Sample records for volume segmentation analysis

  1. Economic Analysis. Volume V. Course Segments 65-79.

    ERIC Educational Resources Information Center

    Sterling Inst., Washington, DC. Educational Technology Center.

    The fifth volume of the multimedia, individualized course in economic analysis produced for the United States Naval Academy covers segments 65-79 of the course. Included in the volume are discussions of monopoly markets, monopolistic competition, oligopoly markets, and the theory of factor demand and supply. Other segments of the course, the…

  2. Automated segmentation and dose-volume analysis with DICOMautomaton

    NASA Astrophysics Data System (ADS)

    Clark, H.; Thomas, S.; Moiseenko, V.; Lee, R.; Gill, B.; Duzenli, C.; Wu, J.

    2014-03-01

    Purpose: Exploration of historical data for regional organ dose sensitivity is limited by the effort needed to (sub-)segment large numbers of contours. A system has been developed which can rapidly perform autonomous contour sub-segmentation and generic dose-volume computations, substantially reducing the effort required for exploratory analyses. Methods: A contour-centric approach is taken which enables lossless, reversible segmentation and dramatically reduces computation time compared with voxel-centric approaches. Segmentation can be specified on a per-contour, per-organ, or per-patient basis, and can be performed along either an embedded plane or in terms of the contour's bounds (e.g., split organ into fractional-volume/dose pieces along any 3D unit vector). More complex segmentation techniques are available. Anonymized data from 60 head-and-neck cancer patients were used to compare dose-volume computations with Varian's EclipseTM (Varian Medical Systems, Inc.). Results: Mean doses and Dose-volume-histograms computed agree strongly with Varian's EclipseTM. Contours which have been segmented can be injected back into patient data permanently and in a Digital Imaging and Communication in Medicine (DICOM)-conforming manner. Lossless segmentation persists across such injection, and remains fully reversible. Conclusions: DICOMautomaton allows researchers to rapidly, accurately, and autonomously segment large amounts of data into intricate structures suitable for analyses of regional organ dose sensitivity.

  3. Volume Segmentation and Ghost Particles

    NASA Astrophysics Data System (ADS)

    Ziskin, Isaac; Adrian, Ronald

    2011-11-01

    Volume Segmentation Tomographic PIV (VS-TPIV) is a type of tomographic PIV in which images of particles in a relatively thick volume are segmented into images on a set of much thinner volumes that may be approximated as planes, as in 2D planar PIV. The planes of images can be analysed by standard mono-PIV, and the volume of flow vectors can be recreated by assembling the planes of vectors. The interrogation process is similar to a Holographic PIV analysis, except that the planes of image data are extracted from two-dimensional camera images of the volume of particles instead of three-dimensional holographic images. Like the tomographic PIV method using the MART algorithm, Volume Segmentation requires at least two cameras and works best with three or four. Unlike the MART method, Volume Segmentation does not require reconstruction of individual particle images one pixel at a time and it does not require an iterative process, so it operates much faster. As in all tomographic reconstruction strategies, ambiguities known as ghost particles are produced in the segmentation process. The effect of these ghost particles on the PIV measurement is discussed. This research was supported by Contract 79419-001-09, Los Alamos National Laboratory.

  4. Direct volume estimation without segmentation

    NASA Astrophysics Data System (ADS)

    Zhen, X.; Wang, Z.; Islam, A.; Bhaduri, M.; Chan, I.; Li, S.

    2015-03-01

    Volume estimation plays an important role in clinical diagnosis. For example, cardiac ventricular volumes including left ventricle (LV) and right ventricle (RV) are important clinical indicators of cardiac functions. Accurate and automatic estimation of the ventricular volumes is essential to the assessment of cardiac functions and diagnosis of heart diseases. Conventional methods are dependent on an intermediate segmentation step which is obtained either manually or automatically. However, manual segmentation is extremely time-consuming, subjective and highly non-reproducible; automatic segmentation is still challenging, computationally expensive, and completely unsolved for the RV. Towards accurate and efficient direct volume estimation, our group has been researching on learning based methods without segmentation by leveraging state-of-the-art machine learning techniques. Our direct estimation methods remove the accessional step of segmentation and can naturally deal with various volume estimation tasks. Moreover, they are extremely flexible to be used for volume estimation of either joint bi-ventricles (LV and RV) or individual LV/RV. We comparatively study the performance of direct methods on cardiac ventricular volume estimation by comparing with segmentation based methods. Experimental results show that direct estimation methods provide more accurate estimation of cardiac ventricular volumes than segmentation based methods. This indicates that direct estimation methods not only provide a convenient and mature clinical tool for cardiac volume estimation but also enables diagnosis of cardiac diseases to be conducted in a more efficient and reliable way.

  5. Automatic liver volume segmentation and fibrosis classification

    NASA Astrophysics Data System (ADS)

    Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

    2018-02-01

    In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

  6. Bioimpedance Measurement of Segmental Fluid Volumes and Hemodynamics

    NASA Technical Reports Server (NTRS)

    Montgomery, Leslie D.; Wu, Yi-Chang; Ku, Yu-Tsuan E.; Gerth, Wayne A.; DeVincenzi, D. (Technical Monitor)

    2000-01-01

    Bioimpedance has become a useful tool to measure changes in body fluid compartment volumes. An Electrical Impedance Spectroscopic (EIS) system is described that extends the capabilities of conventional fixed frequency impedance plethysmographic (IPG) methods to allow examination of the redistribution of fluids between the intracellular and extracellular compartments of body segments. The combination of EIS and IPG techniques was evaluated in the human calf, thigh, and torso segments of eight healthy men during 90 minutes of six degree head-down tilt (HDT). After 90 minutes HDT the calf and thigh segments significantly (P < 0.05) lost conductive volume (eight and four percent, respectively) while the torso significantly (P < 0.05) gained volume (approximately three percent). Hemodynamic responses calculated from pulsatile IPG data also showed a segmental pattern consistent with vascular fluid loss from the lower extremities and vascular engorgement in the torso. Lumped-parameter equivalent circuit analyses of EIS data for the calf and thigh indicated that the overall volume decreases in these segments arose from reduced extracellular volume that was not completely balanced by increased intracellular volume. The combined use of IPG and EIS techniques enables noninvasive tracking of multi-segment volumetric and hemodynamic responses to environmental and physiological stresses.

  7. Segment-specific resistivity improves body fluid volume estimates from bioimpedance spectroscopy in hemodialysis patients.

    PubMed

    Zhu, F; Kuhlmann, M K; Kaysen, G A; Sarkar, S; Kaitwatcharachai, C; Khilnani, R; Stevens, L; Leonard, E F; Wang, J; Heymsfield, S; Levin, N W

    2006-02-01

    Discrepancies in body fluid estimates between segmental bioimpedance spectroscopy (SBIS) and gold-standard methods may be due to the use of a uniform value of tissue resistivity to compute extracellular fluid volume (ECV) and intracellular fluid volume (ICV). Discrepancies may also arise from the exclusion of fluid volumes of hands, feet, neck, and head from measurements due to electrode positions. The aim of this study was to define the specific resistivity of various body segments and to use those values for computation of ECV and ICV along with a correction for unmeasured fluid volumes. Twenty-nine maintenance hemodialysis patients (16 men) underwent body composition analysis including whole body MRI, whole body potassium (40K) content, deuterium, and sodium bromide dilution, and segmental and wrist-to-ankle bioimpedance spectroscopy, all performed on the same day before a hemodialysis. Segment-specific resistivity was determined from segmental fat-free mass (FFM; by MRI), hydration status of FFM (by deuterium and sodium bromide), tissue resistance (by SBIS), and segment length. Segmental FFM was higher and extracellular hydration of FFM was lower in men compared with women. Segment-specific resistivity values for arm, trunk, and leg all differed from the uniform resistivity used in traditional SBIS algorithms. Estimates for whole body ECV, ICV, and total body water from SBIS using segmental instead of uniform resistivity values and after adjustment for unmeasured fluid volumes of the body did not differ significantly from gold-standard measures. The uniform tissue resistivity values used in traditional SBIS algorithms result in underestimation of ECV, ICV, and total body water. Use of segmental resistivity values combined with adjustment for body volumes that are neglected by traditional SBIS technique significantly improves estimations of body fluid volume in hemodialysis patients.

  8. Tooth segmentation system with intelligent editing for cephalometric analysis

    NASA Astrophysics Data System (ADS)

    Chen, Shoupu

    2015-03-01

    Cephalometric analysis is the study of the dental and skeletal relationship in the head, and it is used as an assessment and planning tool for improved orthodontic treatment of a patient. Conventional cephalometric analysis identifies bony and soft-tissue landmarks in 2D cephalometric radiographs, in order to diagnose facial features and abnormalities prior to treatment, or to evaluate the progress of treatment. Recent studies in orthodontics indicate that there are persistent inaccuracies and inconsistencies in the results provided using conventional 2D cephalometric analysis. Obviously, plane geometry is inappropriate for analyzing anatomical volumes and their growth; only a 3D analysis is able to analyze the three-dimensional, anatomical maxillofacial complex, which requires computing inertia systems for individual or groups of digitally segmented teeth from an image volume of a patient's head. For the study of 3D cephalometric analysis, the current paper proposes a system for semi-automatically segmenting teeth from a cone beam computed tomography (CBCT) volume with two distinct features, including an intelligent user-input interface for automatic background seed generation, and a graphics processing unit (GPU) acceleration mechanism for three-dimensional GrowCut volume segmentation. Results show a satisfying average DICE score of 0.92, with the use of the proposed tooth segmentation system, by 15 novice users who segmented a randomly sampled tooth set. The average GrowCut processing time is around one second per tooth, excluding user interaction time.

  9. [Definition of nodal volumes in breast cancer treatment and segmentation guidelines].

    PubMed

    Kirova, Y M; Castro Pena, P; Dendale, R; Campana, F; Bollet, M A; Fournier-Bidoz, N; Fourquet, A

    2009-06-01

    To assist in the determination of breast and nodal volumes in the setting of radiotherapy for breast cancer and establish segmentation guidelines. Materials and methods. Contrast metarial enhanced CT examinations were obtained in the treatment position in 25 patients to clearly define the target volumes. The clinical target volume (CTV) including the breast, internal mammary nodes, supraclavicular and subclavicular regions and axxilary region were segmented along with the brachial plexus and interpectoral nodes. The following critical organs were also segmented: heart, lungs, contralateral breast, thyroid, esophagus and humeral head. A correlation between clinical and imaging findings and meeting between radiation oncologists and breast specialists resulted in a better definition of irradiation volumes for breast and nodes with establishement of segmentation guidelines and creation of an anatomical atlas. A practical approach, based on anatomical criteria, is proposed to assist in the segmentation of breast and node volumes in the setting of breast cancer treatment along with a definition of irradiation volumes.

  10. The error analysis of Lobular and segmental division of right liver by volume measurement.

    PubMed

    Zhang, Jianfei; Lin, Weigang; Chi, Yanyan; Zheng, Nan; Xu, Qiang; Zhang, Guowei; Yu, Shengbo; Li, Chan; Wang, Bin; Sui, Hongjin

    2017-07-01

    The aim of this study is to explore the inconsistencies between right liver volume as measured by imaging and the actual anatomical appearance of the right lobe. Five healthy donated livers were studied. The liver slices were obtained with hepatic segments multicolor-infused through the portal vein. In the slices, the lobes were divided by two methods: radiological landmarks and real anatomical boundaries. The areas of the right anterior lobe (RAL) and right posterior lobe (RPL) on each slice were measured using Photoshop CS5 and AutoCAD, and the volumes of the two lobes were calculated. There was no statistically significant difference between the volumes of the RAL or RPL as measured by the radiological landmarks (RL) and anatomical boundaries (AB) methods. However, the curves of the square error value of the RAL and RPL measured using CT showed that the three lowest points were at the cranial, intermediate, and caudal levels. The U- or V-shaped curves of the square error rate of the RAL and RPL revealed that the lowest value is at the intermediate level and the highest at the cranial and caudal levels. On CT images, less accurate landmarks were used to divide the RAL and RPL at the cranial and caudal layers. The measured volumes of hepatic segments VIII and VI would be less than their true values, and the measured volumes of hepatic segments VII and V would be greater than their true values, according to radiological landmarks. Clin. Anat. 30:585-590, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  11. 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.

  12. 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.

  13. Improving Spleen Volume Estimation via Computer Assisted Segmentation on Clinically Acquired CT Scans

    PubMed Central

    Xu, Zhoubing; Gertz, Adam L.; Burke, Ryan P.; Bansal, Neil; Kang, Hakmook; Landman, Bennett A.; Abramson, Richard G.

    2016-01-01

    OBJECTIVES Multi-atlas fusion is a promising approach for computer-assisted segmentation of anatomical structures. The purpose of this study was to evaluate the accuracy and time efficiency of multi-atlas segmentation for estimating spleen volumes on clinically-acquired CT scans. MATERIALS AND METHODS Under IRB approval, we obtained 294 deidentified (HIPAA-compliant) abdominal CT scans on 78 subjects from a recent clinical trial. We compared five pipelines for obtaining splenic volumes: Pipeline 1–manual segmentation of all scans, Pipeline 2–automated segmentation of all scans, Pipeline 3–automated segmentation of all scans with manual segmentation for outliers on a rudimentary visual quality check, Pipelines 4 and 5–volumes derived from a unidimensional measurement of craniocaudal spleen length and three-dimensional splenic index measurements, respectively. Using Pipeline 1 results as ground truth, the accuracy of Pipelines 2–5 (Dice similarity coefficient [DSC], Pearson correlation, R-squared, and percent and absolute deviation of volume from ground truth) were compared for point estimates of splenic volume and for change in splenic volume over time. Time cost was also compared for Pipelines 1–5. RESULTS Pipeline 3 was dominant in terms of both accuracy and time cost. With a Pearson correlation coefficient of 0.99, average absolute volume deviation 23.7 cm3, and 1 minute per scan, Pipeline 3 yielded the best results. The second-best approach was Pipeline 5, with a Pearson correlation coefficient 0.98, absolute deviation 46.92 cm3, and 1 minute 30 seconds per scan. Manual segmentation (Pipeline 1) required 11 minutes per scan. CONCLUSION A computer-automated segmentation approach with manual correction of outliers generated accurate splenic volumes with reasonable time efficiency. PMID:27519156

  14. Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

    PubMed

    Looney, Pádraig; Stevenson, Gordon N; Nicolaides, Kypros H; Plasencia, Walter; Molloholli, Malid; Natsis, Stavros; Collins, Sally L

    2018-06-07

    We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.

  15. Bidirectional segmentation of prostate capsule from ultrasound volumes: an improved strategy

    NASA Astrophysics Data System (ADS)

    Wei, Liyang; Narayanan, Ramkrishnan; Kumar, Dinesh; Fenster, Aaron; Barqawi, Albaha; Werahera, Priya; Crawford, E. David; Suri, Jasjit S.

    2008-03-01

    Prostate volume is an indirect indicator for several prostate diseases. Volume estimation is a desired requirement during prostate biopsy, therapy and clinical follow up. Image segmentation is thus necessary. Previously, discrete dynamic contour (DDC) was implemented in orthogonal unidirectional on the slice-by-slice basis for prostate boundary estimation. This suffered from the glitch that it needed stopping criteria during the propagation of segmentation procedure from slice-to-slice. To overcome this glitch, axial DDC was implemented and this suffered from the fact that central axis never remains fixed and wobbles during propagation of segmentation from slice-to-slice. The effect of this was a multi-fold reconstructed surface. This paper presents a bidirectional DDC approach, thereby removing the two glitches. Our bidirectional DDC protocol was tested on a clinical dataset on 28 3-D ultrasound image volumes acquired using side fire Philips transrectal ultrasound. We demonstrate the orthogonal bidirectional DDC strategy achieved the most accurate volume estimation compared with previously published orthogonal unidirectional DDC and axial DDC methods. Compared to the ground truth, we show that the mean volume estimation errors were: 18.48%, 9.21% and 7.82% for unidirectional, axial and bidirectional DDC methods, respectively. The segmentation architecture is implemented in Visual C++ in Windows environment.

  16. Knowledge-based segmentation of pediatric kidneys in CT for measuring parenchymal volume

    NASA Astrophysics Data System (ADS)

    Brown, Matthew S.; Feng, Waldo C.; Hall, Theodore R.; McNitt-Gray, Michael F.; Churchill, Bernard M.

    2000-06-01

    The purpose of this work was to develop an automated method for segmenting pediatric kidneys in contrast-enhanced helical CT images and measuring the volume of the renal parenchyma. An automated system was developed to segment the abdomen, spine, aorta and kidneys. The expected size, shape, topology an X-ray attenuation of anatomical structures are stored as features in an anatomical model. These features guide 3-D threshold-based segmentation and then matching of extracted image regions to anatomical structures in the model. Following segmentation, the kidney volumes are calculated by summing included voxels. To validate the system, the kidney volumes of 4 swine were calculated using our approach and compared to the 'true' volumes measured after harvesting the kidneys. Automated volume calculations were also performed retrospectively in a cohort of 10 children. The mean difference between the calculated and measured values in the swine kidneys was 1.38 (S.D. plus or minus 0.44) cc. For the pediatric cases, calculated volumes ranged from 41.7 - 252.1 cc/kidney, and the mean ratio of right to left kidney volume was 0.96 (S.D. plus or minus 0.07). These results demonstrate the accuracy of the volumetric technique that may in the future provide an objective assessment of renal damage.

  17. Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images

    NASA Astrophysics Data System (ADS)

    Johnston, Brian; Atkins, M. Stella; Booth, Kellogg S.

    1994-05-01

    An important first step in diagnosis and treatment planning using tomographic imaging is differentiating and quantifying diseased as well as healthy tissue. One of the difficulties encountered in solving this problem to date has been distinguishing the partial volume constituents of each voxel in the image volume. Most proposed solutions to this problem involve analysis of planar images, in sequence, in two dimensions only. We have extended a model-based method of image segmentation which applies the technique of iterated conditional modes in three dimensions. A minimum of user intervention is required to train the algorithm. Partial volume estimates for each voxel in the image are obtained yielding fractional compositions of multiple tissue types for individual voxels. A multispectral approach is applied, where spatially registered data sets are available. The algorithm is simple and has been parallelized using a dataflow programming environment to reduce the computational burden. The algorithm has been used to segment dual echo MRI data sets of multiple sclerosis patients using lesions, gray matter, white matter, and cerebrospinal fluid as the partial volume constituents. The results of the application of the algorithm to these datasets is presented and compared to the manual lesion segmentation of the same data.

  18. Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET.

    PubMed

    Hatt, M; Lamare, F; Boussion, N; Turzo, A; Collet, C; Salzenstein, F; Roux, C; Jarritt, P; Carson, K; Cheze-Le Rest, C; Visvikis, D

    2007-06-21

    Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both

  19. Automatic segmentation of tumor-laden lung volumes from the LIDC database

    NASA Astrophysics Data System (ADS)

    O'Dell, Walter G.

    2012-03-01

    The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

  20. Statistical representative elementary volumes of porous media determined using greyscale analysis of 3D tomograms

    NASA Astrophysics Data System (ADS)

    Bruns, S.; Stipp, S. L. S.; Sørensen, H. O.

    2017-09-01

    Digital rock physics carries the dogmatic concept of having to segment volume images for quantitative analysis but segmentation rejects huge amounts of signal information. Information that is essential for the analysis of difficult and marginally resolved samples, such as materials with very small features, is lost during segmentation. In X-ray nanotomography reconstructions of Hod chalk we observed partial volume voxels with an abundance that limits segmentation based analysis. Therefore, we investigated the suitability of greyscale analysis for establishing statistical representative elementary volumes (sREV) for the important petrophysical parameters of this type of chalk, namely porosity, specific surface area and diffusive tortuosity, by using volume images without segmenting the datasets. Instead, grey level intensities were transformed to a voxel level porosity estimate using a Gaussian mixture model. A simple model assumption was made that allowed formulating a two point correlation function for surface area estimates using Bayes' theory. The same assumption enables random walk simulations in the presence of severe partial volume effects. The established sREVs illustrate that in compacted chalk, these simulations cannot be performed in binary representations without increasing the resolution of the imaging system to a point where the spatial restrictions of the represented sample volume render the precision of the measurement unacceptable. We illustrate this by analyzing the origins of variance in the quantitative analysis of volume images, i.e. resolution dependence and intersample and intrasample variance. Although we cannot make any claims on the accuracy of the approach, eliminating the segmentation step from the analysis enables comparative studies with higher precision and repeatability.

  1. 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.

