dropout. Root mean square errors of the perpendicular distances between the algorithm's delineation and manual tracings are within 2 mm which is less than 10% of the length of a typical fetal heart. The ejection fractions were determined from the 3D datasets. We validate the algorithm using a physical phantom and obtain volumes that are comparable to those from physically determined means. The algorithm segments volumes with an error of within 13% as determined using a physical phantom. Our original work in fetal cardiac segmentation compares automatic and manual tracings to a physical phantom and also measures inter observer variation.

Velocity vector imaging fails to quantify regional myocardial dysfunction in a mouse model of isoprenaline-induced cardiotoxicity.

PubMed

Täng, Margareta Scharin; Redfors, Bjorn; Shao, Yangzhen; Omerovic, Elmir

2012-08-01

Regional myocardial deformation patterns are important in a variety of cardiac diseases, including stress-induced cardiomyopathy. Velocity-vector-based imaging is a speckle-tracking echocardiography (STE)-based algorithm that has been shown to allow in-depth cardiac phenotyping in humans. Regional posterior wall myocardial dysfunction occurs during severe isoprenaline stress in mice. We have previously shown that regional posterior wall end-systolic transmural strain decreases after severe isoprenaline toxicity in mice. We hypothesize that STE can detect and further quantify these perturbations. Twenty-three mice underwent echocardiographic examination using the VEVO2100 system. Regional transmural radial strain and strain rate were calculated in both parasternal short-axis and parasternal long-axis cine loops using the VisualSonics VEVO 2100 velocity vector imaging (VVI) STE algorithm. Eight C57BL/6 mice underwent baseline echocardiographic examination using the VisualSonics VEVO 770 system, which can acquire >1,000 frames/s cine loops. In a parasternal short-axis cine loop, the heart was divided into six segments, and regional fractional wall thickening (FWT) was assessed manually. The same protocols were also performed 90 minutes post 400 mg/kg intraperitoneally isoprenaline. Regional myocardial FWT is uniform at baseline but increases significantly in anterolateral segments, whereas it decreases significantly in posterior segments (P < 0.05). A similar pattern is seen using the VVI algorithm although the variance is larger, and differences are smaller and fail to reach significance. VVI is less sensitive in detecting regional perturbations in myocardial function than manual tracing, possibly due to the low frame rate in the cine loops used. © 2012, Wiley Periodicals, Inc.

A fast alignment method for breast MRI follow-up studies using automated breast segmentation and current-prior registration

NASA Astrophysics Data System (ADS)

Wang, Lei; Strehlow, Jan; Rühaak, Jan; Weiler, Florian; Diez, Yago; Gubern-Merida, Albert; Diekmann, Susanne; Laue, Hendrik; Hahn, Horst K.

2015-03-01

In breast cancer screening for high-risk women, follow-up magnetic resonance images (MRI) are acquired with a time interval ranging from several months up to a few years. Prior MRI studies may provide additional clinical value when examining the current one and thus have the potential to increase sensitivity and specificity of screening. To build a spatial correlation between suspicious findings in both current and prior studies, a reliable alignment method between follow-up studies is desirable. However, long time interval, different scanners and imaging protocols, and varying breast compression can result in a large deformation, which challenges the registration process. In this work, we present a fast and robust spatial alignment framework, which combines automated breast segmentation and current-prior registration techniques in a multi-level fashion. First, fully automatic breast segmentation is applied to extract the breast masks that are used to obtain an initial affine transform. Then, a non-rigid registration algorithm using normalized gradient fields as similarity measure together with curvature regularization is applied. A total of 29 subjects and 58 breast MR images were collected for performance assessment. To evaluate the global registration accuracy, the volume overlap and boundary surface distance metrics are calculated, resulting in an average Dice Similarity Coefficient (DSC) of 0.96 and root mean square distance (RMSD) of 1.64 mm. In addition, to measure local registration accuracy, for each subject a radiologist annotated 10 pairs of markers in the current and prior studies representing corresponding anatomical locations. The average distance error of marker pairs dropped from 67.37 mm to 10.86 mm after applying registration.