  2. Superpixel guided active contour segmentation of retinal layers in OCT volumes

    NASA Astrophysics Data System (ADS)

    Bai, Fangliang; Gibson, Stuart J.; Marques, Manuel J.; Podoleanu, Adrian

    2018-03-01

    Retinal OCT image segmentation is a precursor to subsequent medical diagnosis by a clinician or machine learning algorithm. In the last decade, many algorithms have been proposed to detect retinal layer boundaries and simplify the image representation. Inspired by the recent success of superpixel methods for pre-processing natural images, we present a novel framework for segmentation of retinal layers in OCT volume data. In our framework, the region of interest (e.g. the fovea) is located using an adaptive-curve method. The cell layer boundaries are then robustly detected firstly using 1D superpixels, applied to A-scans, and then fitting active contours in B-scan images. Thereafter the 3D cell layer surfaces are efficiently segmented from the volume data. The framework was tested on healthy eye data and we show that it is capable of segmenting up to 12 layers. The experimental results imply the effectiveness of proposed method and indicate its robustness to low image resolution and intrinsic speckle noise.

  3. FluoRender: joint freehand segmentation and visualization for many-channel fluorescence data analysis.

    PubMed

    Wan, Yong; Otsuna, Hideo; Holman, Holly A; Bagley, Brig; Ito, Masayoshi; Lewis, A Kelsey; Colasanto, Mary; Kardon, Gabrielle; Ito, Kei; Hansen, Charles

    2017-05-26

    Image segmentation and registration techniques have enabled biologists to place large amounts of volume data from fluorescence microscopy, morphed three-dimensionally, onto a common spatial frame. Existing tools built on volume visualization pipelines for single channel or red-green-blue (RGB) channels have become inadequate for the new challenges of fluorescence microscopy. For a three-dimensional atlas of the insect nervous system, hundreds of volume channels are rendered simultaneously, whereas fluorescence intensity values from each channel need to be preserved for versatile adjustment and analysis. Although several existing tools have incorporated support of multichannel data using various strategies, the lack of a flexible design has made true many-channel visualization and analysis unavailable. The most common practice for many-channel volume data presentation is still converting and rendering pseudosurfaces, which are inaccurate for both qualitative and quantitative evaluations. Here, we present an alternative design strategy that accommodates the visualization and analysis of about 100 volume channels, each of which can be interactively adjusted, selected, and segmented using freehand tools. Our multichannel visualization includes a multilevel streaming pipeline plus a triple-buffer compositing technique. Our method also preserves original fluorescence intensity values on graphics hardware, a crucial feature that allows graphics-processing-unit (GPU)-based processing for interactive data analysis, such as freehand segmentation. We have implemented the design strategies as a thorough restructuring of our original tool, FluoRender. The redesign of FluoRender not only maintains the existing multichannel capabilities for a greatly extended number of volume channels, but also enables new analysis functions for many-channel data from emerging biomedical-imaging techniques.

  4. Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease.

    PubMed

    Farooq, Zerwa; Behzadi, Ashkan Heshmatzadeh; Blumenfeld, Jon D; Zhao, Yize; Prince, Martin R

    To compare MRI segmentation methods for measuring liver cyst volumes in autosomal dominant polycystic kidney disease (ADPKD). Liver cyst volumes in 42 ADPKD patients were measured using region growing, thresholding and cyst diameter techniques. Manual segmentation was the reference standard. Root mean square deviation was 113, 155, and 500 for cyst diameter, thresholding and region growing respectively. Thresholding error for cyst volumes below 500ml was 550% vs 17% for cyst volumes above 500ml (p<0.001). For measuring volume of a small number of cysts, cyst diameter and manual segmentation methods are recommended. For severe disease with numerous, large hepatic cysts, thresholding is an acceptable alternative. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images

    NASA Astrophysics Data System (ADS)

    Deng, Xiang; Huang, Haibin; Zhu, Lei; Du, Guangwei; Xu, Xiaodong; Sun, Yiyong; Xu, Chenyang; Jolly, Marie-Pierre; Chen, Jiuhong; Xiao, Jie; Merges, Reto; Suehling, Michael; Rinck, Daniel; Song, Lan; Jin, Zhengyu; Jiang, Zhaoxia; Wu, Bin; Wang, Xiaohong; Zhang, Shuai; Peng, Weijun

    2008-03-01

    Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation. In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200 tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can provide complementary information regarding the quality of the segmentation results. The reproducibility was measured by the variation of the volume measurements from 10 independent segmentations. The effect of disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale evaluation of segmentation techniques for other clinical applications.

  6. Automatic quantitative computed tomography segmentation and analysis of aerated lung volumes in acute respiratory distress syndrome-A comparative diagnostic study.

    PubMed

    Klapsing, Philipp; Herrmann, Peter; Quintel, Michael; Moerer, Onnen

    2017-12-01

    Quantitative lung computed tomographic (CT) analysis yields objective data regarding lung aeration but is currently not used in clinical routine primarily because of the labor-intensive process of manual CT segmentation. Automatic lung segmentation could help to shorten processing times significantly. In this study, we assessed bias and precision of lung CT analysis using automatic segmentation compared with manual segmentation. In this monocentric clinical study, 10 mechanically ventilated patients with mild to moderate acute respiratory distress syndrome were included who had received lung CT scans at 5- and 45-mbar airway pressure during a prior study. Lung segmentations were performed both automatically using a computerized algorithm and manually. Automatic segmentation yielded similar lung volumes compared with manual segmentation with clinically minor differences both at 5 and 45 mbar. At 5 mbar, results were as follows: overdistended lung 49.58mL (manual, SD 77.37mL) and 50.41mL (automatic, SD 77.3mL), P=.028; normally aerated lung 2142.17mL (manual, SD 1131.48mL) and 2156.68mL (automatic, SD 1134.53mL), P = .1038; and poorly aerated lung 631.68mL (manual, SD 196.76mL) and 646.32mL (automatic, SD 169.63mL), P = .3794. At 45 mbar, values were as follows: overdistended lung 612.85mL (manual, SD 449.55mL) and 615.49mL (automatic, SD 451.03mL), P=.078; normally aerated lung 3890.12mL (manual, SD 1134.14mL) and 3907.65mL (automatic, SD 1133.62mL), P = .027; and poorly aerated lung 413.35mL (manual, SD 57.66mL) and 469.58mL (automatic, SD 70.14mL), P=.007. Bland-Altman analyses revealed the following mean biases and limits of agreement at 5 mbar for automatic vs manual segmentation: overdistended lung +0.848mL (±2.062mL), normally aerated +14.51mL (±49.71mL), and poorly aerated +14.64mL (±98.16mL). At 45 mbar, results were as follows: overdistended +2.639mL (±8.231mL), normally aerated 17.53mL (±41.41mL), and poorly aerated 56.23mL (±100.67mL). Automatic

  7. [Target volume segmentation of PET images by an iterative method based on threshold value].

    PubMed

    Castro, P; Huerga, C; Glaría, L A; Plaza, R; Rodado, S; Marín, M D; Mañas, A; Serrada, A; Núñez, L

    2014-01-01

    An automatic segmentation method is presented for PET images based on an iterative approximation by threshold value that includes the influence of both lesion size and background present during the acquisition. Optimal threshold values that represent a correct segmentation of volumes were determined based on a PET phantom study that contained different sizes spheres and different known radiation environments. These optimal values were normalized to background and adjusted by regression techniques to a two-variable function: lesion volume and signal-to-background ratio (SBR). This adjustment function was used to build an iterative segmentation method and then, based in this mention, a procedure of automatic delineation was proposed. This procedure was validated on phantom images and its viability was confirmed by retrospectively applying it on two oncology patients. The resulting adjustment function obtained had a linear dependence with the SBR and was inversely proportional and negative with the volume. During the validation of the proposed method, it was found that the volume deviations respect to its real value and CT volume were below 10% and 9%, respectively, except for lesions with a volume below 0.6 ml. The automatic segmentation method proposed can be applied in clinical practice to tumor radiotherapy treatment planning in a simple and reliable way with a precision close to the resolution of PET images. Copyright © 2013 Elsevier España, S.L.U. and SEMNIM. All rights reserved.

  8. Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches.

    PubMed

    Le Troter, Arnaud; Fouré, Alexandre; Guye, Maxime; Confort-Gouny, Sylviane; Mattei, Jean-Pierre; Gondin, Julien; Salort-Campana, Emmanuelle; Bendahan, David

    2016-04-01

    Atlas-based segmentation is a powerful method for automatic structural segmentation of several sub-structures in many organs. However, such an approach has been very scarcely used in the context of muscle segmentation, and so far no study has assessed such a method for the automatic delineation of individual muscles of the quadriceps femoris (QF). In the present study, we have evaluated a fully automated multi-atlas method and a semi-automated single-atlas method for the segmentation and volume quantification of the four muscles of the QF and for the QF as a whole. The study was conducted in 32 young healthy males, using high-resolution magnetic resonance images (MRI) of the thigh. The multi-atlas-based segmentation method was conducted in 25 subjects. Different non-linear registration approaches based on free-form deformable (FFD) and symmetric diffeomorphic normalization algorithms (SyN) were assessed. Optimal parameters of two fusion methods, i.e., STAPLE and STEPS, were determined on the basis of the highest Dice similarity index (DSI) considering manual segmentation (MSeg) as the ground truth. Validation and reproducibility of this pipeline were determined using another MRI dataset recorded in seven healthy male subjects on the basis of additional metrics such as the muscle volume similarity values, intraclass coefficient, and coefficient of variation. Both non-linear registration methods (FFD and SyN) were also evaluated as part of a single-atlas strategy in order to assess longitudinal muscle volume measurements. The multi- and the single-atlas approaches were compared for the segmentation and the volume quantification of the four muscles of the QF and for the QF as a whole. Considering each muscle of the QF, the DSI of the multi-atlas-based approach was high 0.87 ± 0.11 and the best results were obtained with the combination of two deformation fields resulting from the SyN registration method and the STEPS fusion algorithm. The optimal variables for FFD

  9. Blood-threshold CMR volume analysis of functional univentricular heart.

    PubMed

    Secchi, Francesco; Alì, Marco; Petrini, Marcello; Pluchinotta, Francesca Romana; Cozzi, Andrea; Carminati, Mario; Sardanelli, Francesco

    2018-05-01

    To validate a blood-threshold (BT) segmentation software for cardiac magnetic resonance (CMR) cine images in patients with functional univentricular heart (FUH). We evaluated retrospectively 44 FUH patients aged 25 ± 8 years (mean ± standard deviation). For each patient, the epicardial contour of the single ventricle was manually segmented on cine images by two readers and an automated BT algorithm was independently applied to calculate end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and cardiac mass (CM). Aortic flow analysis (AFA) was performed on through-plane images to obtain forward volumes and used as a benchmark. Reproducibility was tested in a subgroup of 24 randomly selected patients. Wilcoxon, Spearman, and Bland-Altman statistics were used. No significant difference was found between SV (median 57.7 ml; interquartile range 47.9-75.6) and aortic forward flow (57.4 ml; 48.9-80.4) (p = 0.123), with a high correlation (r = 0.789, p < 0.001). Intra-reader reproducibility was 86% for SV segmentation, and 96% for AFA. Inter-reader reproducibility was 85 and 96%, respectively. The BT segmentation provided an accurate and reproducible assessment of heart function in FUH patients.

  10. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features.

    PubMed

    Zheng, Yefeng; Barbu, Adrian; Georgescu, Bogdan; Scheuering, Michael; Comaniciu, Dorin

    2008-11-01

    We propose an automatic four-chamber heart segmentation system for the quantitative functional analysis of the heart from cardiac computed tomography (CT) volumes. Two topics are discussed: heart modeling and automatic model fitting to an unseen volume. Heart modeling is a nontrivial task since the heart is a complex nonrigid organ. The model must be anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. Unlike previous work, we explicitly represent important landmarks (such as the valves and the ventricular septum cusps) among the control points of the model. The control points can be detected reliably to guide the automatic model fitting process. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3-D CT volumes. We formulate the segmentation as a two-step learning problem: anatomical structure localization and boundary delineation. In both steps, we exploit the recent advances in learning discriminative models. A novel algorithm, marginal space learning (MSL), is introduced to solve the 9-D similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3-D shape through learning-based boundary delineation. The proposed method has been extensively tested on the largest dataset (with 323 volumes from 137 patients) ever reported in the literature. To the best of our knowledge, our system is the fastest with a speed of 4.0 s per volume (on a dual-core 3.2-GHz processor) for the automatic segmentation of all four chambers.

  11. Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest

    NASA Astrophysics Data System (ADS)

    Park, Sang Cheol; Kim, Won Pil; Zheng, Bin; Leader, Joseph K.; Pu, Jiantao; Tan, Jun; Gur, David

    2009-02-01

    Airways tree segmentation is an important step in quantitatively assessing the severity of and changes in several lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. It can also be used in guiding bronchoscopy. The purpose of this study is to develop an automated scheme for segmenting the airways tree structure depicted on chest CT examinations. After lung volume segmentation, the scheme defines the first cylinder-like volume of interest (VOI) using a series of images depicting the trachea. The scheme then iteratively defines and adds subsequent VOIs using a region growing algorithm combined with adaptively determined thresholds in order to trace possible sections of airways located inside the combined VOI in question. The airway tree segmentation process is automatically terminated after the scheme assesses all defined VOIs in the iteratively assembled VOI list. In this preliminary study, ten CT examinations with 1.25mm section thickness and two different CT image reconstruction kernels ("bone" and "standard") were selected and used to test the proposed airways tree segmentation scheme. The experiment results showed that (1) adopting this approach affectively prevented the scheme from infiltrating into the parenchyma, (2) the proposed method reasonably accurately segmented the airways trees with lower false positive identification rate as compared with other previously reported schemes that are based on 2-D image segmentation and data analyses, and (3) the proposed adaptive, iterative threshold selection method for the region growing step in each identified VOI enables the scheme to segment the airways trees reliably to the 4th generation in this limited dataset with successful segmentation up to the 5th generation in a fraction of the airways tree branches.

  12. Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

    PubMed

    Dalmış, Mehmet Ufuk; Litjens, Geert; Holland, Katharina; Setio, Arnaud; Mann, Ritse; Karssemeijer, Nico; Gubern-Mérida, Albert

    2017-02-01

    Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep-learning approach known as "U-net." We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in-house developed workstation. We experimented with the application of U-net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U-nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3-class (3C) U-net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas-based method and a sheetness-based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed

  13. Dynamic gadoxetate-enhanced MRI for the assessment of total and segmental liver function and volume in primary sclerosing cholangitis.

    PubMed

    Nilsson, Henrik; Blomqvist, Lennart; Douglas, Lena; Nordell, Anders; Jacobsson, Hans; Hagen, Karin; Bergquist, Annika; Jonas, Eduard

    2014-04-01

    To evaluate dynamic hepatocyte-specific contrast-enhanced MRI (DHCE-MRI) for the assessment of global and segmental liver volume and function in patients with primary sclerosing cholangitis (PSC), and to explore the heterogeneous distribution of liver function in this patient group. Twelve patients with primary sclerosing cholangitis (PSC) and 20 healthy volunteers were examined using DHCE-MRI with Gd-EOB-DTPA. Segmental and total liver volume were calculated, and functional parameters (hepatic extraction fraction [HEF], input relative blood-flow [irBF], and mean transit time [MTT]) were calculated in each liver voxel using deconvolutional analysis. In each study subject, and incongruence score (IS) was constructed to describe the mismatch between segmental function and volume. Among patients, the liver function parameters were correlated to bile duct obstruction and to established scoring models for liver disease. Liver function was significantly more heterogeneously distributed in the patient group (IS 1.0 versus 0.4). There were significant correlations between biliary obstruction and segmental functional parameters (HEF rho -0.24; irBF rho -0.45), and the Mayo risk score correlated significantly with the total liver extraction capacity of Gd-EOB-DTPA (rho -0.85). The study demonstrates a new method to quantify total and segmental liver function using DHCE-MRI in patients with PSC. Copyright © 2013 Wiley Periodicals, Inc.

  14. Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT

    NASA Astrophysics Data System (ADS)

    Liu, Qingyi; Mohy-ud-Din, Hassan; Boutagy, Nabil E.; Jiang, Mingyan; Ren, Silin; Stendahl, John C.; Sinusas, Albert J.; Liu, Chi

    2017-05-01

    Anatomical-based partial volume correction (PVC) has been shown to improve image quality and quantitative accuracy in cardiac SPECT/CT. However, this method requires manual segmentation of various organs from contrast-enhanced computed tomography angiography (CTA) data. In order to achieve fully automatic CTA segmentation for clinical translation, we investigated the most common multi-atlas segmentation methods. We also modified the multi-atlas segmentation method by introducing a novel label fusion algorithm for multiple organ segmentation to eliminate overlap and gap voxels. To evaluate our proposed automatic segmentation, eight canine 99mTc-labeled red blood cell SPECT/CT datasets that incorporated PVC were analyzed, using the leave-one-out approach. The Dice similarity coefficient of each organ was computed. Compared to the conventional label fusion method, our proposed label fusion method effectively eliminated gaps and overlaps and improved the CTA segmentation accuracy. The anatomical-based PVC of cardiac SPECT images with automatic multi-atlas segmentation provided consistent image quality and quantitative estimation of intramyocardial blood volume, as compared to those derived using manual segmentation. In conclusion, our proposed automatic multi-atlas segmentation method of CTAs is feasible, practical, and facilitates anatomical-based PVC of cardiac SPECT/CT images.

  15. Multi-Modal Glioblastoma Segmentation: Man versus Machine

    PubMed Central

    Pica, Alessia; Schucht, Philippe; Beck, Jürgen; Verma, Rajeev Kumar; Slotboom, Johannes; Reyes, Mauricio; Wiest, Roland

    2014-01-01

    Background and Purpose Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. Methods We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. Results Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. Conclusions In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity. PMID:24804720

  16. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

    PubMed Central

    Lyden, Hannah; Gimbel, Sarah I.; Del Piero, Larissa; Tsai, A. Bryna; Sachs, Matthew E.; Kaplan, Jonas T.; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used. PMID:27656121

  17. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results.

    PubMed

    Lyden, Hannah; Gimbel, Sarah I; Del Piero, Larissa; Tsai, A Bryna; Sachs, Matthew E; Kaplan, Jonas T; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used.

  18. Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors

    NASA Astrophysics Data System (ADS)

    Gloger, Oliver; Tönnies, Klaus; Bülow, Robin; Völzke, Henry

    2017-07-01

    To develop the first fully automated 3D spleen segmentation framework derived from T1-weighted magnetic resonance (MR) imaging data and to verify its performance for spleen delineation and volumetry. This approach considers the issue of low contrast between spleen and adjacent tissue in non-contrast-enhanced MR images. Native T1-weighted MR volume data was performed on a 1.5 T MR system in an epidemiological study. We analyzed random subsamples of MR examinations without pathologies to develop and verify the spleen segmentation framework. The framework is modularized to include different kinds of prior knowledge into the segmentation pipeline. Classification by support vector machines differentiates between five different shape types in computed foreground probability maps and recognizes characteristic spleen regions in axial slices of MR volume data. A spleen-shape space generated by training produces subject-specific prior shape knowledge that is then incorporated into a final 3D level set segmentation method. Individually adapted shape-driven forces as well as image-driven forces resulting from refined foreground probability maps steer the level set successfully to the segment the spleen. The framework achieves promising segmentation results with mean Dice coefficients of nearly 0.91 and low volumetric mean errors of 6.3%. The presented spleen segmentation approach can delineate spleen tissue in native MR volume data. Several kinds of prior shape knowledge including subject-specific 3D prior shape knowledge can be used to guide segmentation processes achieving promising results.

  19. Model-based segmentation in orbital volume measurement with cone beam computed tomography and evaluation against current concepts.

    PubMed

    Wagner, Maximilian E H; Gellrich, Nils-Claudius; Friese, Karl-Ingo; Becker, Matthias; Wolter, Franz-Erich; Lichtenstein, Juergen T; Stoetzer, Marcus; Rana, Majeed; Essig, Harald

    2016-01-01

    Objective determination of the orbital volume is important in the diagnostic process and in evaluating the efficacy of medical and/or surgical treatment of orbital diseases. Tools designed to measure orbital volume with computed tomography (CT) often cannot be used with cone beam CT (CBCT) because of inferior tissue representation, although CBCT has the benefit of greater availability and lower patient radiation exposure. Therefore, a model-based segmentation technique is presented as a new method for measuring orbital volume and compared to alternative techniques. Both eyes from thirty subjects with no known orbital pathology who had undergone CBCT as a part of routine care were evaluated (n = 60 eyes). Orbital volume was measured with manual, atlas-based, and model-based segmentation methods. Volume measurements, volume determination time, and usability were compared between the three methods. Differences in means were tested for statistical significance using two-tailed Student's t tests. Neither atlas-based (26.63 ± 3.15 mm(3)) nor model-based (26.87 ± 2.99 mm(3)) measurements were significantly different from manual volume measurements (26.65 ± 4.0 mm(3)). However, the time required to determine orbital volume was significantly longer for manual measurements (10.24 ± 1.21 min) than for atlas-based (6.96 ± 2.62 min, p < 0.001) or model-based (5.73 ± 1.12 min, p < 0.001) measurements. All three orbital volume measurement methods examined can accurately measure orbital volume, although atlas-based and model-based methods seem to be more user-friendly and less time-consuming. The new model-based technique achieves fully automated segmentation results, whereas all atlas-based segmentations at least required manipulations to the anterior closing. Additionally, model-based segmentation can provide reliable orbital volume measurements when CT image quality is poor.

  20. 3D robust Chan-Vese model for industrial computed tomography volume data segmentation

    NASA Astrophysics Data System (ADS)

    Liu, Linghui; Zeng, Li; Luan, Xiao

    2013-11-01

    Industrial computed tomography (CT) has been widely applied in many areas of non-destructive testing (NDT) and non-destructive evaluation (NDE). In practice, CT volume data to be dealt with may be corrupted by noise. This paper addresses the segmentation of noisy industrial CT volume data. Motivated by the research on the Chan-Vese (CV) model, we present a region-based active contour model that draws upon intensity information in local regions with a controllable scale. In the presence of noise, a local energy is firstly defined according to the intensity difference within a local neighborhood. Then a global energy is defined to integrate local energy with respect to all image points. In a level set formulation, this energy is represented by a variational level set function, where a surface evolution equation is derived for energy minimization. Comparative analysis with the CV model indicates the comparable performance of the 3D robust Chan-Vese (RCV) model. The quantitative evaluation also shows the segmentation accuracy of 3D RCV. In addition, the efficiency of our approach is validated under several types of noise, such as Poisson noise, Gaussian noise, salt-and-pepper noise and speckle noise.

  1. A parameter for the assessment of the segmentation of TEM tomography reconstructed volumes based on mutual information.

    PubMed

    Okariz, Ana; Guraya, Teresa; Iturrondobeitia, Maider; Ibarretxe, Julen

    2017-12-01

    A method is proposed and verified for selecting the optimum segmentation of a TEM reconstruction among the results of several segmentation algorithms. The selection criterion is the accuracy of the segmentation. To do this selection, a parameter for the comparison of the accuracies of the different segmentations has been defined. It consists of the mutual information value between the acquired TEM images of the sample and the Radon projections of the segmented volumes. In this work, it has been proved that this new mutual information parameter and the Jaccard coefficient between the segmented volume and the ideal one are correlated. In addition, the results of the new parameter are compared to the results obtained from another validated method to select the optimum segmentation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

    PubMed

    Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

    2017-02-01

    Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

  3. Accurate airway segmentation based on intensity structure analysis and graph-cut

    NASA Astrophysics Data System (ADS)

    Meng, Qier; Kitsaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Mori, Kensaku

    2016-03-01

    This paper presents a novel airway segmentation method based on intensity structure analysis and graph-cut. Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3-D airway tree structure from a CT volume is quite challenging. Several researchers have proposed automated algorithms basically based on region growing and machine learning techniques. However these methods failed to detect the peripheral bronchi branches. They caused a large amount of leakage. This paper presents a novel approach that permits more accurate extraction of complex bronchial airway region. Our method are composed of three steps. First, the Hessian analysis is utilized for enhancing the line-like structure in CT volumes, then a multiscale cavity-enhancement filter is employed to detect the cavity-like structure from the previous enhanced result. In the second step, we utilize the support vector machine (SVM) to construct a classifier for removing the FP regions generated. Finally, the graph-cut algorithm is utilized to connect all of the candidate voxels to form an integrated airway tree. We applied this method to sixteen cases of 3D chest CT volumes. The results showed that the branch detection rate of this method can reach about 77.7% without leaking into the lung parenchyma areas.

  4. Volumetric analysis of pelvic hematomas after blunt trauma using semi-automated seeded region growing segmentation: a method validation study.

    PubMed

    Dreizin, David; Bodanapally, Uttam K; Neerchal, Nagaraj; Tirada, Nikki; Patlas, Michael; Herskovits, Edward

    2016-11-01

    Manually segmented traumatic pelvic hematoma volumes are strongly predictive of active bleeding at conventional angiography, but the method is time intensive, limiting its clinical applicability. We compared volumetric analysis using semi-automated region growing segmentation to manual segmentation and diameter-based size estimates in patients with pelvic hematomas after blunt pelvic trauma. A 14-patient cohort was selected in an anonymous randomized fashion from a dataset of patients with pelvic binders at MDCT, collected retrospectively as part of a HIPAA-compliant IRB-approved study from January 2008 to December 2013. To evaluate intermethod differences, one reader (R1) performed three volume measurements using the manual technique and three volume measurements using the semi-automated technique. To evaluate interobserver differences for semi-automated segmentation, a second reader (R2) performed three semi-automated measurements. One-way analysis of variance was used to compare differences in mean volumes. Time effort was also compared. Correlation between the two methods as well as two shorthand appraisals (greatest diameter, and the ABC/2 method for estimating ellipsoid volumes) was assessed with Spearman's rho (r). Intraobserver variability was lower for semi-automated compared to manual segmentation, with standard deviations ranging between ±5-32 mL and ±17-84 mL, respectively (p = 0.0003). There was no significant difference in mean volumes between the two readers' semi-automated measurements (p = 0.83); however, means were lower for the semi-automated compared with the manual technique (manual: mean and SD 309.6 ± 139 mL; R1 semi-auto: 229.6 ± 88.2 mL, p = 0.004; R2 semi-auto: 243.79 ± 99.7 mL, p = 0.021). Despite differences in means, the correlation between the two methods was very strong and highly significant (r = 0.91, p < 0.001). Correlations with diameter-based methods were only moderate and nonsignificant. Mean semi

  5. Image Segmentation Analysis for NASA Earth Science Applications

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2010-01-01

    NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

  6. A 3D Hermite-based multiscale local active contour method with elliptical shape constraints for segmentation of cardiac MR and CT volumes.

    PubMed

    Barba-J, Leiner; Escalante-Ramírez, Boris; Vallejo Venegas, Enrique; Arámbula Cosío, Fernando

    2018-05-01

    Analysis of cardiac images is a fundamental task to diagnose heart problems. Left ventricle (LV) is one of the most important heart structures used for cardiac evaluation. In this work, we propose a novel 3D hierarchical multiscale segmentation method based on a local active contour (AC) model and the Hermite transform (HT) for LV analysis in cardiac magnetic resonance (MR) and computed tomography (CT) volumes in short axis view. Features such as directional edges, texture, and intensities are analyzed using the multiscale HT space. A local AC model is configured using the HT coefficients and geometrical constraints. The endocardial and epicardial boundaries are used for evaluation. Segmentation of the endocardium is controlled using elliptical shape constraints. The final endocardial shape is used to define the geometrical constraints for segmentation of the epicardium. We follow the assumption that epicardial and endocardial shapes are similar in volumes with short axis view. An initialization scheme based on a fuzzy C-means algorithm and mathematical morphology was designed. The algorithm performance was evaluated using cardiac MR and CT volumes in short axis view demonstrating the feasibility of the proposed method.

  7. Precise segmentation of multiple organs in CT volumes using learning-based approach and information theory.

    PubMed

    Lu, Chao; Zheng, Yefeng; Birkbeck, Neil; Zhang, Jingdan; Kohlberger, Timo; Tietjen, Christian; Boettger, Thomas; Duncan, James S; Zhou, S Kevin

    2012-01-01

    In this paper, we present a novel method by incorporating information theory into the learning-based approach for automatic and accurate pelvic organ segmentation (including the prostate, bladder and rectum). We target 3D CT volumes that are generated using different scanning protocols (e.g., contrast and non-contrast, with and without implant in the prostate, various resolution and position), and the volumes come from largely diverse sources (e.g., diseased in different organs). Three key ingredients are combined to solve this challenging segmentation problem. First, marginal space learning (MSL) is applied to efficiently and effectively localize the multiple organs in the largely diverse CT volumes. Second, learning techniques, steerable features, are applied for robust boundary detection. This enables handling of highly heterogeneous texture pattern. Third, a novel information theoretic scheme is incorporated into the boundary inference process. The incorporation of the Jensen-Shannon divergence further drives the mesh to the best fit of the image, thus improves the segmentation performance. The proposed approach is tested on a challenging dataset containing 188 volumes from diverse sources. Our approach not only produces excellent segmentation accuracy, but also runs about eighty times faster than previous state-of-the-art solutions. The proposed method can be applied to CT images to provide visual guidance to physicians during the computer-aided diagnosis, treatment planning and image-guided radiotherapy to treat cancers in pelvic region.

  8. A novel magnetic resonance imaging segmentation technique for determining diffuse intrinsic pontine glioma tumor volume.

    PubMed

    Singh, Ranjodh; Zhou, Zhiping; Tisnado, Jamie; Haque, Sofia; Peck, Kyung K; Young, Robert J; Tsiouris, Apostolos John; Thakur, Sunitha B; Souweidane, Mark M

    2016-11-01

    OBJECTIVE Accurately determining diffuse intrinsic pontine glioma (DIPG) tumor volume is clinically important. The aims of the current study were to 1) measure DIPG volumes using methods that require different degrees of subjective judgment; and 2) evaluate interobserver agreement of measurements made using these methods. METHODS Eight patients from a Phase I clinical trial testing convection-enhanced delivery (CED) of a therapeutic antibody were included in the study. Pre-CED, post-radiation therapy axial T2-weighted images were analyzed using 2 methods requiring high degrees of subjective judgment (picture archiving and communication system [PACS] polygon and Volume Viewer auto-contour methods) and 1 method requiring a low degree of subjective judgment (k-means clustering segmentation) to determine tumor volumes. Lin's concordance correlation coefficients (CCCs) were calculated to assess interobserver agreement. RESULTS The CCCs of measurements made by 2 observers with the PACS polygon and the Volume Viewer auto-contour methods were 0.9465 (lower 1-sided 95% confidence limit 0.8472) and 0.7514 (lower 1-sided 95% confidence limit 0.3143), respectively. Both were considered poor agreement. The CCC of measurements made using k-means clustering segmentation was 0.9938 (lower 1-sided 95% confidence limit 0.9772), which was considered substantial strength of agreement. CONCLUSIONS The poor interobserver agreement of PACS polygon and Volume Viewer auto-contour methods highlighted the difficulty in consistently measuring DIPG tumor volumes using methods requiring high degrees of subjective judgment. k-means clustering segmentation, which requires a low degree of subjective judgment, showed better interobserver agreement and produced tumor volumes with delineated borders.

  9. 3D prostate TRUS segmentation using globally optimized volume-preserving prior.

    PubMed

    Qiu, Wu; Rajchl, Martin; Guo, Fumin; Sun, Yue; Ukwatta, Eranga; Fenster, Aaron; Yuan, Jing

    2014-01-01

    An efficient and accurate segmentation of 3D transrectal ultrasound (TRUS) images plays an important role in the planning and treatment of the practical 3D TRUS guided prostate biopsy. However, a meaningful segmentation of 3D TRUS images tends to suffer from US speckles, shadowing and missing edges etc, which make it a challenging task to delineate the correct prostate boundaries. In this paper, we propose a novel convex optimization based approach to extracting the prostate surface from the given 3D TRUS image, while preserving a new global volume-size prior. We, especially, study the proposed combinatorial optimization problem by convex relaxation and introduce its dual continuous max-flow formulation with the new bounded flow conservation constraint, which results in an efficient numerical solver implemented on GPUs. Experimental results using 12 patient 3D TRUS images show that the proposed approach while preserving the volume-size prior yielded a mean DSC of 89.5% +/- 2.4%, a MAD of 1.4 +/- 0.6 mm, a MAXD of 5.2 +/- 3.2 mm, and a VD of 7.5% +/- 6.2% in - 1 minute, deomonstrating the advantages of both accuracy and efficiency. In addition, the low standard deviation of the segmentation accuracy shows a good reliability of the proposed approach.

  10. Development and Evaluation of a Semi-automated Segmentation Tool and a Modified Ellipsoid Formula for Volumetric Analysis of the Kidney in Non-contrast T2-Weighted MR Images.

    PubMed

    Seuss, Hannes; Janka, Rolf; Prümmer, Marcus; Cavallaro, Alexander; Hammon, Rebecca; Theis, Ragnar; Sandmair, Martin; Amann, Kerstin; Bäuerle, Tobias; Uder, Michael; Hammon, Matthias

    2017-04-01

    Volumetric analysis of the kidney parenchyma provides additional information for the detection and monitoring of various renal diseases. Therefore the purposes of the study were to develop and evaluate a semi-automated segmentation tool and a modified ellipsoid formula for volumetric analysis of the kidney in non-contrast T2-weighted magnetic resonance (MR)-images. Three readers performed semi-automated segmentation of the total kidney volume (TKV) in axial, non-contrast-enhanced T2-weighted MR-images of 24 healthy volunteers (48 kidneys) twice. A semi-automated threshold-based segmentation tool was developed to segment the kidney parenchyma. Furthermore, the three readers measured renal dimensions (length, width, depth) and applied different formulas to calculate the TKV. Manual segmentation served as a reference volume. Volumes of the different methods were compared and time required was recorded. There was no significant difference between the semi-automatically and manually segmented TKV (p = 0.31). The difference in mean volumes was 0.3 ml (95% confidence interval (CI), -10.1 to 10.7 ml). Semi-automated segmentation was significantly faster than manual segmentation, with a mean difference = 188 s (220 vs. 408 s); p < 0.05. Volumes did not differ significantly comparing the results of different readers. Calculation of TKV with a modified ellipsoid formula (ellipsoid volume × 0.85) did not differ significantly from the reference volume; however, the mean error was three times higher (difference of mean volumes -0.1 ml; CI -31.1 to 30.9 ml; p = 0.95). Applying the modified ellipsoid formula was the fastest way to get an estimation of the renal volume (41 s). Semi-automated segmentation and volumetric analysis of the kidney in native T2-weighted MR data delivers accurate and reproducible results and was significantly faster than manual segmentation. Applying a modified ellipsoid formula quickly provides an accurate kidney volume.

  11. Improving the clinical correlation of multiple sclerosis black hole volume change by paired-scan analysis.

    PubMed

    Tam, Roger C; Traboulsee, Anthony; Riddehough, Andrew; Li, David K B

    2012-01-01

    The change in T 1-hypointense lesion ("black hole") volume is an important marker of pathological progression in multiple sclerosis (MS). Black hole boundaries often have low contrast and are difficult to determine accurately and most (semi-)automated segmentation methods first compute the T 2-hyperintense lesions, which are a superset of the black holes and are typically more distinct, to form a search space for the T 1w lesions. Two main potential sources of measurement noise in longitudinal black hole volume computation are partial volume and variability in the T 2w lesion segmentation. A paired analysis approach is proposed herein that uses registration to equalize partial volume and lesion mask processing to combine T 2w lesion segmentations across time. The scans of 247 MS patients are used to compare a selected black hole computation method with an enhanced version incorporating paired analysis, using rank correlation to a clinical variable (MS functional composite) as the primary outcome measure. The comparison is done at nine different levels of intensity as a previous study suggests that darker black holes may yield stronger correlations. The results demonstrate that paired analysis can strongly improve longitudinal correlation (from -0.148 to -0.303 in this sample) and may produce segmentations that are more sensitive to clinically relevant changes.

  12. A novel magnetic resonance imaging segmentation technique for determining diffuse intrinsic pontine glioma tumor volume

    PubMed Central

    Singh, Ranjodh; Zhou, Zhiping; Tisnado, Jamie; Haque, Sofia; Peck, Kyung K.; Young, Robert J.; Tsiouris, Apostolos John; Thakur, Sunitha B.; Souweidane, Mark M.

    2017-01-01

    OBJECTIVE Accurately determining diffuse intrinsic pontine glioma (DIPG) tumor volume is clinically important. The aims of the current study were to 1) measure DIPG volumes using methods that require different degrees of subjective judgment; and 2) evaluate interobserver agreement of measurements made using these methods. METHODS Eight patients from a Phase I clinical trial testing convection-enhanced delivery (CED) of a therapeutic antibody were included in the study. Pre-CED, post–radiation therapy axial T2-weighted images were analyzed using 2 methods requiring high degrees of subjective judgment (picture archiving and communication system [PACS] polygon and Volume Viewer auto-contour methods) and 1 method requiring a low degree of subjective judgment (k-means clustering segmentation) to determine tumor volumes. Lin’s concordance correlation coefficients (CCCs) were calculated to assess interobserver agreement. RESULTS The CCCs of measurements made by 2 observers with the PACS polygon and the Volume Viewer auto-contour methods were 0.9465 (lower 1-sided 95% confidence limit 0.8472) and 0.7514 (lower 1-sided 95% confidence limit 0.3143), respectively. Both were considered poor agreement. The CCC of measurements made using k-means clustering segmentation was 0.9938 (lower 1-sided 95% confidence limit 0.9772), which was considered substantial strength of agreement. CONCLUSIONS The poor interobserver agreement of PACS polygon and Volume Viewer auto-contour methods high-lighted the difficulty in consistently measuring DIPG tumor volumes using methods requiring high degrees of subjective judgment. k-means clustering segmentation, which requires a low degree of subjective judgment, showed better interob-server agreement and produced tumor volumes with delineated borders. PMID:27391980

  13. Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images.

    PubMed

    Lee, Kyungmoo; Buitendijk, Gabriëlle H S; Bogunovic, Hrvoje; Springelkamp, Henriët; Hofman, Albert; Wahle, Andreas; Sonka, Milan; Vingerling, Johannes R; Klaver, Caroline C W; Abràmoff, Michael D

    2016-03-01

    To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies. Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm 3 ) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis. The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC ( P < 0.01). The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected. Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.

  14. Multi-stage learning for robust lung segmentation in challenging CT volumes.

    PubMed

    Sofka, Michal; Wetzl, Jens; Birkbeck, Neil; Zhang, Jingdan; Kohlberger, Timo; Kaftan, Jens; Declerck, Jérôme; Zhou, S Kevin

    2011-01-01

    Simple algorithms for segmenting healthy lung parenchyma in CT are unable to deal with high density tissue common in pulmonary diseases. To overcome this problem, we propose a multi-stage learning-based approach that combines anatomical information to predict an initialization of a statistical shape model of the lungs. The initialization first detects the carina of the trachea, and uses this to detect a set of automatically selected stable landmarks on regions near the lung (e.g., ribs, spine). These landmarks are used to align the shape model, which is then refined through boundary detection to obtain fine-grained segmentation. Robustness is obtained through hierarchical use of discriminative classifiers that are trained on a range of manually annotated data of diseased and healthy lungs. We demonstrate fast detection (35s per volume on average) and segmentation of 2 mm accuracy on challenging data.

  15. Semiautomatic regional segmentation to measure orbital fat volumes in thyroid-associated ophthalmopathy. A validation study.

    PubMed

    Comerci, M; Elefante, A; Strianese, D; Senese, R; Bonavolontà, P; Alfano, B; Bonavolontà, B; Brunetti, A

    2013-08-01

    This study was designed to validate a novel semi-automated segmentation method to measure regional intra-orbital fat tissue volume in Graves' ophthalmopathy. Twenty-four orbits from 12 patients with Graves' ophthalmopathy, 24 orbits from 12 controls, ten orbits from five MRI study simulations and two orbits from a digital model were used. Following manual region of interest definition of the orbital volumes performed by two operators with different levels of expertise, an automated procedure calculated intra-orbital fat tissue volumes (global and regional, with automated definition of four quadrants). In patients with Graves' disease, clinical activity score and degree of exophthalmos were measured and correlated with intra-orbital fat volumes. Operator performance was evaluated and statistical analysis of the measurements was performed. Accurate intra-orbital fat volume measurements were obtained with coefficients of variation below 5%. The mean operator difference in total fat volume measurements was 0.56%. Patients had significantly higher intra-orbital fat volumes than controls (p<0.001 using Student's t test). Fat volumes and clinical score were significantly correlated (p<0.001). The semi-automated method described here can provide accurate, reproducible intra-orbital fat measurements with low inter-operator variation and good correlation with clinical data.