Vortex coronagraphs for the Habitable Exoplanet Imaging Mission concept: theoretical performance and telescope requirements

NASA Astrophysics Data System (ADS)

Ruane, Garreth; Mawet, Dimitri; Mennesson, Bertrand; Jewell, Jeffrey; Shaklan, Stuart

2018-01-01

The Habitable Exoplanet Imaging Mission concept requires an optical coronagraph that provides deep starlight suppression over a broad spectral bandwidth, high throughput for point sources at small angular separation, and insensitivity to temporally varying, low-order aberrations. Vortex coronagraphs are a promising solution that performs optimally on off-axis, monolithic telescopes and may also be designed for segmented telescopes with minor losses in performance. We describe the key advantages of vortex coronagraphs on off-axis telescopes such as (1) unwanted diffraction due to aberrations is passively rejected in several low-order Zernike modes relaxing the wavefront stability requirements for imaging Earth-like planets from <10 to >100 pm rms, (2) stars with angular diameters >0.1 λ / D may be sufficiently suppressed, (3) the absolute planet throughput is >10 % , even for unfavorable telescope architectures, and (4) broadband solutions (Δλ / λ > 0.1) are readily available for both monolithic and segmented apertures. The latter make use of grayscale apodizers in an upstream pupil plane to provide suppression of diffracted light from amplitude discontinuities in the telescope pupil without inducing additional stroke on the deformable mirrors. We set wavefront stability requirements on the telescope, based on a stellar irradiance threshold set at an angular separation of 3 ± 0.5λ / D from the star, and discuss how some requirements may be relaxed by trading robustness to aberrations for planet throughput.

Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection.

PubMed

Luo, Ping; Lin, Liang; Liu, Xiaobai

2016-07-01

This paper presents a novel compositional contour-based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations. Our approach contains two key steps: 1) contour feature generation and 2) generative model pursuit. For each category, we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and describe each segment using three different types of distance metrics. These metrics are diverse and complementary with each other to capture various shape deformations. We regard the parameterized contour segment plus an additive residual ϵ as a basic subspace, namely, ϵ -ball, in the sense that it represents local shape variance under the certain distance metric. Using these ϵ -balls as features, we then propose a generative learning algorithm to pursue the compositional shape model, which greedily selects the most representative features under the information projection principle. In experiments, we evaluate our model on several public challenging data sets, and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations, articulations, and background clutter, hence boosting system performance.

Identification of uncommon objects in containers

DOEpatents

Bremer, Peer-Timo; Kim, Hyojin; Thiagarajan, Jayaraman J.

2017-09-12

A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

Tilton, James C.; Lawrence, William T.

2005-01-01

NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

Active geodynamics of the Caucasus/Caspian region educed from GPS, and seismic Observations

NASA Astrophysics Data System (ADS)

Gadirov (Kadirov), Fakhraddin; Floyd, Michael; Reilinger, Robert; Alizadeh, Akif; Guliyev, Ibrahim; Mammadov, Samir; Safarov, Rafig

2017-04-01

The geodynamic and earthquake activity in the Caucasus/Caspian region is due to the ongoing collision of the Arabian plate with Eurasia. The Caucasus and Caspian Sea are historically among the most seismically active regions on earth. These earthquakes have caused thousands of deaths and great economic distress. Future earthquakes in the Caucasus and Caspian Sea must be considered and planned for in order to limit their impact on the people, ecology, and infrastructure of the region. Within this plate tectonics context, we examine deformation of the Caucasus region and show that most crustal shortening in the collision zone is accommodated by the Greater Caucasus Fold-and-Thrust Belt (GCFTB) along the southern edge of the Greater Caucasus Mountains. The eastern GCFTB appears to bifurcate west of Baku, with one branch following the accurate geometry of the Greater Caucasus, turning towards the south and traversing the Neftchala Peninsula. A second branch may extend directly into the Caspian Sea south of Baku, likely connecting to the Central Caspian Seismic Zone. We model deformation in terms of a locked thrust fault that coincides in general with the main surface trace of the GCFTB. We consider two end-member models, each of which tests the likelihood of one or other of the branches being the dominant cause of observed deformation. Our models indicate that strain is actively accumulating on the fault along the 200 km segment of the fault west of Baku (approximately between longitudes 47-49°E). Parts of this segment of the fault broke in major earthquakes historically (1191, 1859, 1902) suggesting that significant future earthquakes (M 6-7) are likely on the central and western segment of the fault. We observe a similar deformation pattern across the eastern end of the GCFTB along a profile crossing the Kura Depression and Greater Caucasus Mountains in the vicinity of Baku. Along this eastern segment, a branch of the fault changes from a NW-SE striking thrust to an N-S oriented strike-slip fault. The similar deformation pattern along the eastern and central GCFTB segments raises the possibility that major earthquakes may also occur in eastern Azerbaijan. However, the eastern segment of the GCFTB has no record of large historic earthquakes, and is characterized by thick, highly saturated and over-pressured sediments within the Kura Depression and adjacent Caspian Basin that may inhibit elastic strain accumulation in favour of fault creep, and/or distributed faulting and folding. Thus, while our analyses suggest that large earthquakes are likely in central and western Azerbaijan, it is still uncertain whether significant earthquakes are also likely along the eastern segment, and on which structure. Ongoing and future focused studies of active deformation promise to shed further light on the tectonics and earthquake hazards in this highly populated and developed part of Azerbaijan.