  16. Partial volume correction and image segmentation for accurate measurement of standardized uptake value of grey matter in the brain.

    PubMed

    Bural, Gonca; Torigian, Drew; Basu, Sandip; Houseni, Mohamed; Zhuge, Ying; Rubello, Domenico; Udupa, Jayaram; Alavi, Abass

    2015-12-01

    Our aim was to explore a novel quantitative method [based upon an MRI-based image segmentation that allows actual calculation of grey matter, white matter and cerebrospinal fluid (CSF) volumes] for overcoming the difficulties associated with conventional techniques for measuring actual metabolic activity of the grey matter. We included four patients with normal brain MRI and fluorine-18 fluorodeoxyglucose (F-FDG)-PET scans (two women and two men; mean age 46±14 years) in this analysis. The time interval between the two scans was 0-180 days. We calculated the volumes of grey matter, white matter and CSF by using a novel segmentation technique applied to the MRI images. We measured the mean standardized uptake value (SUV) representing the whole metabolic activity of the brain from the F-FDG-PET images. We also calculated the white matter SUV from the upper transaxial slices (centrum semiovale) of the F-FDG-PET images. The whole brain volume was calculated by summing up the volumes of the white matter, grey matter and CSF. The global cerebral metabolic activity was calculated by multiplying the mean SUV with total brain volume. The whole brain white matter metabolic activity was calculated by multiplying the mean SUV for the white matter by the white matter volume. The global cerebral metabolic activity only reflects those of the grey matter and the white matter, whereas that of the CSF is zero. We subtracted the global white matter metabolic activity from that of the whole brain, resulting in the global grey matter metabolism alone. We then divided the grey matter global metabolic activity by grey matter volume to accurately calculate the SUV for the grey matter alone. The brain volumes ranged between 1546 and 1924 ml. The mean SUV for total brain was 4.8-7. Total metabolic burden of the brain ranged from 5565 to 9617. The mean SUV for white matter was 2.8-4.1. On the basis of these measurements we generated the grey matter SUV, which ranged from 8.1 to 11.3. The

  17. Automated 3D renal segmentation based on image partitioning

    NASA Astrophysics Data System (ADS)

    Yeghiazaryan, Varduhi; Voiculescu, Irina D.

    2016-03-01

    Despite several decades of research into segmentation techniques, automated medical image segmentation is barely usable in a clinical context, and still at vast user time expense. This paper illustrates unsupervised organ segmentation through the use of a novel automated labelling approximation algorithm followed by a hypersurface front propagation method. The approximation stage relies on a pre-computed image partition forest obtained directly from CT scan data. We have implemented all procedures to operate directly on 3D volumes, rather than slice-by-slice, because our algorithms are dimensionality-independent. The results picture segmentations which identify kidneys, but can easily be extrapolated to other body parts. Quantitative analysis of our automated segmentation compared against hand-segmented gold standards indicates an average Dice similarity coefficient of 90%. Results were obtained over volumes of CT data with 9 kidneys, computing both volume-based similarity measures (such as the Dice and Jaccard coefficients, true positive volume fraction) and size-based measures (such as the relative volume difference). The analysis considered both healthy and diseased kidneys, although extreme pathological cases were excluded from the overall count. Such cases are difficult to segment both manually and automatically due to the large amplitude of Hounsfield unit distribution in the scan, and the wide spread of the tumorous tissue inside the abdomen. In the case of kidneys that have maintained their shape, the similarity range lies around the values obtained for inter-operator variability. Whilst the procedure is fully automated, our tools also provide a light level of manual editing.

  18. Automatic, accurate, and reproducible segmentation of the brain and cerebro-spinal fluid in T1-weighted volume MRI scans and its application to serial cerebral and intracranial volumetry

    NASA Astrophysics Data System (ADS)

    Lemieux, Louis

    2001-07-01

    A new fully automatic algorithm for the segmentation of the brain and cerebro-spinal fluid (CSF) from T1-weighted volume MRI scans of the head was specifically developed in the context of serial intra-cranial volumetry. The method is an extension of a previously published brain extraction algorithm. The brain mask is used as a basis for CSF segmentation based on morphological operations, automatic histogram analysis and thresholding. Brain segmentation is then obtained by iterative tracking of the brain-CSF interface. Grey matter (GM), white matter (WM) and CSF volumes are calculated based on a model of intensity probability distribution that includes partial volume effects. Accuracy was assessed using a digital phantom scan. Reproducibility was assessed by segmenting pairs of scans from 20 normal subjects scanned 8 months apart and 11 patients with epilepsy scanned 3.5 years apart. Segmentation accuracy as measured by overlap was 98% for the brain and 96% for the intra-cranial tissues. The volume errors were: total brain (TBV): -1.0%, intra-cranial (ICV):0.1%, CSF: +4.8%. For repeated scans, matching resulted in improved reproducibility. In the controls, the coefficient of reliability (CR) was 1.5% for the TVB and 1.0% for the ICV. In the patients, the Cr for the ICV was 1.2%.

  19. Improving CCTA-based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation.

    PubMed

    Freiman, Moti; Nickisch, Hannes; Prevrhal, Sven; Schmitt, Holger; Vembar, Mani; Maurovich-Horvat, Pál; Donnelly, Patrick; Goshen, Liran

    2017-03-01

    , integrating PVE analysis into an automatic coronary lumen segmentation algorithm improved the flow simulation specificity from 0.6 to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved the area under the ROC curve for detecting hemodynamically significant CAD from 0.76 to 0.8 compared to automatic segmentation without PVE analysis with invasive FFR threshold of 0.8 as the reference standard. Accounting for PVE in flow simulation to support the detection of hemodynamic significant disease in CCTA-based obstructive lesions improved specificity from 0.51 to 0.73 with same sensitivity of 0.83 and the area under the curve from 0.69 to 0.79. The improvement in the AUC was statistically significant (N = 76, Delong's test, P = 0.012). Accounting for the partial volume effects in automatic coronary lumen segmentation algorithms has the potential to improve the accuracy of CCTA-based hemodynamic assessment of coronary artery lesions. © 2017 American Association of Physicists in Medicine.

  20. Automatic partitioning of head CTA for enabling segmentation

    NASA Astrophysics Data System (ADS)

    Suryanarayanan, Srikanth; Mullick, Rakesh; Mallya, Yogish; Kamath, Vidya; Nagaraj, Nithin

    2004-05-01

    Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: "proximal", "middle", and "distal". The "proximal" and "distal" sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the "middle" partition that remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy, and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds / slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing the "proximal" and "distal" partitions. Complex methods are restricted to only the "middle" partition. The partitionenabled segmentation has been successfully tested and results are shown from multiple cases.

  1. The Navy Enlistment Field Marketing Experiment. Volume 7. The Wharton Administered Navy Tracking Survey: A Segmentation Approach

    DTIC Science & Technology

    1982-10-15

    the two may interact. L1 *1 -35- REFERENCES [1] Arnold, Stephen J. (1979), "A Test for Clusters," Journal of Marketing Research , November, pp 545-551...of Marketing Research , August, pp 405-412. APPENDIX A RESULTS OF FACTOR ANALYSIS OF LIFE GOALS . . . . . -37- Ft-M AnSIS CF FEq. LIFE GOALS GM"L...Volume 5, Pre-intervention Recruiting Environ- ment, 1981. [9] Wind, Yoram (1978), "Issues and Advances in Segmentation Research," Journal of Marketing

  2. Non-invasive measurement of choroidal volume change and ocular rigidity through automated segmentation of high-speed OCT imaging

    PubMed Central

    Beaton, L.; Mazzaferri, J.; Lalonde, F.; Hidalgo-Aguirre, M.; Descovich, D.; Lesk, M. R.; Costantino, S.

    2015-01-01

    We have developed a novel optical approach to determine pulsatile ocular volume changes using automated segmentation of the choroid, which, together with Dynamic Contour Tonometry (DCT) measurements of intraocular pressure (IOP), allows estimation of the ocular rigidity (OR) coefficient. Spectral Domain Optical Coherence Tomography (OCT) videos were acquired with Enhanced Depth Imaging (EDI) at 7Hz during ~50 seconds at the fundus. A novel segmentation algorithm based on graph search with an edge-probability weighting scheme was developed to measure choroidal thickness (CT) at each frame. Global ocular volume fluctuations were derived from frame-to-frame CT variations using an approximate eye model. Immediately after imaging, IOP and ocular pulse amplitude (OPA) were measured using DCT. OR was calculated from these peak pressure and volume changes. Our automated segmentation algorithm provides the first non-invasive method for determining ocular volume change due to pulsatile choroidal filling, and the estimation of the OR constant. Future applications of this method offer an important avenue to understanding the biomechanical basis of ocular pathophysiology. PMID:26137373

  3. Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry?

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

    Heeswijk, Miriam M. van; Department of Surgery, Maastricht University Medical Centre, Maastricht; Lambregts, Doenja M.J., E-mail: d.lambregts@nki.nl

    Purpose: Diffusion-weighted imaging (DWI) tumor volumetry is promising for rectal cancer response assessment, but an important drawback is that manual per-slice tumor delineation can be highly time consuming. This study investigated whether manual DWI-volumetry can be reproduced using a (semi)automated segmentation approach. Methods and Materials: Seventy-nine patients underwent magnetic resonance imaging (MRI) that included DWI (highest b value [b1000 or b1100]) before and after chemoradiation therapy (CRT). Tumor volumes were assessed on b1000 (or b1100) DWI before and after CRT by means of (1) automated segmentation (by 2 inexperienced readers), (2) semiautomated segmentation (manual adjustment of the volumes obtained bymore » method 1 by 2 radiologists), and (3) manual segmentation (by 2 radiologists); this last assessment served as the reference standard. Intraclass correlation coefficients (ICC) and Dice similarity indices (DSI) were calculated to evaluate agreement between different methods and observers. Measurement times (from a radiologist's perspective) were recorded for each method. Results: Tumor volumes were not significantly different among the 3 methods, either before or after CRT (P=.08 to .92). ICCs compared to manual segmentation were 0.80 to 0.91 and 0.53 to 0.66 before and after CRT, respectively, for the automated segmentation and 0.91 to 0.97 and 0.61 to 0.75, respectively, for the semiautomated method. Interobserver agreement (ICC) pre and post CRT was 0.82 and 0.59 for automated segmentation, 0.91 and 0.73 for semiautomated segmentation, and 0.91 and 0.75 for manual segmentation, respectively. Mean DSI between the automated and semiautomated method were 0.83 and 0.58 pre-CRT and post-CRT, respectively; DSI between the automated and manual segmentation were 0.68 and 0.42 and 0.70 and 0.41 between the semiautomated and manual segmentation, respectively. Median measurement time for the radiologists was 0 seconds (pre- and post-CRT) for the

  4. Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry?

    PubMed

    van Heeswijk, Miriam M; Lambregts, Doenja M J; van Griethuysen, Joost J M; Oei, Stanley; Rao, Sheng-Xiang; de Graaff, Carla A M; Vliegen, Roy F A; Beets, Geerard L; Papanikolaou, Nikos; Beets-Tan, Regina G H

    2016-03-15

    Diffusion-weighted imaging (DWI) tumor volumetry is promising for rectal cancer response assessment, but an important drawback is that manual per-slice tumor delineation can be highly time consuming. This study investigated whether manual DWI-volumetry can be reproduced using a (semi)automated segmentation approach. Seventy-nine patients underwent magnetic resonance imaging (MRI) that included DWI (highest b value [b1000 or b1100]) before and after chemoradiation therapy (CRT). Tumor volumes were assessed on b1000 (or b1100) DWI before and after CRT by means of (1) automated segmentation (by 2 inexperienced readers), (2) semiautomated segmentation (manual adjustment of the volumes obtained by method 1 by 2 radiologists), and (3) manual segmentation (by 2 radiologists); this last assessment served as the reference standard. Intraclass correlation coefficients (ICC) and Dice similarity indices (DSI) were calculated to evaluate agreement between different methods and observers. Measurement times (from a radiologist's perspective) were recorded for each method. Tumor volumes were not significantly different among the 3 methods, either before or after CRT (P=.08 to .92). ICCs compared to manual segmentation were 0.80 to 0.91 and 0.53 to 0.66 before and after CRT, respectively, for the automated segmentation and 0.91 to 0.97 and 0.61 to 0.75, respectively, for the semiautomated method. Interobserver agreement (ICC) pre and post CRT was 0.82 and 0.59 for automated segmentation, 0.91 and 0.73 for semiautomated segmentation, and 0.91 and 0.75 for manual segmentation, respectively. Mean DSI between the automated and semiautomated method were 0.83 and 0.58 pre-CRT and post-CRT, respectively; DSI between the automated and manual segmentation were 0.68 and 0.42 and 0.70 and 0.41 between the semiautomated and manual segmentation, respectively. Median measurement time for the radiologists was 0 seconds (pre- and post-CRT) for the automated method, 41 to 69 seconds (pre-CRT) and

  5. Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching.

    PubMed

    Liao, Miao; Zhao, Yu-Qian; Liu, Xi-Yao; Zeng, Ye-Zhan; Zou, Bei-Ji; Wang, Xiao-Fang; Shih, Frank Y

    2017-05-01

    Identifying liver regions from abdominal computed tomography (CT) volumes is an important task for computer-aided liver disease diagnosis and surgical planning. This paper presents a fully automatic method for liver segmentation from CT volumes based on graph cuts and border marching. An initial slice is segmented by density peak clustering. Based on pixel- and patch-wise features, an intensity model and a PCA-based regional appearance model are developed to enhance the contrast between liver and background. Then, these models as well as the location constraint estimated iteratively are integrated into graph cuts in order to segment the liver in each slice automatically. Finally, a vessel compensation method based on the border marching is used to increase the segmentation accuracy. Experiments are conducted on a clinical data set we created and also on the MICCAI2007 Grand Challenge liver data. The results show that the proposed intensity, appearance models, and the location constraint are significantly effective for liver recognition, and the undersegmented vessels can be compensated by the border marching based method. The segmentation performances in terms of VOE, RVD, ASD, RMSD, and MSD as well as the average running time achieved by our method on the SLIVER07 public database are 5.8 ± 3.2%, -0.1 ± 4.1%, 1.0 ± 0.5mm, 2.0 ± 1.2mm, 21.2 ± 9.3mm, and 4.7 minutes, respectively, which are superior to those of existing methods. The proposed method does not require time-consuming training process and statistical model construction, and is capable of dealing with complicated shapes and intensity variations successfully. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Guiding automated left ventricular chamber segmentation in cardiac imaging using the concept of conserved myocardial volume.

    PubMed

    Garson, Christopher D; Li, Bing; Acton, Scott T; Hossack, John A

    2008-06-01

    The active surface technique using gradient vector flow allows semi-automated segmentation of ventricular borders. The accuracy of the algorithm depends on the optimal selection of several key parameters. We investigated the use of conservation of myocardial volume for quantitative assessment of each of these parameters using synthetic and in vivo data. We predicted that for a given set of model parameters, strong conservation of volume would correlate with accurate segmentation. The metric was most useful when applied to the gradient vector field weighting and temporal step-size parameters, but less effective in guiding an optimal choice of the active surface tension and rigidity parameters.

  7. Brain extraction in partial volumes T2*@7T by using a quasi-anatomic segmentation with bias field correction.

    PubMed

    Valente, João; Vieira, Pedro M; Couto, Carlos; Lima, Carlos S

    2018-02-01

    Poor brain extraction in Magnetic Resonance Imaging (MRI) has negative consequences in several types of brain post-extraction such as tissue segmentation and related statistical measures or pattern recognition algorithms. Current state of the art algorithms for brain extraction work on weighted T1 and T2, being not adequate for non-whole brain images such as the case of T2*FLASH@7T partial volumes. This paper proposes two new methods that work directly in T2*FLASH@7T partial volumes. The first is an improvement of the semi-automatic threshold-with-morphology approach adapted to incomplete volumes. The second method uses an improved version of a current implementation of the fuzzy c-means algorithm with bias correction for brain segmentation. Under high inhomogeneity conditions the performance of the first method degrades, requiring user intervention which is unacceptable. The second method performed well for all volumes, being entirely automatic. State of the art algorithms for brain extraction are mainly semi-automatic, requiring a correct initialization by the user and knowledge of the software. These methods can't deal with partial volumes and/or need information from atlas which is not available in T2*FLASH@7T. Also, combined volumes suffer from manipulations such as re-sampling which deteriorates significantly voxel intensity structures making segmentation tasks difficult. The proposed method can overcome all these difficulties, reaching good results for brain extraction using only T2*FLASH@7T volumes. The development of this work will lead to an improvement of automatic brain lesions segmentation in T2*FLASH@7T volumes, becoming more important when lesions such as cortical Multiple-Sclerosis need to be detected. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Image segmentation and registration for the analysis of joint motion from 3D MRI

    NASA Astrophysics Data System (ADS)

    Hu, Yangqiu; Haynor, David R.; Fassbind, Michael; Rohr, Eric; Ledoux, William

    2006-03-01

    We report an image segmentation and registration method for studying joint morphology and kinematics from in vivo MRI scans and its application to the analysis of ankle joint motion. Using an MR-compatible loading device, a foot was scanned in a single neutral and seven dynamic positions including maximal flexion, rotation and inversion/eversion. A segmentation method combining graph cuts and level sets was developed which allows a user to interactively delineate 14 bones in the neutral position volume in less than 30 minutes total, including less than 10 minutes of user interaction. In the subsequent registration step, a separate rigid body transformation for each bone is obtained by registering the neutral position dataset to each of the dynamic ones, which produces an accurate description of the motion between them. We have processed six datasets, including 3 normal and 3 pathological feet. For validation our results were compared with those obtained from 3DViewnix, a semi-automatic segmentation program, and achieved good agreement in volume overlap ratios (mean: 91.57%, standard deviation: 3.58%) for all bones. Our tool requires only 1/50 and 1/150 of the user interaction time required by 3DViewnix and NIH Image Plus, respectively, an improvement that has the potential to make joint motion analysis from MRI practical in research and clinical applications.

  9. Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation.

    PubMed

    Kang, Sung-Won; Lee, Woo-Jin; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Kim, Tae-Il; Yi, Won-Jin

    2015-03-01

    We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

  10. 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.

  11. Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography.

    PubMed

    Ahlers, C; Simader, C; Geitzenauer, W; Stock, G; Stetson, P; Dastmalchi, S; Schmidt-Erfurth, U

    2008-02-01

    A limited number of scans compromise conventional optical coherence tomography (OCT) to track chorioretinal disease in its full extension. Failures in edge-detection algorithms falsify the results of retinal mapping even further. High-definition-OCT (HD-OCT) is based on raster scanning and was used to visualise the localisation and volume of intra- and sub-pigment-epithelial (RPE) changes in fibrovascular pigment epithelial detachments (fPED). Two different scanning patterns were evaluated. 22 eyes with fPED were imaged using a frequency-domain, high-speed prototype of the Cirrus HD-OCT. The axial resolution was 6 mum, and the scanning speed was 25 kA scans/s. Two different scanning patterns covering an area of 6 x 6 mm in the macular retina were compared. Three-dimensional topographic reconstructions and volume calculations were performed using MATLAB-based automatic segmentation software. Detailed information about layer-specific distribution of fluid accumulation and volumetric measurements can be obtained for retinal- and sub-RPE volumes. Both raster scans show a high correlation (p<0.01; R2>0.89) of measured values, that is PED volume/area, retinal volume and mean retinal thickness. Quality control of the automatic segmentation revealed reasonable results in over 90% of the examinations. Automatic segmentation allows for detailed quantitative and topographic analysis of the RPE and the overlying retina. In fPED, the 128 x 512 scanning-pattern shows mild advantages when compared with the 256 x 256 scan. Together with the ability for automatic segmentation, HD-OCT clearly improves the clinical monitoring of chorioretinal disease by adding relevant new parameters. HD-OCT is likely capable of enhancing the understanding of pathophysiology and benefits of treatment for current anti-CNV strategies in future.