SU-E-J-100: The Combination of Deformable Image Registration and Regions-Of-Interest Mapping Technique to Accomplish Accurate Dose Calculation On Cone Beam Computed Tomography for Esophageal Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, B-T; Lu, J-Y

Purpose: We introduce a new method combined with the deformable image registration (DIR) and regions-of-interest mapping (ROIM) technique to accurately calculate dose on daily CBCT for esophageal cancer. Methods: Patients suffered from esophageal cancer were enrolled in the study. Prescription was set to 66 Gy/30 F and 54 Gy/30 F to the primary tumor (PTV66) and subclinical disease (PTV54) . Planning CT (pCT) were segmented into 8 substructures in terms of their differences in physical density, such as gross target volume (GTV), venae cava superior (SVC), aorta, heart, spinal cord, lung, muscle and bones. The pCT and its substructures weremore » transferred to the MIM software to readout their mean HU values. Afterwards, a deformable planning CT to daily KV-CBCT image registration method was then utilized to acquire a new structure set on CBCT. The newly generated structures on CBCT were then transferred back to the treatment planning system (TPS) and its HU information were overridden manually with mean HU values obtained from pCT. Finally, the treatment plan was projected onto the CBCT images with the same beam arrangements and monitor units (MUs) to accomplish dose calculation. Planning target volume (PTV) and organs at risk (OARs) from both of the pCT and CBCT were compared to evaluate the dose calculation accuracy. Results: It was found that the dose distribution in the CBCT showed little differences compared to the pCT, regardless of whether PTV or OARs were concerned. Specifically, dose variation in GTV, PTV54, PTV66, SVC, lung and heart were within 0.1%. The maximum dose variation was presented in the spinal cord, which was up to 2.7% dose difference. Conclusion: The proposed method combined with DIR and ROIM technique to accurately calculate dose distribution on CBCT for esophageal cancer is feasible.« less

Initial clinical observations of intra- and interfractional motion variation in MR-guided lung SBRT.

PubMed

Thomas, David H; Santhanam, Anand; Kishan, Amar U; Cao, Minsong; Lamb, James; Min, Yugang; O'Connell, Dylan; Yang, Yingli; Agazaryan, Nzhde; Lee, Percy; Low, Daniel

2018-02-01

To evaluate variations in intra- and interfractional tumour motion, and the effect on internal target volume (ITV) contour accuracy, using deformable image registration of real-time two-dimensional-sagittal cine-mode MRI acquired during lung stereotactic body radiation therapy (SBRT) treatments. Five lung tumour patients underwent free-breathing SBRT treatments on the ViewRay system, with dose prescribed to a planning target volume (defined as a 3-6 mm expansion of the 4DCT-ITV). Sagittal slice cine-MR images (3.5 × 3.5 mm 2 pixels) were acquired through the centre of the tumour at 4 frames per second throughout the treatments (3-4 fractions of 21-32 min). Tumour gross tumour volumes (GTVs) were contoured on the first frame of the MR cine and tracked for the first 20 min of each treatment using offline optical-flow based deformable registration implemented on a GPU cluster. A ground truth ITV (MR-ITV 20 min ) was formed by taking the union of tracked GTV contours. Pseudo-ITVs were generated from unions of the GTV contours tracked over 10 s segments of image data (MR-ITV 10 s ). Differences were observed in the magnitude of median tumour displacement between days of treatments. MR-ITV 10 s areas were as small as 46% of the MR-ITV 20 min . An ITV offers a "snapshot" of breathing motion for the brief period of time the tumour is imaged on a specific day. Real-time MRI over prolonged periods of time and over multiple treatment fractions shows that ITV size varies. Further work is required to investigate the dosimetric effect of these results. Advances in knowledge: Five lung tumour patients underwent free-breathing MRI-guided SBRT treatments, and their tumours tracked using deformable registration of cine-mode MRI. The results indicate that variability of both intra- and interfractional breathing amplitude should be taken into account during planning of lung radiotherapy.

Imaging the North Anatolian Fault using the scattered teleseismic wavefield

NASA Astrophysics Data System (ADS)

Thompson, D. A.; Rost, S.; Houseman, G. A.; Cornwell, D. G.; Turkelli, N.; Teoman, U.; Kahraman, M.; Altuncu Poyraz, S.; Gülen, L.; Utkucu, M.; Frederiksen, A. W.; Rondenay, S.

2013-12-01

The North Anatolian Fault Zone (NAFZ) is a major continental strike-slip fault system, similar in size and scale to the San Andreas system, that extends ˜1200 km across Turkey. In 2012, a new multidisciplinary project (FaultLab) was instigated to better understand deformation throughout the entire crust in the NAFZ, in particular the expected transition from narrow zones of brittle deformation in the upper crust to possibly broader shear zones in the lower crust/upper mantle and how these features contribute to the earthquake loading cycle. This contribution will discuss the first results from the seismic component of the project, a 73 station network encompassing the northern and southern branches of the NAFZ in the Sakarya region. The Dense Array for North Anatolia (DANA) is arranged as a 6×11 grid with a nominal station spacing of 7 km, with a further 7 stations located outside of the main grid. With the excellent resolution afforded by the DANA network, we will present images of crustal structure using the technique of teleseismic scattering tomography. The method uses a full waveform inversion of the teleseismic scattered wavefield coupled with array processing techniques to infer the properties and location of small-scale heterogeneities (with scales on the order of the seismic wavelength) within the crust. We will also present preliminary results of teleseismic scattering migration, another powerful method that benefits from the dense data coverage of the deployed seismic network. Images obtained using these methods together with other conventional imaging techniques will provide evidence for how the deformation is distributed within the fault zone at depth, providing constraints that can be used in conjunction with structural analyses of exhumed fault segments and models of geodetic strain-rate across the fault system. By linking together results from the complementary techniques being employed in the FaultLab project, we aim to produce a comprehensive picture of fault structure and dynamics throughout the crust and shallow upper mantle of this major active fault zone.

Using deep learning in image hyper spectral segmentation, classification, and detection

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Su, Zhenyu

2018-02-01

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

A fully convolutional networks (FCN) based image segmentation algorithm in binocular imaging system

NASA Astrophysics Data System (ADS)

Long, Zourong; Wei, Biao; Feng, Peng; Yu, Pengwei; Liu, Yuanyuan

2018-01-01

This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation. Different from the classical convolutional neural networks (CNN), FCN uses convolution layers instead of the fully connected layers. So it can accept image of arbitrary size. In this paper, we combine the convolutional neural network and scale invariant feature matching to solve the problem of visual positioning under different scenarios. All high-resolution images are captured with our calibrated binocular imaging system and several groups of test data are collected to verify this method. The experimental results show that the binocular images are effectively segmented without over-segmentation. With these segmented images, feature matching via SURF method is implemented to obtain regional information for further image processing. The final positioning procedure shows that the results are acceptable in the range of 1.4 1.6 m, the distance error is less than 10mm.

SU-E-J-104: Evaluation of Accuracy for Various Deformable Image Registrations with Virtual Deformation QA Software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, S; Kim, K; Kim, M

Purpose: The accuracy of deformable image registration (DIR) has a significant dosimetric impact in radiation treatment planning. We evaluated accuracy of various DIR algorithms using virtual deformation QA software (ImSimQA, Oncology System Limited, UK). Methods: The reference image (Iref) and volume (Vref) was first generated with IMSIMQA software. We deformed Iref with axial movement of deformation point and Vref depending on the type of deformation that are the deformation1 is to increase the Vref (relaxation) and the deformation 2 is to decrease the Vref (contraction) .The deformed image (Idef) and volume (Vdef) were inversely deformed to Iref and Vref usingmore » DIR algorithms. As a Result, we acquired deformed image (Iid) and volume (Vid). The DIR algorithms were optical flow (HS, IOF) and demons (MD, FD) of the DIRART. The image similarity evaluation between Iref and Iid was calculated by Normalized Mutual Information (NMI) and Normalized Cross Correlation (NCC). The value of Dice Similarity Coefficient (DSC) was used for evaluation of volume similarity. Results: When moving distance of deformation point was 4 mm, the value of NMI was above 1.81 and NCC was above 0.99 in all DIR algorithms. Since the degree of deformation was increased, the degree of image similarity was decreased. When the Vref increased or decreased about 12%, the difference between Vref and Vid was within ±5% regardless of the type of deformation. The value of DSC was above 0.95 in deformation1 except for the MD algorithm. In case of deformation 2, that of DSC was above 0.95 in all DIR algorithms. Conclusion: The Idef and Vdef have not been completely restored to Iref and Vref and the accuracy of DIR algorithms was different depending on the degree of deformation. Hence, the performance of DIR algorithms should be verified for the desired applications.« less