  12. Automated lung tumor segmentation for whole body PET volume based on novel downhill region growing

    NASA Astrophysics Data System (ADS)

    Ballangan, Cherry; Wang, Xiuying; Eberl, Stefan; Fulham, Michael; Feng, Dagan

    2010-03-01

    We propose an automated lung tumor segmentation method for whole body PET images based on a novel downhill region growing (DRG) technique, which regards homogeneous tumor hotspots as 3D monotonically decreasing functions. The method has three major steps: thoracic slice extraction with K-means clustering of the slice features; hotspot segmentation with DRG; and decision tree analysis based hotspot classification. To overcome the common problem of leakage into adjacent hotspots in automated lung tumor segmentation, DRG employs the tumors' SUV monotonicity features. DRG also uses gradient magnitude of tumors' SUV to improve tumor boundary definition. We used 14 PET volumes from patients with primary NSCLC for validation. The thoracic region extraction step achieved good and consistent results for all patients despite marked differences in size and shape of the lungs and the presence of large tumors. The DRG technique was able to avoid the problem of leakage into adjacent hotspots and produced a volumetric overlap fraction of 0.61 +/- 0.13 which outperformed four other methods where the overlap fraction varied from 0.40 +/- 0.24 to 0.59 +/- 0.14. Of the 18 tumors in 14 NSCLC studies, 15 lesions were classified correctly, 2 were false negative and 15 were false positive.

  13. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation.

    PubMed

    Daisne, Jean-François; Blumhofer, Andreas

    2013-06-26

    Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.

  14. Metabolically active tumour volume segmentation from dynamic [(18)F]FLT PET studies in non-small cell lung cancer.

    PubMed

    Hoyng, Lieke L; Frings, Virginie; Hoekstra, Otto S; Kenny, Laura M; Aboagye, Eric O; Boellaard, Ronald

    2015-01-01

    Positron emission tomography (PET) with (18)F-3'-deoxy-3'-fluorothymidine ([(18)F]FLT) can be used to assess tumour proliferation. A kinetic-filtering (KF) classification algorithm has been suggested for segmentation of tumours in dynamic [(18)F]FLT PET data. The aim of the present study was to evaluate KF segmentation and its test-retest performance in [(18)F]FLT PET in non-small cell lung cancer (NSCLC) patients. Nine NSCLC patients underwent two 60-min dynamic [(18)F]FLT PET scans within 7 days prior to treatment. Dynamic scans were reconstructed with filtered back projection (FBP) as well as with ordered subsets expectation maximisation (OSEM). Twenty-eight lesions were identified by an experienced physician. Segmentation was performed using KF applied to the dynamic data set and a source-to-background corrected 50% threshold (A50%) was applied to the sum image of the last three frames (45- to 60-min p.i.). Furthermore, several adaptations of KF were tested. Both for KF and A50% test-retest (TRT) variability of metabolically active tumour volume and standard uptake value (SUV) were evaluated. KF performed better on OSEM- than on FBP-reconstructed PET images. The original KF implementation segmented 15 out of 28 lesions, whereas A50% segmented each lesion. Adapted KF versions, however, were able to segment 26 out of 28 lesions. In the best performing adapted versions, metabolically active tumour volume and SUV TRT variability was similar to those of A50%. KF misclassified certain tumour areas as vertebrae or liver tissue, which was shown to be related to heterogeneous [(18)F]FLT uptake areas within the tumour. For [(18)F]FLT PET studies in NSCLC patients, KF and A50% show comparable tumour volume segmentation performance. The KF method needs, however, a site-specific optimisation. The A50% is therefore a good alternative for tumour segmentation in NSCLC [(18)F]FLT PET studies in multicentre studies. Yet, it was observed that KF has the potential to subsegment

  15. Vessel segmentation in 3D spectral OCT scans of the retina

    NASA Astrophysics Data System (ADS)

    Niemeijer, Meindert; Garvin, Mona K.; van Ginneken, Bram; Sonka, Milan; Abràmoff, Michael D.

    2008-03-01

    The latest generation of spectral optical coherence tomography (OCT) scanners is able to image 3D cross-sectional volumes of the retina at a high resolution and high speed. These scans offer a detailed view of the structure of the retina. Automated segmentation of the vessels in these volumes may lead to more objective diagnosis of retinal vascular disease including hypertensive retinopathy, retinopathy of prematurity. Additionally, vessel segmentation can allow color fundus images to be registered to these 3D volumes, possibly leading to a better understanding of the structure and localization of retinal structures and lesions. In this paper we present a method for automatically segmenting the vessels in a 3D OCT volume. First, the retina is automatically segmented into multiple layers, using simultaneous segmentation of their boundary surfaces in 3D. Next, a 2D projection of the vessels is produced by only using information from certain segmented layers. Finally, a supervised, pixel classification based vessel segmentation approach is applied to the projection image. We compared the influence of two methods for the projection on the performance of the vessel segmentation on 10 optic nerve head centered 3D OCT scans. The method was trained on 5 independent scans. Using ROC analysis, our proposed vessel segmentation system obtains an area under the curve of 0.970 when compared with the segmentation of a human observer.

  16. SU-E-J-123: Assessing Segmentation Accuracy of Internal Volumes and Sub-Volumes in 4D PET/CT of Lung Tumors Using a Novel 3D Printed Phantom

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

    Soultan, D; Murphy, J; James, C

    2015-06-15

    Purpose: To assess the accuracy of internal target volume (ITV) segmentation of lung tumors for treatment planning of simultaneous integrated boost (SIB) radiotherapy as seen in 4D PET/CT images, using a novel 3D-printed phantom. Methods: The insert mimics high PET tracer uptake in the core and 50% uptake in the periphery, by using a porous design at the periphery. A lung phantom with the insert was placed on a programmable moving platform. Seven breathing waveforms of ideal and patient-specific respiratory motion patterns were fed to the platform, and 4D PET/CT scans were acquired of each of them. CT images weremore » binned into 10 phases, and PET images were binned into 5 phases following the clinical protocol. Two scenarios were investigated for segmentation: a gate 30–70 window, and no gating. The radiation oncologist contoured the outer ITV of the porous insert with on CT images, while the internal void volume with 100% uptake was contoured on PET images for being indistinguishable from the outer volume in CT images. Segmented ITVs were compared to the expected volumes based on known target size and motion. Results: 3 ideal breathing patterns, 2 regular-breathing patient waveforms, and 2 irregular-breathing patient waveforms were used for this study. 18F-FDG was used as the PET tracer. The segmented ITVs from CT closely matched the expected motion for both no gating and gate 30–70 window, with disagreement of contoured ITV with respect to the expected volume not exceeding 13%. PET contours were seen to overestimate volumes in all the cases, up to more than 40%. Conclusion: 4DPET images of a novel 3D printed phantom designed to mimic different uptake values were obtained. 4DPET contours overestimated ITV volumes in all cases, while 4DCT contours matched expected ITV volume values. Investigation of the cause and effects of the discrepancies is undergoing.« less

  17. A novel approach to segmentation and measurement of medical image using level set methods.

    PubMed

    Chen, Yao-Tien

    2017-06-01

    The study proposes a novel approach for segmentation and visualization plus value-added surface area and volume measurements for brain medical image analysis. The proposed method contains edge detection and Bayesian based level set segmentation, surface and volume rendering, and surface area and volume measurements for 3D objects of interest (i.e., brain tumor, brain tissue, or whole brain). Two extensions based on edge detection and Bayesian level set are first used to segment 3D objects. Ray casting and a modified marching cubes algorithm are then adopted to facilitate volume and surface visualization of medical-image dataset. To provide physicians with more useful information for diagnosis, the surface area and volume of an examined 3D object are calculated by the techniques of linear algebra and surface integration. Experiment results are finally reported in terms of 3D object extraction, surface and volume rendering, and surface area and volume measurements for medical image analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation

    PubMed Central

    Kang, Sung-Won; Lee, Woo-Jin; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe

    2015-01-01

    Purpose We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). Materials and Methods The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. Results VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). Conclusion It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method. PMID:25793178

  19. Multi-atlas segmentation of the cartilage in knee MR images with sequential volume- and bone-mask-based registrations

    NASA Astrophysics Data System (ADS)

    Lee, Han Sang; Kim, Hyeun A.; Kim, Hyeonjin; Hong, Helen; Yoon, Young Cheol; Kim, Junmo

    2016-03-01

    In spite of its clinical importance in diagnosis of osteoarthritis, segmentation of cartilage in knee MRI remains a challenging task due to its shape variability and low contrast with surrounding soft tissues and synovial fluid. In this paper, we propose a multi-atlas segmentation of cartilage in knee MRI with sequential atlas registrations and locallyweighted voting (LWV). First, bone is segmented by sequential volume- and object-based registrations and LWV. Second, to overcome the shape variability of cartilage, cartilage is segmented by bone-mask-based registration and LWV. In experiments, the proposed method improved the bone segmentation by reducing misclassified bone region, and enhanced the cartilage segmentation by preventing cartilage leakage into surrounding similar intensity region, with the help of sequential registrations and LWV.

  20. Evaluation metrics for bone segmentation in ultrasound

    NASA Astrophysics Data System (ADS)

    Lougheed, Matthew; Fichtinger, Gabor; Ungi, Tamas

    2015-03-01

    Tracked ultrasound is a safe alternative to X-ray for imaging bones. The interpretation of bony structures is challenging as ultrasound has no specific intensity characteristic of bones. Several image segmentation algorithms have been devised to identify bony structures. We propose an open-source framework that would aid in the development and comparison of such algorithms by quantitatively measuring segmentation performance in the ultrasound images. True-positive and false-negative metrics used in the framework quantify algorithm performance based on correctly segmented bone and correctly segmented boneless regions. Ground-truth for these metrics are defined manually and along with the corresponding automatically segmented image are used for the performance analysis. Manually created ground truth tests were generated to verify the accuracy of the analysis. Further evaluation metrics for determining average performance per slide and standard deviation are considered. The metrics provide a means of evaluating accuracy of frames along the length of a volume. This would aid in assessing the accuracy of the volume itself and the approach to image acquisition (positioning and frequency of frame). The framework was implemented as an open-source module of the 3D Slicer platform. The ground truth tests verified that the framework correctly calculates the implemented metrics. The developed framework provides a convenient way to evaluate bone segmentation algorithms. The implementation fits in a widely used application for segmentation algorithm prototyping. Future algorithm development will benefit by monitoring the effects of adjustments to an algorithm in a standard evaluation framework.

  1. Segmentation of brain volume based on 3D region growing by integrating intensity and edge for image-guided surgery

    NASA Astrophysics Data System (ADS)

    Tsagaan, Baigalmaa; Abe, Keiichi; Goto, Masahiro; Yamamoto, Seiji; Terakawa, Susumu

    2006-03-01

    This paper presents a segmentation method of brain tissues from MR images, invented for our image-guided neurosurgery system under development. Our goal is to segment brain tissues for creating biomechanical model. The proposed segmentation method is based on 3-D region growing and outperforms conventional approaches by stepwise usage of intensity similarities between voxels in conjunction with edge information. Since the intensity and the edge information are complementary to each other in the region-based segmentation, we use them twice by performing a coarse-to-fine extraction. First, the edge information in an appropriate neighborhood of the voxel being considered is examined to constrain the region growing. The expanded region of the first extraction result is then used as the domain for the next processing. The intensity and the edge information of the current voxel only are utilized in the final extraction. Before segmentation, the intensity parameters of the brain tissues as well as partial volume effect are estimated by using expectation-maximization (EM) algorithm in order to provide an accurate data interpretation into the extraction. We tested the proposed method on T1-weighted MR images of brain and evaluated the segmentation effectiveness comparing the results with ground truths. Also, the generated meshes from the segmented brain volume by using mesh generating software are shown in this paper.

  2. Fetal brain volumetry through MRI volumetric reconstruction and segmentation

    PubMed Central

    Estroff, Judy A.; Barnewolt, Carol E.; Connolly, Susan A.; Warfield, Simon K.

    2013-01-01

    Purpose Fetal MRI volumetry is a useful technique but it is limited by a dependency upon motion-free scans, tedious manual segmentation, and spatial inaccuracy due to thick-slice scans. An image processing pipeline that addresses these limitations was developed and tested. Materials and methods The principal sequences acquired in fetal MRI clinical practice are multiple orthogonal single-shot fast spin echo scans. State-of-the-art image processing techniques were used for inter-slice motion correction and super-resolution reconstruction of high-resolution volumetric images from these scans. The reconstructed volume images were processed with intensity non-uniformity correction and the fetal brain extracted by using supervised automated segmentation. Results Reconstruction, segmentation and volumetry of the fetal brains for a cohort of twenty-five clinically acquired fetal MRI scans was done. Performance metrics for volume reconstruction, segmentation and volumetry were determined by comparing to manual tracings in five randomly chosen cases. Finally, analysis of the fetal brain and parenchymal volumes was performed based on the gestational age of the fetuses. Conclusion The image processing pipeline developed in this study enables volume rendering and accurate fetal brain volumetry by addressing the limitations of current volumetry techniques, which include dependency on motion-free scans, manual segmentation, and inaccurate thick-slice interpolation. PMID:20625848

  3. Fast and robust segmentation of the striatum using deep convolutional neural networks.

    PubMed

    Choi, Hongyoon; Jin, Kyong Hwan

    2016-12-01

    Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transformation. We employed two serial CNN, Global and Local CNN: The Global CNN determined approximate locations of the striatum. It performed a regression of input MR images fitted to smoothed segmentation maps of the striatum. From the output volume of Global CNN, cropped MR volumes which included the striatum were extracted. The cropped MR volumes and the output volumes of Global CNN were used for inputs of Local CNN. Local CNN predicted the accurate label of all voxels. Segmentation results were compared with a widely used segmentation method, FreeSurfer. Our method showed higher Dice Similarity Coefficient (DSC) (0.893±0.017 vs. 0.786±0.015) and precision score (0.905±0.018 vs. 0.690±0.022) than FreeSurfer-based striatum segmentation (p=0.06). Our approach was also tested using another independent dataset, which showed high DSC (0.826±0.038) comparable with that of FreeSurfer. Comparison with existing method Segmentation performance of our proposed method was comparable with that of FreeSurfer. The running time of our approach was approximately three seconds. We suggested a fast and accurate deep CNN-based segmentation for small brain structures which can be widely applied to brain image analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

  4. Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404.

    PubMed

    Besemer, Abigail E; Titz, Benjamin; Grudzinski, Joseph J; Weichert, Jamey P; Kuo, John S; Robins, H Ian; Hall, Lance T; Bednarz, Bryan P

    2017-07-06

    Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124 I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131 I-CLR1404 voxel-level dose distribution was calculated from the 124 I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3-63.9 cc, 0.1-34.7 cc, and 0.4-11.8 cc, respectively. The average  ±  standard deviation (range) was 0.19  ±  0.13 (0.01-0.51), 0.30  ±  0.17 (0.03-0.67), and 0.75  ±  0.29 (0.05-1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131 I-CLR1404 tumor doses ranged from 0.28-1.75 Gy GBq -1 (0.07-0.37 Gy GBq -1 ). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4-2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for

  5. Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404

    NASA Astrophysics Data System (ADS)

    Besemer, Abigail E.; Titz, Benjamin; Grudzinski, Joseph J.; Weichert, Jamey P.; Kuo, John S.; Robins, H. Ian; Hall, Lance T.; Bednarz, Bryan P.

    2017-08-01

    Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131I-CLR1404 voxel-level dose distribution was calculated from the 124I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3-63.9 cc, 0.1-34.7 cc, and 0.4-11.8 cc, respectively. The average  ±  standard deviation (range) was 0.19  ±  0.13 (0.01-0.51), 0.30  ±  0.17 (0.03-0.67), and 0.75  ±  0.29 (0.05-1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131I-CLR1404 tumor doses ranged from 0.28-1.75 Gy GBq-1 (0.07-0.37 Gy GBq-1). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4-2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for standard

  6. Semi-automated brain tumor and edema segmentation using MRI.

    PubMed

    Xie, Kai; Yang, Jie; Zhang, Z G; Zhu, Y M

    2005-10-01

    Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. A semi-automated method has been developed for brain tumor and edema segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments non-enhancing brain tumor and edema from healthy tissues in magnetic resonance images. In this study, a semi-automated method was developed for brain tumor and edema segmentation and volume measurement using magnetic resonance imaging (MRI). Some novel algorithms for tumor segmentation from MRI were integrated in this medical diagnosis system. We exploit a hybrid level set (HLS) segmentation method driven by region and boundary information simultaneously, region information serves as a propagation force which is robust and boundary information serves as a stopping functional which is accurate. Ten different patients with brain tumors of different size, shape and location were selected, a total of 246 axial tumor-containing slices obtained from 10 patients were used to evaluate the effectiveness of segmentation methods. This method was applied to 10 non-enhancing brain tumors and satisfactory results were achieved. Two quantitative measures for tumor segmentation quality estimation, namely, correspondence ratio (CR) and percent matching (PM), were performed. For the segmentation of brain tumor, the volume total PM varies from 79.12 to 93.25% with the mean of 85.67+/-4.38% while the volume total CR varies from 0.74 to 0.91 with the mean of 0.84+/-0.07. For the segmentation of edema, the volume total PM varies from 72.86 to 87.29% with the mean of 79.54+/-4.18% while the volume total CR varies from 0.69 to 0.85 with the mean of 0.79+/-0.08. The HLS segmentation method perform better than the classical level sets (LS) segmentation method in PM and CR. The results of this research may have potential applications, both as a staging procedure and a method of

  7. Three-dimensional murine airway segmentation in micro-CT images

    NASA Astrophysics Data System (ADS)

    Shi, Lijun; Thiesse, Jacqueline; McLennan, Geoffrey; Hoffman, Eric A.; Reinhardt, Joseph M.

    2007-03-01

    Thoracic imaging for small animals has emerged as an important tool for monitoring pulmonary disease progression and therapy response in genetically engineered animals. Micro-CT is becoming the standard thoracic imaging modality in small animal imaging because it can produce high-resolution images of the lung parenchyma, vasculature, and airways. Segmentation, measurement, and visualization of the airway tree is an important step in pulmonary image analysis. However, manual analysis of the airway tree in micro-CT images can be extremely time-consuming since a typical dataset is usually on the order of several gigabytes in size. Automated and semi-automated tools for micro-CT airway analysis are desirable. In this paper, we propose an automatic airway segmentation method for in vivo micro-CT images of the murine lung and validate our method by comparing the automatic results to manual tracing. Our method is based primarily on grayscale morphology. The results show good visual matches between manually segmented and automatically segmented trees. The average true positive volume fraction compared to manual analysis is 91.61%. The overall runtime for the automatic method is on the order of 30 minutes per volume compared to several hours to a few days for manual analysis.

  8. A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes.

    PubMed

    Miri, Mohammad Saleh; Abràmoff, Michael D; Kwon, Young H; Sonka, Milan; Garvin, Mona K

    2017-07-01

    Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes. The problem is formulated as an optimization problem for finding a 3D path within the SD-OCT volume. In particular, the SD-OCT volumes are transferred to the radial domain where the closed loop BMO points in the original volume form a path within the radial volume. The estimated location of BMO points in 3D are identified by finding the projected location of BMO points using a graph-theoretic approach and mapping the projected locations onto the Bruch's membrane (BM) surface. Dynamic programming is employed in order to find the 3D BMO locations as the minimum-cost path within the volume. In order to compute the cost function needed for finding the minimum-cost path, a random forest classifier is utilized to learn a BMO model, obtained by extracting intensity features from the volumes in the training set, and computing the required 3D cost function. The proposed method is tested on 44 glaucoma patients and evaluated using manual delineations. Results show that the proposed method successfully identifies the 3D BMO locations and has significantly smaller errors compared to the existing 3D BMO identification approaches. Published by Elsevier B.V.

  9. Segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis

    NASA Astrophysics Data System (ADS)

    Han, Miaofei; Ma, Jinfeng; Li, Yan; Li, Meiling; Song, Yanli; Li, Qiang

    2015-03-01

    Accurate segmentation of organs at risk (OARs) is a key step in treatment planning system (TPS) of image guided radiation therapy. We are developing three classes of methods to segment 17 organs at risk throughout the whole body, including brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin. The three classes of segmentation methods include (1) threshold-based methods for organs of large contrast with adjacent structures such as lungs, trachea, and skin; (2) context-driven Generalized Hough Transform-based methods combined with graph cut algorithm for robust localization and segmentation of liver, kidneys and spleen; and (3) atlas and registration-based methods for segmentation of heart and all organs in CT volumes of head and pelvis. The segmentation accuracy for the seventeen organs was subjectively evaluated by two medical experts in three levels of score: 0, poor (unusable in clinical practice); 1, acceptable (minor revision needed); and 2, good (nearly no revision needed). A database was collected from Ruijin Hospital, Huashan Hospital, and Xuhui Central Hospital in Shanghai, China, including 127 head scans, 203 thoracic scans, 154 abdominal scans, and 73 pelvic scans. The percentages of "good" segmentation results were 97.6%, 92.9%, 81.1%, 87.4%, 85.0%, 78.7%, 94.1%, 91.1%, 81.3%, 86.7%, 82.5%, 86.4%, 79.9%, 72.6%, 68.5%, 93.2%, 96.9% for brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin, respectively. Various organs at risk can be reliably segmented from CT scans by use of the three classes of segmentation methods.