Metric Learning to Enhance Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

Thompson, David R.; Castano, Rebecca; Bue, Brian; Gilmore, Martha S.

2013-01-01

Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.

Image Segmentation Using Minimum Spanning Tree

NASA Astrophysics Data System (ADS)

Dewi, M. P.; Armiati, A.; Alvini, S.

2018-04-01

This research aim to segmented the digital image. The process of segmentation is to separate the object from the background. So the main object can be processed for the other purposes. Along with the development of technology in digital image processing application, the segmentation process becomes increasingly necessary. The segmented image which is the result of the segmentation process should accurate due to the next process need the interpretation of the information on the image. This article discussed the application of minimum spanning tree on graph in segmentation process of digital image. This method is able to separate an object from the background and the image will change to be the binary images. In this case, the object that being the focus is set in white, while the background is black or otherwise.

Segmentation of megathrust rupture zones from fore-arc deformation patterns over hundreds to millions of years, Arauco peninsula, Chile

NASA Astrophysics Data System (ADS)

Melnick, Daniel; Bookhagen, Bodo; Strecker, Manfred R.; Echtler, Helmut P.

2009-01-01

This work explores the control of fore-arc structure on segmentation of megathrust earthquake ruptures using coastal geomorphic markers. The Arauco-Nahuelbuta region at the south-central Chile margin constitutes an anomalous fore-arc sector in terms of topography, geology, and exhumation, located within the overlap between the Concepción and Valdivia megathrust segments. This boundary, however, is only based on ˜500 years of historical records. We integrate deformed marine terraces dated by cosmogenic nuclides, syntectonic sediments, published fission track data, seismic reflection profiles, and microseismicity to analyze this earthquake boundary over 102-106 years. Rapid exhumation of Nahuelbuta's dome-like core started at 4 ± 1.2 Ma, coeval with inversion of the adjacent Arauco basin resulting in emergence of the Arauco peninsula. Here, similarities between topography, spatiotemporal trends in fission track ages, Pliocene-Pleistocene growth strata, and folded marine terraces suggest that margin-parallel shortening has dominated since Pliocene time. This shortening likely results from translation of a fore-arc sliver or microplate, decoupled from South America by an intra-arc strike-slip fault. Microplate collision against a buttress leads to localized uplift at Arauco accrued by deep-seated reverse faults, as well as incipient oroclinal bending. The extent of the Valdivia segment, which ruptured last in 1960 with an Mw 9.5 event, equals the inferred microplate. We propose that mechanical homogeneity of the fore-arc microplate delimits the Valdivia segment and that a marked discontinuity in the continental basement at Arauco acts as an inhomogeneous barrier controlling nucleation and propagation of 1960-type ruptures. As microplate-related deformation occurs since the Pliocene, we propose that this earthquake boundary and the extent of the Valdivia segment are spatially stable seismotectonic features at million year scale.

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: A comparison of CT and CT-MRI based tissue segmentation on simulated temperature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verhaart, René F., E-mail: r.f.verhaart@erasmusmc.nl; Paulides, Margarethus M.; Fortunati, Valerio

Purpose: In current clinical practice, head and neck (H and N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors’ study is to investigate the relevance of using MRI in addition to CT for patient modeling in H and N HTP. Methods: CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreousmore » humor, and the optical nerve. For these tissues that are used for patient modeling in H and N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRI{sub db}). To quantify the relevance of MRI based segmentation for H and N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (T{sub max}) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRI{sub db}. Results: In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (T{sub max}: 38.0 °C) and CT and MRI (T{sub max}: 38.1 °C) result in similar simulated temperatures, while CT and MRI{sub db} (T{sub max}: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly. Conclusions: Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.« less