  10. Robust tissue-air volume segmentation of MR images based on the statistics of phase and magnitude: Its applications in the display of susceptibility-weighted imaging of the brain.

    PubMed

    Du, Yiping P; Jin, Zhaoyang

    2009-10-01

    To develop a robust algorithm for tissue-air segmentation in magnetic resonance imaging (MRI) using the statistics of phase and magnitude of the images. A multivariate measure based on the statistics of phase and magnitude was constructed for tissue-air volume segmentation. The standard deviation of first-order phase difference and the standard deviation of magnitude were calculated in a 3 x 3 x 3 kernel in the image domain. To improve differentiation accuracy, the uniformity of phase distribution in the kernel was also calculated and linear background phase introduced by field inhomogeneity was corrected. The effectiveness of the proposed volume segmentation technique was compared to a conventional approach that uses the magnitude data alone. The proposed algorithm was shown to be more effective and robust in volume segmentation in both synthetic phantom and susceptibility-weighted images of human brain. Using our proposed volume segmentation method, veins in the peripheral regions of the brain were well depicted in the minimum-intensity projection of the susceptibility-weighted images. Using the additional statistics of phase, tissue-air volume segmentation can be substantially improved compared to that using the statistics of magnitude data alone. (c) 2009 Wiley-Liss, Inc.

  11. Biometric analysis of pigment dispersion syndrome using anterior segment optical coherence tomography.

    PubMed

    Aptel, Florent; Beccat, Sylvain; Fortoul, Vincent; Denis, Philippe

    2011-08-01

    To compare anterior chamber volume (ACV), iris volume, and iridolenticular contact (ILC) area before and after laser peripheral iridotomy (LPI) in eyes with pigment dispersion syndrome (PDS) using anterior segment optical coherence tomography (AS OCT) and image processing software. Cross-sectional study. Eighteen eyes of 18 patients with PDS; 30 eyes of 30 controls matched for age, gender, and refraction. Anterior segment OCT imaging was performed in all eyes before LPI and 1, 4, and 12 weeks after LPI. At each visit, 12 cross-sectional images of the AS were taken: 4 in bright conditions with accommodation (accommodation), 4 in bright conditions without accommodation (physiological miosis), and 4 under dark conditions (physiologic mydriasis). Biometric parameters were estimated using AS OCT radial sections and customized image-processing software. Anterior chamber volume, iris volume-to-length ratio, ILC area, AS OCT anterior chamber depth, and A-scan ultrasonography axial length. Before LPI, PDS eyes had a significantly greater ACV and ILC area than control eyes (P<0.01) and a significantly smaller iris volume-to-length ratio than the controls (P<0.05). After LPI, ACV and ILC area decreased significantly in PDS eyes, but iris volume-to-length ratio increased significantly (P<0.02) and was not significantly different from that of controls. These biometric changes were stable over time. Iris volume-to-length ratio decreased significantly from accommodation to mydriasis and from miosis to mydriasis, both in PDS and control eyes (P<0.01). In PDS eyes, ILC area decreased significantly from accommodation to mydriasis, both before and after LPI (P<0.01). On multivariate analysis, greater anterior chamber (AC) volume (P<0.02) and larger AC depth (P<0.05) before LPI were significant predictors of a larger ILC area. Pigment dispersion syndrome eyes do not have an iris that is abnormally large, relative to the AS size, but have a weakly resistant iris that is stretched and

  12. Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma

    PubMed Central

    Dunn, William D.; Aerts, Hugo J.W.L.; Cooper, Lee A.; Holder, Chad A.; Hwang, Scott N.; Jaffe, Carle C.; Brat, Daniel J.; Jain, Rajan; Flanders, Adam E.; Zinn, Pascal O.; Colen, Rivka R.; Gutman, David A.

    2017-01-01

    Background Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis. Methods Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression. Results We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman’s r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features. Conclusion Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement

  13. Volume of Lytic Vertebral Body Metastatic Disease Quantified Using Computed Tomography–Based Image Segmentation Predicts Fracture Risk After Spine Stereotactic Body Radiation Therapy

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

    Thibault, Isabelle; Department of Radiation Oncology, Centre Hospitalier de L'Universite de Québec–Université Laval, Quebec, Quebec; Whyne, Cari M.

    Purpose: To determine a threshold of vertebral body (VB) osteolytic or osteoblastic tumor involvement that would predict vertebral compression fracture (VCF) risk after stereotactic body radiation therapy (SBRT), using volumetric image-segmentation software. Methods and Materials: A computational semiautomated skeletal metastasis segmentation process refined in our laboratory was applied to the pretreatment planning CT scan of 100 vertebral segments in 55 patients treated with spine SBRT. Each VB was segmented and the percentage of lytic and/or blastic disease by volume determined. Results: The cumulative incidence of VCF at 3 and 12 months was 14.1% and 17.3%, respectively. The median follow-up was 7.3 months (range,more » 0.6-67.6 months). In all, 56% of segments were determined lytic, 23% blastic, and 21% mixed, according to clinical radiologic determination. Within these 3 clinical cohorts, the segmentation-determined mean percentages of lytic and blastic tumor were 8.9% and 6.0%, 0.2% and 26.9%, and 3.4% and 15.8% by volume, respectively. On the basis of the entire cohort (n=100), a significant association was observed for the osteolytic percentage measures and the occurrence of VCF (P<.001) but not for the osteoblastic measures. The most significant lytic disease threshold was observed at ≥11.6% (odds ratio 37.4, 95% confidence interval 9.4-148.9). On multivariable analysis, ≥11.6% lytic disease (P<.001), baseline VCF (P<.001), and SBRT with ≥20 Gy per fraction (P=.014) were predictive. Conclusions: Pretreatment lytic VB disease volumetric measures, independent of the blastic component, predict for SBRT-induced VCF. Larger-scale trials evaluating our software are planned to validate the results.« less

  14. An automatic method of brain tumor segmentation from MRI volume based on the symmetry of brain and level set method

    NASA Astrophysics Data System (ADS)

    Li, Xiaobing; Qiu, Tianshuang; Lebonvallet, Stephane; Ruan, Su

    2010-02-01

    This paper presents a brain tumor segmentation method which automatically segments tumors from human brain MRI image volume. The presented model is based on the symmetry of human brain and level set method. Firstly, the midsagittal plane of an MRI volume is searched, the slices with potential tumor of the volume are checked out according to their symmetries, and an initial boundary of the tumor in the slice, in which the tumor is in the largest size, is determined meanwhile by watershed and morphological algorithms; Secondly, the level set method is applied to the initial boundary to drive the curve evolving and stopping to the appropriate tumor boundary; Lastly, the tumor boundary is projected one by one to its adjacent slices as initial boundaries through the volume for the whole tumor. The experiment results are compared with hand tracking of the expert and show relatively good accordance between both.

  15. Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST).

    PubMed

    Amann, Michael; Andělová, Michaela; Pfister, Armanda; Mueller-Lenke, Nicole; Traud, Stefan; Reinhardt, Julia; Magon, Stefano; Bendfeldt, Kerstin; Kappos, Ludwig; Radue, Ernst-Wilhelm; Stippich, Christoph; Sprenger, Till

    2015-01-01

    Brain atrophy has been identified as an important contributing factor to the development of disability in multiple sclerosis (MS). In this respect, more and more interest is focussing on the role of deep grey matter (DGM) areas. Novel data analysis pipelines are available for the automatic segmentation of DGM using three-dimensional (3D) MRI data. However, in clinical trials, often no such high-resolution data are acquired and hence no conclusions regarding the impact of new treatments on DGM atrophy were possible so far. In this work, we used FMRIB's Integrated Registration and Segmentation Tool (FIRST) to evaluate the possibility of segmenting DGM structures using standard two-dimensional (2D) T1-weighted MRI. In a cohort of 70 MS patients, both 2D and 3D T1-weighted data were acquired. The thalamus, putamen, pallidum, nucleus accumbens, and caudate nucleus were bilaterally segmented using FIRST. Volumes were calculated for each structure and for the sum of basal ganglia (BG) as well as for the total DGM. The accuracy and reliability of the 2D data segmentation were compared with the respective results of 3D segmentations using volume difference, volume overlap and intra-class correlation coefficients (ICCs). The mean differences for the individual substructures were between 1.3% (putamen) and -25.2% (nucleus accumbens). The respective values for the BG were -2.7% and for DGM 1.3%. Mean volume overlap was between 89.1% (thalamus) and 61.5% (nucleus accumbens); BG: 84.1%; DGM: 86.3%. Regarding ICC, all structures showed good agreement with the exception of the nucleus accumbens. The results of the segmentation were additionally validated through expert manual delineation of the caudate nucleus and putamen in a subset of the 3D data. In conclusion, we demonstrate that subcortical segmentation of 2D data are feasible using FIRST. The larger subcortical GM structures can be segmented with high consistency. This forms the basis for the application of FIRST in large 2D

  16. Impact of endobronchial coiling on segmental bronchial lumen in treated and untreated lung lobes: Correlation with changes in lung volume, clinical and pulmonary function tests.

    PubMed

    Kloth, C; Thaiss, W M; Hetzel, J; Ditt, H; Grosse, U; Nikolaou, K; Horger, M

    2016-07-01

    To assess the impact of endobronchial coiling on the segment bronchus cross-sectional area and volumes in patients with lung emphysema using quantitative chest-CT measurements. Thirty patients (female = 15; median age = 65.36 years) received chest-CT before and after endobronchial coiling for lung volume reduction (LVR) between January 2010 and December 2014. Thin-slice (0.6 mm) non-enhanced image data sets were acquired both at end-inspiration and end-expiration using helical technique and 120 kV/100-150 mAs. Clinical response was defined as an increase in the walking distance (Six-minute walk test; 6MWT) after LVR-therapy. Additionally, pulmonary function test (PFT) measurements were used for clinical correlation. In the treated segmental bronchia, the cross-sectional lumen area showed significant reduction (p < 0.05) in inspiration and tendency towards enlargement in expiration (p > 0.05). In the ipsilateral lobes, the lumina showed no significant changes. In the contralateral lung, we found tendency towards increased cross-sectional area in inspiration (p = 0.06). Volumes of the treated segments correlated with the treated segmental bronchial lumina in expiration (r = 0.80, p < 0.001). Clinical correlation with changes in 6MWT/PFT showed a significant decrease of the inspiratory volume of the treated lobe in responders only. Endobronchial coiling causes significant decrease in the cross-sectional area of treated segment bronchi in inspiration and a slight increase in expiration accompanied by a volume reduction. • Endobronchial coiling has indirect impact on cross-sectional area of treated segment bronchi • Volume changes of treated lobes correlate with changes in bronchial cross-sectional area • Coil-induced effects reflect their stabilizing and stiffening impact on lung parenchyma • Endobronchial coiling reduces bronchial collapsing compensating the loss of elasticity.

  17. Development of automatic visceral fat volume calculation software for CT volume data.

    PubMed

    Nemoto, Mitsutaka; Yeernuer, Tusufuhan; Masutani, Yoshitaka; Nomura, Yukihiro; Hanaoka, Shouhei; Miki, Soichiro; Yoshikawa, Takeharu; Hayashi, Naoto; Ohtomo, Kuni

    2014-01-01

    To develop automatic visceral fat volume calculation software for computed tomography (CT) volume data and to evaluate its feasibility. A total of 24 sets of whole-body CT volume data and anthropometric measurements were obtained, with three sets for each of four BMI categories (under 20, 20 to 25, 25 to 30, and over 30) in both sexes. True visceral fat volumes were defined on the basis of manual segmentation of the whole-body CT volume data by an experienced radiologist. Software to automatically calculate visceral fat volumes was developed using a region segmentation technique based on morphological analysis with CT value threshold. Automatically calculated visceral fat volumes were evaluated in terms of the correlation coefficient with the true volumes and the error relative to the true volume. Automatic visceral fat volume calculation results of all 24 data sets were obtained successfully and the average calculation time was 252.7 seconds/case. The correlation coefficients between the true visceral fat volume and the automatically calculated visceral fat volume were over 0.999. The newly developed software is feasible for calculating visceral fat volumes in a reasonable time and was proved to have high accuracy.

  18. Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes.

    PubMed

    Subbanna, Nagesh K; Precup, Doina; Collins, D Louis; Arbel, Tal

    2013-01-01

    In this paper, we present a fully automated hierarchical probabilistic framework for segmenting brain tumours from multispectral human brain magnetic resonance images (MRIs) using multiwindow Gabor filters and an adapted Markov Random Field (MRF) framework. In the first stage, a customised Gabor decomposition is developed, based on the combined-space characteristics of the two classes (tumour and non-tumour) in multispectral brain MRIs in order to optimally separate tumour (including edema) from healthy brain tissues. A Bayesian framework then provides a coarse probabilistic texture-based segmentation of tumours (including edema) whose boundaries are then refined at the voxel level through a modified MRF framework that carefully separates the edema from the main tumour. This customised MRF is not only built on the voxel intensities and class labels as in traditional MRFs, but also models the intensity differences between neighbouring voxels in the likelihood model, along with employing a prior based on local tissue class transition probabilities. The second inference stage is shown to resolve local inhomogeneities and impose a smoothing constraint, while also maintaining the appropriate boundaries as supported by the local intensity difference observations. The method was trained and tested on the publicly available MICCAI 2012 Brain Tumour Segmentation Challenge (BRATS) Database [1] on both synthetic and clinical volumes (low grade and high grade tumours). Our method performs well compared to state-of-the-art techniques, outperforming the results of the top methods in cases of clinical high grade and low grade tumour core segmentation by 40% and 45% respectively.

  19. Automatic delineation of tumor volumes by co-segmentation of combined PET/MR data

    NASA Astrophysics Data System (ADS)

    Leibfarth, S.; Eckert, F.; Welz, S.; Siegel, C.; Schmidt, H.; Schwenzer, N.; Zips, D.; Thorwarth, D.

    2015-07-01

    Combined PET/MRI may be highly beneficial for radiotherapy treatment planning in terms of tumor delineation and characterization. To standardize tumor volume delineation, an automatic algorithm for the co-segmentation of head and neck (HN) tumors based on PET/MR data was developed. Ten HN patient datasets acquired in a combined PET/MR system were available for this study. The proposed algorithm uses both the anatomical T2-weighted MR and FDG-PET data. For both imaging modalities tumor probability maps were derived, assigning each voxel a probability of being cancerous based on its signal intensity. A combination of these maps was subsequently segmented using a threshold level set algorithm. To validate the method, tumor delineations from three radiation oncologists were available. Inter-observer variabilities and variabilities between the algorithm and each observer were quantified by means of the Dice similarity index and a distance measure. Inter-observer variabilities and variabilities between observers and algorithm were found to be comparable, suggesting that the proposed algorithm is adequate for PET/MR co-segmentation. Moreover, taking into account combined PET/MR data resulted in more consistent tumor delineations compared to MR information only.

  20. Outdoor recreation activity trends by volume segments: U.S. and Northeast market analyses, 1982-1989

    Treesearch

    Rodney B. Warnick

    1992-01-01

    The purpose of this review was to examine volume segmentation within three selected outdoor recreational activities -- swimming, hunting and downhill skiing over an eight-year period, from 1982 through 1989 at the national level and within the Northeast Region of the U.S.; and to determine if trend patterns existed within any of these activities when the market size...

  1. AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

    PubMed

    Kong, Jun; Wang, Fusheng; Teodoro, George; Liang, Yanhui; Zhu, Yangyang; Tucker-Burden, Carol; Brat, Daniel J

    2015-04-01

    A large number of cell-oriented cancer investigations require an effective and reliable cell segmentation method on three dimensional (3D) fluorescence microscopic images for quantitative analysis of cell biological properties. In this paper, we present a fully automated cell segmentation method that can detect cells from 3D fluorescence microscopic images. Enlightened by fluorescence imaging techniques, we regulated the image gradient field by gradient vector flow (GVF) with interpolated and smoothed data volume, and grouped voxels based on gradient modes identified by tracking GVF field. Adaptive thresholding was then applied to voxels associated with the same gradient mode where voxel intensities were enhanced by a multiscale cell filter. We applied the method to a large volume of 3D fluorescence imaging data of human brain tumor cells with (1) small cell false detection and missing rates for individual cells; and (2) trivial over and under segmentation incidences for clustered cells. Additionally, the concordance of cell morphometry structure between automated and manual segmentation was encouraging. These results suggest a promising 3D cell segmentation method applicable to cancer studies.

  2. Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

    NASA Astrophysics Data System (ADS)

    Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing; Harrigan, Robert L.; Assad, Albert; Abramson, Richard G.; Landman, Bennett A.

    2017-02-01

    Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≍1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

  3. Multi-atlas Segmentation Enables Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly.

    PubMed

    Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing; Harrigan, Robert L; Assad, Albert; Abramson, Richard G; Landman, Bennett A

    2017-02-11

    Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≈1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

  4. Analysis of Regional Effects on Market Segment Production

    DTIC Science & Technology

    2016-06-01

    REGIONAL EFFECTS ON MARKET SEGMENT PRODUCTION by James D. Moffitt June 2016 Thesis Advisor: Lyn R. Whitaker Co-Advisor: Jonathan K. Alt...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE ANALYSIS OF REGIONAL EFFECTS ON MARKET SEGMENT PRODUCTION 5. FUNDING NUMBERS 6...accessions in Potential Rating Index Zip Code Market New Evolution (PRIZM NE) market segments. This model will aid USAREC G2 analysts involved in

  5. Calculation of Lung Cancer Volume of Target Based on Thorax Computed Tomography Images using Active Contour Segmentation Method for Treatment Planning System

    NASA Astrophysics Data System (ADS)

    Patra Yosandha, Fiet; Adi, Kusworo; Edi Widodo, Catur

    2017-06-01

    In this research, calculation process of the lung cancer volume of target based on computed tomography (CT) thorax images was done. Volume of the target calculation was done in purpose to treatment planning system in radiotherapy. The calculation of the target volume consists of gross tumor volume (GTV), clinical target volume (CTV), planning target volume (PTV) and organs at risk (OAR). The calculation of the target volume was done by adding the target area on each slices and then multiply the result with the slice thickness. Calculations of area using of digital image processing techniques with active contour segmentation method. This segmentation for contouring to obtain the target volume. The calculation of volume produced on each of the targets is 577.2 cm3 for GTV, 769.9 cm3 for CTV, 877.8 cm3 for PTV, 618.7 cm3 for OAR 1, 1,162 cm3 for OAR 2 right, and 1,597 cm3 for OAR 2 left. These values indicate that the image processing techniques developed can be implemented to calculate the lung cancer target volume based on CT thorax images. This research expected to help doctors and medical physicists in determining and contouring the target volume quickly and precisely.

  6. In vivo estimation of normal amygdala volume from structural MRI scans with anatomical-based segmentation.

    PubMed

    Siozopoulos, Achilleas; Thomaidis, Vasilios; Prassopoulos, Panos; Fiska, Aliki

    2018-02-01

    Literature includes a number of studies using structural MRI (sMRI) to determine the volume of the amygdala, which is modified in various pathologic conditions. The reported values vary widely mainly because of different anatomical approaches to the complex. This study aims at estimating of the normal amygdala volume from sMRI scans using a recent anatomical definition described in a study based on post-mortem material. The amygdala volume has been calculated in 106 healthy subjects, using sMRI and anatomical-based segmentation. The resulting volumes have been analyzed for differences related to hemisphere, sex, and age. The mean amygdalar volume was estimated at 1.42 cm 3 . The mean right amygdala volume has been found larger than the left, but the difference for the raw values was within the limits of the method error. No intersexual differences or age-related alterations have been observed. The study provides a method for determining the boundaries of the amygdala in sMRI scans based on recent anatomical considerations and an estimation of the mean normal amygdala volume from a quite large number of scans for future use in comparative studies.

  7. NSEG, a segmented mission analysis program for low and high speed aircraft. Volume 1: Theoretical development

    NASA Technical Reports Server (NTRS)

    Hague, D. S.; Rozendaal, H. L.

    1977-01-01

    A rapid mission analysis code based on the use of approximate flight path equations of motion is presented. Equation form varies with the segment type, for example, accelerations, climbs, cruises, descents, and decelerations. Realistic and detailed characteristics were specified in tabular form. The code also contains extensive flight envelope performance mapping capabilities. Approximate take off and landing analyses were performed. At high speeds, centrifugal lift effects were accounted for. Extensive turbojet and ramjet engine scaling procedures were incorporated in the code.

  8. Intra- and interoperator variability of lobar pulmonary volumes and emphysema scores in patients with chronic obstructive pulmonary disease and emphysema: comparison of manual and semi-automated segmentation techniques.

    PubMed

    Molinari, Francesco; Pirronti, Tommaso; Sverzellati, Nicola; Diciotti, Stefano; Amato, Michele; Paolantonio, Guglielmo; Gentile, Luigia; Parapatt, George K; D'Argento, Francesco; Kuhnigk, Jan-Martin

    2013-01-01

    We aimed to compare the intra- and interoperator variability of lobar volumetry and emphysema scores obtained by semi-automated and manual segmentation techniques in lung emphysema patients. In two sessions held three months apart, two operators performed lobar volumetry of unenhanced chest computed tomography examinations of 47 consecutive patients with chronic obstructive pulmonary disease and lung emphysema. Both operators used the manual and semi-automated segmentation techniques. The intra- and interoperator variability of the volumes and emphysema scores obtained by semi-automated segmentation was compared with the variability obtained by manual segmentation of the five pulmonary lobes. The intra- and interoperator variability of the lobar volumes decreased when using semi-automated lobe segmentation (coefficients of repeatability for the first operator: right upper lobe, 147 vs. 96.3; right middle lobe, 137.7 vs. 73.4; right lower lobe, 89.2 vs. 42.4; left upper lobe, 262.2 vs. 54.8; and left lower lobe, 260.5 vs. 56.5; coefficients of repeatability for the second operator: right upper lobe, 61.4 vs. 48.1; right middle lobe, 56 vs. 46.4; right lower lobe, 26.9 vs. 16.7; left upper lobe, 61.4 vs. 27; and left lower lobe, 63.6 vs. 27.5; coefficients of reproducibility in the interoperator analysis: right upper lobe, 191.3 vs. 102.9; right middle lobe, 219.8 vs. 126.5; right lower lobe, 122.6 vs. 90.1; left upper lobe, 166.9 vs. 68.7; and left lower lobe, 168.7 vs. 71.6). The coefficients of repeatability and reproducibility of emphysema scores also decreased when using semi-automated segmentation and had ranges that varied depending on the target lobe and selected threshold of emphysema. Semi-automated segmentation reduces the intra- and interoperator variability of lobar volumetry and provides a more objective tool than manual technique for quantifying lung volumes and severity of emphysema.

  9. Radio Frequency Ablation Registration, Segmentation, and Fusion Tool

    PubMed Central

    McCreedy, Evan S.; Cheng, Ruida; Hemler, Paul F.; Viswanathan, Anand; Wood, Bradford J.; McAuliffe, Matthew J.

    2008-01-01

    The Radio Frequency Ablation Segmentation Tool (RFAST) is a software application developed using NIH's Medical Image Processing Analysis and Visualization (MIPAV) API for the specific purpose of assisting physicians in the planning of radio frequency ablation (RFA) procedures. The RFAST application sequentially leads the physician through the steps necessary to register, fuse, segment, visualize and plan the RFA treatment. Three-dimensional volume visualization of the CT dataset with segmented 3D surface models enables the physician to interactively position the ablation probe to simulate burns and to semi-manually simulate sphere packing in an attempt to optimize probe placement. PMID:16871716

  10. Model Uncertainty and Test of a Segmented Mirror Telescope

    DTIC Science & Technology

    2014-03-01

    Optical Telescope project EOM: equation of motion FCA: fine control actuator FCD: Face-Centered Cubic Design FEA: finite element analysis FEM: finite...housed in a dark tent to isolate the telescope from stray light, air currents, or dust and other debris. However, the closed volume is prone to...is composed of six hexagonal segments that each have six coarse control actuators (CCA) for segment phasing control, three fine control actuators

  11. Ventriculogram segmentation using boosted decision trees

    NASA Astrophysics Data System (ADS)

    McDonald, John A.; Sheehan, Florence H.

    2004-05-01

    Left ventricular status, reflected in ejection fraction or end systolic volume, is a powerful prognostic indicator in heart disease. Quantitative analysis of these and other parameters from ventriculograms (cine xrays of the left ventricle) is infrequently performed due to the labor required for manual segmentation. None of the many methods developed for automated segmentation has achieved clinical acceptance. We present a method for semi-automatic segmentation of ventriculograms based on a very accurate two-stage boosted decision-tree pixel classifier. The classifier determines which pixels are inside the ventricle at key ED (end-diastole) and ES (end-systole) frames. The test misclassification rate is about 1%. The classifier is semi-automatic, requiring a user to select 3 points in each frame: the endpoints of the aortic valve and the apex. The first classifier stage is 2 boosted decision-trees, trained using features such as gray-level statistics (e.g. median brightness) and image geometry (e.g. coordinates relative to user supplied 3 points). Second stage classifiers are trained using the same features as the first, plus the output of the first stage. Border pixels are determined from the segmented images using dilation and erosion. A curve is then fit to the border pixels, minimizing a penalty function that trades off fidelity to the border pixels with smoothness. ED and ES volumes, and ejection fraction are estimated from border curves using standard area-length formulas. On independent test data, the differences between automatic and manual volumes (and ejection fractions) are similar in size to the differences between two human observers.

  12. 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

  13. Segmental Analysis of Chlorprothixene and Desmethylchlorprothixene in Postmortem Hair.

    PubMed

    Günther, Kamilla Nyborg; Johansen, Sys Stybe; Wicktor, Petra; Banner, Jytte; Linnet, Kristian

    2018-06-26

    Analysis of drugs in hair differs from their analysis in other tissues due to the extended detection window, as well as the opportunity that segmental hair analysis offers for the detection of changes in drug intake over time. The antipsychotic drug chlorprothixene is widely used, but few reports exist on chlorprothixene concentrations in hair. In this study, we analyzed hair segments from 20 deceased psychiatric patients who had undergone chronic chlorprothixene treatment, and we report hair concentrations of chlorprothixene and its metabolite desmethylchlorprothixene. Three to six 1-cm long segments were analyzed per individual, corresponding to ~3-6 months of hair growth before death, depending on the length of the hair. We used a previously published and fully validated liquid chromatography-tandem mass spectrometry method for the hair analysis. The 10th-90th percentiles of chlorprothixene and desmethylchlorprothixene concentrations in all hair segments were 0.05-0.84 ng/mg and 0.06-0.89 ng/mg, respectively, with medians of 0.21 and 0.24 ng/mg, and means of 0.38 and 0.43 ng/mg. The estimated daily dosages ranged from 28 mg/day to 417 mg/day. We found a significant positive correlation between the concentration in hair and the estimated daily doses for both chlorprothixene (P = 0.0016, slope = 0.0044 [ng/mg hair]/[mg/day]) and the metabolite desmethylchlorprothixene (P = 0.0074). Concentrations generally decreased throughout the hair shaft from proximal to distal segments, with an average reduction in concentration from segment 1 to segment 3 of 24% for all cases, indicating that most of the individuals had been compliant with their treatment. We have provided some guidance regarding reference levels for chlorprothixene and desmethylchlorprothixene concentrations in hair from patients undergoing long-term chlorprothixene treatment.

  14. Longitudinal Analysis of Mouse SDOCT Volumes

    PubMed Central

    Antony, Bhavna J.; Carass, Aaron; Lang, Andrew; Kim, Byung-Jin; Zack, Donald J.; Prince, Jerry L.

    2017-01-01

    Spectral-domain optical coherence tomography (SDOCT), in addition to its routine clinical use in the diagnosis of ocular diseases, has begun to find increasing use in animal studies. Animal models are frequently used to study disease mechanisms as well as to test drug efficacy. In particular, SDOCT provides the ability to study animals longitudinally and non-invasively over long periods of time. However, the lack of anatomical landmarks makes the longitudinal scan acquisition prone to inconsistencies in orientation. Here, we propose a method for the automated registration of mouse SDOCT volumes. The method begins by accurately segmenting the blood vessels and the optic nerve head region in the scans using a pixel classification approach. The segmented vessel maps from follow-up scans were registered using an iterative closest point (ICP) algorithm to the baseline scan to allow for the accurate longitudinal tracking of thickness changes. Eighteen SDOCT volumes from a light damage model study were used to train a random forest utilized in the pixel classification step. The area under the curve (AUC) in a leave-one-out study for the retinal blood vessels and the optic nerve head (ONH) was found to be 0.93 and 0.98, respectively. The complete proposed framework, the retinal vasculature segmentation and the ICP registration, was applied to a secondary set of scans obtained from a light damage model. A qualitative assessment of the registration showed no registration failures. PMID:29138527

  15. Longitudinal analysis of mouse SDOCT volumes

    NASA Astrophysics Data System (ADS)

    Antony, Bhavna J.; Carass, Aaron; Lang, Andrew; Kim, Byung-Jin; Zack, Donald J.; Prince, Jerry L.

    2017-03-01

    Spectral-domain optical coherence tomography (SDOCT), in addition to its routine clinical use in the diagnosis of ocular diseases, has begun to fund increasing use in animal studies. Animal models are frequently used to study disease mechanisms as well as to test drug efficacy. In particular, SDOCT provides the ability to study animals longitudinally and non-invasively over long periods of time. However, the lack of anatomical landmarks makes the longitudinal scan acquisition prone to inconsistencies in orientation. Here, we propose a method for the automated registration of mouse SDOCT volumes. The method begins by accurately segmenting the blood vessels and the optic nerve head region in the scans using a pixel classification approach. The segmented vessel maps from follow-up scans were registered using an iterative closest point (ICP) algorithm to the baseline scan to allow for the accurate longitudinal tracking of thickness changes. Eighteen SDOCT volumes from a light damage model study were used to train a random forest utilized in the pixel classification step. The area under the curve (AUC) in a leave-one-out study for the retinal blood vessels and the optic nerve head (ONH) was found to be 0.93 and 0.98, respectively. The complete proposed framework, the retinal vasculature segmentation and the ICP registration, was applied to a secondary set of scans obtained from a light damage model. A qualitative assessment of the registration showed no registration failures.

  16. Multicenter reliability of semiautomatic retinal layer segmentation using OCT

    PubMed Central

    Oberwahrenbrock, Timm; Traber, Ghislaine L.; Lukas, Sebastian; Gabilondo, Iñigo; Nolan, Rachel; Songster, Christopher; Balk, Lisanne; Petzold, Axel; Paul, Friedemann; Villoslada, Pablo; Brandt, Alexander U.; Green, Ari J.

    2018-01-01

    Objective To evaluate the inter-rater reliability of semiautomated segmentation of spectral domain optical coherence tomography (OCT) macular volume scans. Methods Macular OCT volume scans of left eyes from 17 subjects (8 patients with MS and 9 healthy controls) were automatically segmented by Heidelberg Eye Explorer (v1.9.3.0) beta-software (Spectralis Viewing Module v6.0.0.7), followed by manual correction by 5 experienced operators from 5 different academic centers. The mean thicknesses within a 6-mm area around the fovea were computed for the retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer, outer plexiform layer (OPL), and outer nuclear layer (ONL). Intraclass correlation coefficients (ICCs) were calculated for mean layer thickness values. Spatial distribution of ICC values for the segmented volume scans was investigated using heat maps. Results Agreement between raters was good (ICC > 0.84) for all retinal layers, particularly inner retinal layers showed excellent agreement across raters (ICC > 0.96). Spatial distribution of ICC showed highest values in the perimacular area, whereas the ICCs were poorer for the foveola and the more peripheral macular area. The automated segmentation of the OPL and ONL required the most correction and showed the least agreement, whereas differences were less prominent for the remaining layers. Conclusions Automated segmentation with manual correction of macular OCT scans is highly reliable when performed by experienced raters and can thus be applied in multicenter settings. Reliability can be improved by restricting analysis to the perimacular area and compound segmentation of GCL and IPL. PMID:29552598

  17. [Segment analysis of the target market of physiotherapeutic services].

    PubMed

    Babaskin, D V

    2010-01-01

    The objective of the present study was to demonstrate the possibilities to analyse selected segments of the target market of physiotherapeutic services provided by medical and preventive-facilities of two major types. The main features of a target segment, such as provision of therapeutic massage, are illustrated in terms of two characteristics, namely attractiveness to the users and the ability of a given medical facility to satisfy their requirements. Based on the analysis of portfolio of the available target segments the most promising ones (winner segments) were selected for further marketing studies. This choice does not exclude the possibility of involvement of other segments of medical services in marketing activities.

  18. 3D geometric split-merge segmentation of brain MRI datasets.

    PubMed

    Marras, Ioannis; Nikolaidis, Nikolaos; Pitas, Ioannis

    2014-05-01

    In this paper, a novel method for MRI volume segmentation based on region adaptive splitting and merging is proposed. The method, called Adaptive Geometric Split Merge (AGSM) segmentation, aims at finding complex geometrical shapes that consist of homogeneous geometrical 3D regions. In each volume splitting step, several splitting strategies are examined and the most appropriate is activated. A way to find the maximal homogeneity axis of the volume is also introduced. Along this axis, the volume splitting technique divides the entire volume in a number of large homogeneous 3D regions, while at the same time, it defines more clearly small homogeneous regions within the volume in such a way that they have greater probabilities of survival at the subsequent merging step. Region merging criteria are proposed to this end. The presented segmentation method has been applied to brain MRI medical datasets to provide segmentation results when each voxel is composed of one tissue type (hard segmentation). The volume splitting procedure does not require training data, while it demonstrates improved segmentation performance in noisy brain MRI datasets, when compared to the state of the art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. 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.

  20. Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT.

    PubMed

    Zhou, Jinghao; Yan, Zhennan; Lasio, Giovanni; Huang, Junzhou; Zhang, Baoshe; Sharma, Navesh; Prado, Karl; D'Souza, Warren

    2015-12-01

    To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n=38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation. Published by Elsevier Ltd.

  1. An Analysis of Image Segmentation Time in Beam’s-Eye-View Treatment Planning

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

    Li, Chun; Spelbring, D.R.; Chen, George T.Y.

    In this work we tabulate and histogram the image segmentation time for beam’s eye view (BEV) treatment planning in our center. The average time needed to generate contours on CT images delineating normal structures and treatment target volumes is calculated using a data base containing over 500 patients’ BEV plans. The average number of contours and total image segmentation time needed for BEV plans in three common treatment sites, namely, head/neck, lung/chest, and prostate, were estimated.

  2. Validation of Body Volume Acquisition by Using Elliptical Zone Method.

    PubMed

    Chiu, C-Y; Pease, D L; Fawkner, S; Sanders, R H

    2016-12-01

    The elliptical zone method (E-Zone) can be used to obtain reliable body volume data including total body volume and segmental volumes with inexpensive and portable equipment. The purpose of this research was to assess the accuracy of body volume data obtained from E-Zone by comparing them with those acquired from the 3D photonic scanning method (3DPS). 17 male participants with diverse somatotypes were recruited. Each participant was scanned twice on the same day by a 3D whole-body scanner and photographed twice for the E-Zone analysis. The body volume data acquired from 3DPS was regarded as the reference against which the accuracy of the E-Zone was assessed. The relative technical error of measurement (TEM) of total body volume estimations was around 3% for E-Zone. E-Zone can estimate the segmental volumes of upper torso, lower torso, thigh, shank, upper arm and lower arm accurately (relative TEM<10%) but the accuracy for small segments including the neck, hand and foot were poor. In summary, E-Zone provides a reliable, inexpensive, portable, and simple method to obtain reasonable estimates of total body volume and to indicate segmental volume distribution. © Georg Thieme Verlag KG Stuttgart · New York.

  3. Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model.

    PubMed

    Antony, Bhavna Josephine; Kim, Byung-Jin; Lang, Andrew; Carass, Aaron; Prince, Jerry L; Zack, Donald J

    2017-01-01

    The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.

  4. Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model

    PubMed Central

    Lang, Andrew; Carass, Aaron; Prince, Jerry L.; Zack, Donald J.

    2017-01-01

    The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study. PMID:28817571

  5. 4-D segmentation and normalization of 3He MR images for intrasubject assessment of ventilated lung volumes

    NASA Astrophysics Data System (ADS)

    Contrella, Benjamin; Tustison, Nicholas J.; Altes, Talissa A.; Avants, Brian B.; Mugler, John P., III; de Lange, Eduard E.

    2012-03-01

    Although 3He MRI permits compelling visualization of the pulmonary air spaces, quantitation of absolute ventilation is difficult due to confounds such as field inhomogeneity and relative intensity differences between image acquisition; the latter complicating longitudinal investigations of ventilation variation with respiratory alterations. To address these potential difficulties, we present a 4-D segmentation and normalization approach for intra-subject quantitative analysis of lung hyperpolarized 3He MRI. After normalization, which combines bias correction and relative intensity scaling between longitudinal data, partitioning of the lung volume time series is performed by iterating between modeling of the combined intensity histogram as a Gaussian mixture model and modulating the spatial heterogeneity tissue class assignments through Markov random field modeling. Evaluation of the algorithm was retrospectively applied to a cohort of 10 asthmatics between 19-25 years old in which spirometry and 3He MR ventilation images were acquired both before and after respiratory exacerbation by a bronchoconstricting agent (methacholine). Acquisition was repeated under the same conditions from 7 to 467 days (mean +/- standard deviation: 185 +/- 37.2) later. Several techniques were evaluated for matching intensities between the pre and post-methacholine images with the 95th percentile value histogram matching demonstrating superior correlations with spirometry measures. Subsequent analysis evaluated segmentation parameters for assessing ventilation change in this cohort. Current findings also support previous research that areas of poor ventilation in response to bronchoconstriction are relatively consistent over time.

  6. Short segment search method for phylogenetic analysis using nested sliding windows

    NASA Astrophysics Data System (ADS)

    Iskandar, A. A.; Bustamam, A.; Trimarsanto, H.

    2017-10-01

    To analyze phylogenetics in Bioinformatics, coding DNA sequences (CDS) segment is needed for maximal accuracy. However, analysis by CDS cost a lot of time and money, so a short representative segment by CDS, which is envelope protein segment or non-structural 3 (NS3) segment is necessary. After sliding window is implemented, a better short segment than envelope protein segment and NS3 is found. This paper will discuss a mathematical method to analyze sequences using nested sliding window to find a short segment which is representative for the whole genome. The result shows that our method can find a short segment which more representative about 6.57% in topological view to CDS segment than an Envelope segment or NS3 segment.

  7. 3-D segmentation of retinal blood vessels in spectral-domain OCT volumes of the optic nerve head

    NASA Astrophysics Data System (ADS)

    Lee, Kyungmoo; Abràmoff, Michael D.; Niemeijer, Meindert; Garvin, Mona K.; Sonka, Milan

    2010-03-01

    Segmentation of retinal blood vessels can provide important information for detecting and tracking retinal vascular diseases including diabetic retinopathy, arterial hypertension, arteriosclerosis and retinopathy of prematurity (ROP). Many studies on 2-D segmentation of retinal blood vessels from a variety of medical images have been performed. However, 3-D segmentation of retinal blood vessels from spectral-domain optical coherence tomography (OCT) volumes, which is capable of providing geometrically accurate vessel models, to the best of our knowledge, has not been previously studied. The purpose of this study is to develop and evaluate a method that can automatically detect 3-D retinal blood vessels from spectral-domain OCT scans centered on the optic nerve head (ONH). The proposed method utilized a fast multiscale 3-D graph search to segment retinal surfaces as well as a triangular mesh-based 3-D graph search to detect retinal blood vessels. An experiment on 30 ONH-centered OCT scans (15 right eye scans and 15 left eye scans) from 15 subjects was performed, and the mean unsigned error in 3-D of the computer segmentations compared with the independent standard obtained from a retinal specialist was 3.4 +/- 2.5 voxels (0.10 +/- 0.07 mm).

  8. A comprehensive segmentation analysis of crude oil market based on time irreversibility

    NASA Astrophysics Data System (ADS)

    Xia, Jianan; Shang, Pengjian; Lu, Dan; Yin, Yi

    2016-05-01

    In this paper, we perform a comprehensive entropic segmentation analysis of crude oil future prices from 1983 to 2014 which used the Jensen-Shannon divergence as the statistical distance between segments, and analyze the results from original series S and series begin at 1986 (marked as S∗) to find common segments which have same boundaries. Then we apply time irreversibility analysis of each segment to divide all segments into two groups according to their asymmetry degree. Based on the temporal distribution of the common segments and high asymmetry segments, we figure out that these two types of segments appear alternately and do not overlap basically in daily group, while the common portions are also high asymmetry segments in weekly group. In addition, the temporal distribution of the common segments is fairly close to the time of crises, wars or other events, because the hit from severe events to oil price makes these common segments quite different from their adjacent segments. The common segments can be confirmed in daily group series, or weekly group series due to the large divergence between common segments and their neighbors. While the identification of high asymmetry segments is helpful to know the segments which are not affected badly by the events and can recover to steady states automatically. Finally, we rearrange the segments by merging the connected common segments or high asymmetry segments into a segment, and conjoin the connected segments which are neither common nor high asymmetric.

  9. Multi-temporal MRI carpal bone volumes analysis by principal axes registration

    NASA Astrophysics Data System (ADS)

    Ferretti, Roberta; Dellepiane, Silvana

    2016-03-01

    In this paper, a principal axes registration technique is presented, with the relevant application to segmented volumes. The purpose of the proposed registration is to compare multi-temporal volumes of carpal bones from Magnetic Resonance Imaging (MRI) acquisitions. Starting from the study of the second-order moment matrix, the eigenvectors are calculated to allow the rotation of volumes with respect to reference axes. Then the volumes are spatially translated to become perfectly overlapped. A quantitative evaluation of the results obtained is carried out by computing classical indices from the confusion matrix, which depict similarity measures between the volumes of the same organ as extracted from MRI acquisitions executed at different moments. Within the medical field, the way a registration can be used to compare multi-temporal images is of great interest, since it provides the physician with a tool which allows a visual monitoring of a disease evolution. The segmentation method used herein is based on the graph theory and is a robust, unsupervised and parameters independent method. Patients affected by rheumatic diseases have been considered.

  10. Microscopy image segmentation tool: Robust image data analysis

    NASA Astrophysics Data System (ADS)

    Valmianski, Ilya; Monton, Carlos; Schuller, Ivan K.

    2014-03-01

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

  11. Segmental analysis of renal glucose transport in young female rats.

    PubMed Central

    McSherry, N R; Wen, S F

    1984-01-01

    Free-flow micropuncture studies were performed on twenty-seven young female Sprague-Dawley rats before and after 10% extracellular volume expansion to evaluate glucose reabsorption at the accessible sites of both surface and papillary nephrons. In the distal nephron segments no significant glucose reabsorption was observed for the distal tubule and papillary collecting duct but significant difference in fractional glucose delivery was demonstrated between the bend of the Henle's loop and early distal tubule and between the late distal tubule and the base of the collecting duct. Comparison of the fractional glucose delivery within the same nephron group for both superficial and juxtamedullary nephrons indicated that glucose reabsorption occurred at some sites beyond the bend of the Henle's loop. Volume expansion inhibited glucose reabsorption in the proximal convoluted tubule, enhanced it in the segment between the late proximal and early distal tubules, but had no effect on glucose transport at further distal sites. It is concluded that, in addition to the proximal tubule, the ascending loop of Henle or cortical collecting tubule may play a role in maintaining glucose-free urine under physiological conditions. PMID:6394745

  12. Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging.

    PubMed

    Anbeek, Petronella; Vincken, Koen L; Groenendaal, Floris; Koeman, Annemieke; van Osch, Matthias J P; van der Grond, Jeroen

    2008-02-01

    A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of each tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (SI) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity >0.82 and specificity >0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.

  13. An automatic brain tumor segmentation tool.

    PubMed

    Diaz, Idanis; Boulanger, Pierre; Greiner, Russell; Hoehn, Bret; Rowe, Lindsay; Murtha, Albert

    2013-01-01

    This paper introduces an automatic brain tumor segmentation method (ABTS) for segmenting multiple components of brain tumor using four magnetic resonance image modalities. ABTS's four stages involve automatic histogram multi-thresholding and morphological operations including geodesic dilation. Our empirical results, on 16 real tumors, show that ABTS works very effectively, achieving a Dice accuracy compared to expert segmentation of 81% in segmenting edema and 85% in segmenting gross tumor volume (GTV).

  14. Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

    PubMed

    Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A

    2011-04-01

    Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Automatic and manual segmentation of healthy retinas using high-definition optical coherence tomography.

    PubMed

    Golbaz, Isabelle; Ahlers, Christian; Goesseringer, Nina; Stock, Geraldine; Geitzenauer, Wolfgang; Prünte, Christian; Schmidt-Erfurth, Ursula Margarethe

    2011-03-01

    This study compared automatic- and manual segmentation modalities in the retina of healthy eyes using high-definition optical coherence tomography (HD-OCT). Twenty retinas in 20 healthy individuals were examined using an HD-OCT system (Carl Zeiss Meditec, Inc.). Three-dimensional imaging was performed with an axial resolution of 6 μm at a maximum scanning speed of 25,000 A-scans/second. Volumes of 6 × 6 × 2 mm were scanned. Scans were analysed using a matlab-based algorithm and a manual segmentation software system (3D-Doctor). The volume values calculated by the two methods were compared. Statistical analysis revealed a high correlation between automatic and manual modes of segmentation. The automatic mode of measuring retinal volume and the corresponding three-dimensional images provided similar results to the manual segmentation procedure. Both methods were able to visualize retinal and subretinal features accurately. This study compared two methods of assessing retinal volume using HD-OCT scans in healthy retinas. Both methods were able to provide realistic volumetric data when applied to raster scan sets. Manual segmentation methods represent an adequate tool with which to control automated processes and to identify clinically relevant structures, whereas automatic procedures will be needed to obtain data in larger patient populations. © 2009 The Authors. Journal compilation © 2009 Acta Ophthalmol.

  16. Three-dimensional segmentation of pulmonary artery volume from thoracic computed tomography imaging

    NASA Astrophysics Data System (ADS)

    Lindenmaier, Tamas J.; Sheikh, Khadija; Bluemke, Emma; Gyacskov, Igor; Mura, Marco; Licskai, Christopher; Mielniczuk, Lisa; Fenster, Aaron; Cunningham, Ian A.; Parraga, Grace

    2015-03-01

    Chronic obstructive pulmonary disease (COPD), is a major contributor to hospitalization and healthcare costs in North America. While the hallmark of COPD is airflow limitation, it is also associated with abnormalities of the cardiovascular system. Enlargement of the pulmonary artery (PA) is a morphological marker of pulmonary hypertension, and was previously shown to predict acute exacerbations using a one-dimensional diameter measurement of the main PA. We hypothesized that a three-dimensional (3D) quantification of PA size would be more sensitive than 1D methods and encompass morphological changes along the entire central pulmonary artery. Hence, we developed a 3D measurement of the main (MPA), left (LPA) and right (RPA) pulmonary arteries as well as total PA volume (TPAV) from thoracic CT images. This approach incorporates segmentation of pulmonary vessels in cross-section for the MPA, LPA and RPA to provide an estimate of their volumes. Three observers performed five repeated measurements for 15 ex-smokers with ≥10 pack-years, and randomly identified from a larger dataset of 199 patients. There was a strong agreement (r2=0.76) for PA volume and PA diameter measurements, which was used as a gold standard. Observer measurements were strongly correlated and coefficients of variation for observer 1 (MPA:2%, LPA:3%, RPA:2%, TPA:2%) were not significantly different from observer 2 and 3 results. In conclusion, we generated manual 3D pulmonary artery volume measurements from thoracic CT images that can be performed with high reproducibility. Future work will involve automation for implementation in clinical workflows.

  17. Association of narrow angles with anterior chamber area and volume measured with anterior-segment optical coherence tomography.

    PubMed

    Wu, Ren-Yi; Nongpiur, Monisha E; He, Ming-Guang; Sakata, Lisandro M; Friedman, David S; Chan, Yiong-Huak; Lavanya, Raghavan; Wong, Tien-Yin; Aung, Tin

    2011-05-01

    To describe the measurement of anterior chamber area and anterior chamber volume by anterior-segment optical coherence tomography and to investigate the association of these parameters with the presence of narrow angles. This was a cross-sectional study of subjects aged at least 50 years without ophthalmic symptoms recruited from a community clinic. All participants underwent standardized ocular examination and anterior-segment optical coherence tomography. Customized software was used to measure anterior chamber area (cross-sectional area bounded by the corneal endothelium, anterior surface of iris, and lens within the pupil) and anterior chamber volume (calculated by rotating the anterior chamber area 360° around a vertical axis through the midpoint of the anterior chamber area). An eye was considered to have narrow angles if the posterior pigmented trabecular meshwork was not visible for at least 180° on gonioscopy with the eye in the primary position. A total of 1922 subjects were included in the final analyses, 317 (16.5%) of whom had narrow angles. Mean anterior chamber area (15.6 vs 21.1 mm(2); P < .001) and anterior chamber volume (97.6 vs 142.1 mm(3); P < .001) were smaller in eyes with narrow angles compared with those in eyes without narrow angles. After adjusting for age, sex, anterior chamber depth, axial length, and pupil size, smaller anterior chamber area (odds ratio, 53.2; 95% confidence interval, 27.1-104.5) and anterior chamber volume (odds ratio, 40.2; 95% confidence interval, 21.5-75.2) were significantly associated with the presence of narrow angles. Smaller anterior chamber area and anterior chamber volume were independently associated with narrow angles in Singaporeans, even after controlling for other known ocular risk factors.

  18. Identification and analysis the illegal dumping spot of solid waste at Ciliwung segment 5 riverbanks

    NASA Astrophysics Data System (ADS)

    Indrawati, D.; Purwaningrum, P.

    2018-01-01

    Ciliwung River is the main river in the area of Jakarta that is divided into six segments across West Java and Jakarta. The study focuses on the fifth segment which is 30 km long, covering from Kelapa Dua Depok to Manggarai, South Jakarta. The survey of the river consists of 3 sub-segments: Lenteng Agung, Pejaten Timur and Manggarai. Objectives of the study are to describe the characteristics and typology of the residential surrounding the Ciliwung Segment 5 Riverbank, to identification the illegal dumping spot of solid waste, to measure the volume and composition of solid waste in the riverbank, to decide solid waste management for residential area surrounding river banks to control the river pollution. The study shows that there are 11 illegal dumping spot of solid waste consisting of 4.37 m3 solid waste volume. The average composition of solid waste consists of 44% organic, 14% woods, 12% papers, 11% plastics, 3% rubbers, 1% metals and 2% others. To control the river pollution efforts are restoring the function of riverbanks to become green open space area, installing the trash rack into the river, to manage domestic solid waste based on 3R (Reduce, Reuse, Recycle) concept.

  19. NSEG: A segmented mission analysis program for low and high speed aircraft. Volume 2: Program users manual

    NASA Technical Reports Server (NTRS)

    Hague, D. S.; Rozendaal, H. L.

    1977-01-01

    A rapid mission analysis code based on the use of approximate flight path equations of motion is described. Equation form varies with the segment type, for example, accelerations, climbs, cruises, descents, and decelerations. Realistic and detailed vehicle characteristics are specified in tabular form. In addition to its mission performance calculation capabilities, the code also contains extensive flight envelop performance mapping capabilities. Approximate take off and landing analyses can be performed. At high speeds, centrifugal lift effects are taken into account. Extensive turbojet and ramjet engine scaling procedures are incorporated in the code.

  20. NOTE: Reducing the number of segments in unidirectional MLC segmentations

    NASA Astrophysics Data System (ADS)

    Mellado, X.; Cruz, S.; Artacho, J. M.; Canellas, M.

    2010-02-01

    In intensity-modulated radiation therapy (IMRT), fluence matrices obtained from a treatment planning system are usually delivered by a linear accelerator equipped with a multileaf collimator (MLC). A segmentation method is needed for decomposing these fluence matrices into segments suitable for the MLC, and the number of segments used is an important factor for treatment time. In this work, an algorithm for reduction of the number of segments (NS) is presented for unidirectional segmentations, where there is no backtracking of the MLC leaves. It uses a geometrical representation of the segmentation output for searching the key values in a fluence matrix that complicate its decomposition. The NS reduction is achieved by performing minor modifications in these values, under the conditions of avoiding substantial modifications of the dose-volume histogram, and does not increase in average the total number of monitor units delivered. The proposed method was tested using two clinical cases planned with the PCRT 3D® treatment planning system.

  1. Integrating segmentation methods from the Insight Toolkit into a visualization application.

    PubMed

    Martin, Ken; Ibáñez, Luis; Avila, Lisa; Barré, Sébastien; Kaspersen, Jon H

    2005-12-01

    The Insight Toolkit (ITK) initiative from the National Library of Medicine has provided a suite of state-of-the-art segmentation and registration algorithms ideally suited to volume visualization and analysis. A volume visualization application that effectively utilizes these algorithms provides many benefits: it allows access to ITK functionality for non-programmers, it creates a vehicle for sharing and comparing segmentation techniques, and it serves as a visual debugger for algorithm developers. This paper describes the integration of image processing functionalities provided by the ITK into VolView, a visualization application for high performance volume rendering. A free version of this visualization application is publicly available and is available in the online version of this paper. The process for developing ITK plugins for VolView according to the publicly available API is described in detail, and an application of ITK VolView plugins to the segmentation of Abdominal Aortic Aneurysms (AAAs) is presented. The source code of the ITK plugins is also publicly available and it is included in the online version.

  2. Low-dose dobutamine gated-SPECT analysis of left ventricular segmental wall thickening in ischemic cardiomyopathy.

    PubMed

    Candell-Riera, Jaume; Romero-Farina, Guillermo; Milá, Marta; Aguadé-Bruix, Santiago

    2008-10-01

    The objective of this study was to use low-dose dobutamine (LDD) gated single-photon emission computed tomography (SPECT) to evaluate segmental thickening of the left ventricle (LV) and its relationship with changes in ejection fraction (EF) and ventricular volumes in patients with ischemic cardiomyopathy. This prospective multicenter study involved 89 patients with ischemic cardiomyopathy (i.e., EF < or =40%) who underwent LDD gated-SPECT at rest. The LV was divided into 17 segments and systolic thickening was assessed in a total of 1513 segments during LDD infusion. RESULTS; A significant increase in LVEF (33.2% vs. 30.8%; P< .001) was observed during LDD infusion and occurred at the expense of a reduction in end-systolic volume (130.5 mL vs. 136.4 mL; P=.005). The increase in EF was > or =5% in 33.7% of patients, while the EF decreased by > or =5% in 5.6% of patients. With LDD infusion, both an improvement in > or =3 segments with severely decreased baseline thickening (odds ratio [OR] = 18.3; 95% confidence interval [CI], 5.3-63) and an improvement in > or =10 segments with mild-to-moderate alterations in baseline thickening (OR = 4.53; 95% CI, 1.26-16.16) were associated with a > or =5% increase in LVEF. During the assessment of global left ventricular contractile reserve by LDD gated-SPECT, attention should be paid not only to the behavior of segments with severely decreased baseline thickening, which are generally regarded as indicating viability, but also to segments with mild-to-moderate alterations and to those in which thickening decreases.

  3. Reproducibility of myelin content-based human habenula segmentation at 3 Tesla.

    PubMed

    Kim, Joo-Won; Naidich, Thomas P; Joseph, Joshmi; Nair, Divya; Glasser, Matthew F; O'halloran, Rafael; Doucet, Gaelle E; Lee, Won Hee; Krinsky, Hannah; Paulino, Alejandro; Glahn, David C; Anticevic, Alan; Frangou, Sophia; Xu, Junqian

    2018-03-26

    In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi-automated habenula segmentation methods have been reported, the test-retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra- and inter-site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7-0.8 mm isotropic resolution) using our previously proposed semi-automated myelin contrast-based method and its fully-automated version, as well as a previously published manual geometry-based method. The habenula segmentation using our semi-automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi-automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi-automated habenula segmentation showed reliable and reproducible habenula localization, while its fully-automated version offers an efficient way for large sample analysis. © 2018 Wiley Periodicals, Inc.

  4. Segmentation of Unstructured Datasets

    NASA Technical Reports Server (NTRS)

    Bhat, Smitha

    1996-01-01

    Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to be extremely large and complex. It is difficult to visualize these datasets using standard techniques like Volume Rendering and Ray Casting. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This thesis explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and from Finite Element Analysis.

  5. 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.

  6. 3D-segmentation of the 18F-choline PET signal for target volume definition in radiation therapy of the prostate.

    PubMed

    Ciernik, I Frank; Brown, Derek W; Schmid, Daniel; Hany, Thomas; Egli, Peter; Davis, J Bernard

    2007-02-01

    Volumetric assessment of PET signals becomes increasingly relevant for radiotherapy (RT) planning. Here, we investigate the utility of 18F-choline PET signals to serve as a structure for semi-automatic segmentation for forward treatment planning of prostate cancer. 18F-choline PET and CT scans of ten patients with histologically proven prostate cancer without extracapsular growth were acquired using a combined PET/CT scanner. Target volumes were manually delineated on CT images using standard software. Volumes were also obtained from 18F-choline PET images using an asymmetrical segmentation algorithm. PTVs were derived from CT 18F-choline PET based clinical target volumes (CTVs) by automatic expansion and comparative planning was performed. As a read-out for dose given to non-target structures, dose to the rectal wall was assessed. Planning target volumes (PTVs) derived from CT and 18F-choline PET yielded comparable results. Optimal matching of CT and 18F-choline PET derived volumes in the lateral and cranial-caudal directions was obtained using a background-subtracted signal thresholds of 23.0+/-2.6%. In antero-posterior direction, where adaptation compensating for rectal signal overflow was required, optimal matching was achieved with a threshold of 49.5+/-4.6%. 3D-conformal planning with CT or 18F-choline PET resulted in comparable doses to the rectal wall. Choline PET signals of the prostate provide adequate spatial information amendable to standardized asymmetrical region growing algorithms for PET-based target volume definition for external beam RT.

  7. Small rural hospitals: an example of market segmentation analysis.

    PubMed

    Mainous, A G; Shelby, R L

    1991-01-01

    In recent years, market segmentation analysis has shown increased popularity among health care marketers, although marketers tend to focus upon hospitals as sellers. The present analysis suggests that there is merit to viewing hospitals as a market of consumers. Employing a random sample of 741 small rural hospitals, the present investigation sought to determine, through the use of segmentation analysis, the variables associated with hospital success (occupancy). The results of a discriminant analysis yielded a model which classifies hospitals with a high degree of predictive accuracy. Successful hospitals have more beds and employees, and are generally larger and have more resources. However, there was no significant relationship between organizational success and number of services offered by the institution.

  8. Global grey matter volume in adult bipolar patients with and without lithium treatment: A meta-analysis.

    PubMed

    Sun, Yue Ran; Herrmann, Nathan; Scott, Christopher J M; Black, Sandra E; Khan, Maisha M; Lanctôt, Krista L

    2018-01-01

    The goal of this meta-analysis was to quantitatively summarize the evidence available on the differences in grey matter volume between lithium-treated and lithium-free bipolar patients. A systematic search was conducted in Cochrane Central, Embase, MEDLINE, and PsycINFO databases for original peer-reviewed journal articles that reported on global grey matter volume in lithium-medicated and lithium-free bipolar patients. Standard mean difference and Hedges' g were used to calculate effect size in a random-effects model. Risk of publication bias was assessed using Egger's test and quality of evidence was assessed using standard criteria. There were 15 studies with a total of 854 patients (368 lithium-medicated, 486 lithium-free) included in the meta-analysis. Global grey matter volume was significantly larger in lithium-treated bipolar patients compared to lithium-free patients (SMD: 0.17, 95% CI: 0.01-0.33; z = 2.11, p = 0.035). Additionally, there was a difference in global grey matter volume between groups in studies that employed semi-automated segmentation methods (SMD: 0.66, 95% CI: 0.01-1.31; z = 1.99, p = 0.047), but no significant difference in studies that used fully-automated segmentation. No publication bias was detected (bias coefficient = - 0.65, p = 0.46). Variability in imaging methods and lack of high-quality evidence limits the interpretation of the findings. Results suggest that lithium-treated patients have a greater global grey matter volume than those who were lithium-free. Further study of the relationship between lithium and grey matter volume may elucidate the therapeutic potential of lithium in conditions characterized by abnormal changes in brain structure. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  9. NSEG: A segmented mission analysis program for low and high speed aircraft. Volume 3: Demonstration problems

    NASA Technical Reports Server (NTRS)

    Hague, D. S.; Rozendaal, H. L.

    1977-01-01

    Program NSEG is a rapid mission analysis code based on the use of approximate flight path equations of motion. Equation form varies with the segment type, for example, accelerations, climbs, cruises, descents, and decelerations. Realistic and detailed vehicle characteristics are specified in tabular form. In addition to its mission performance calculation capabilities, the code also contains extensive flight envelope performance mapping capabilities. For example, rate-of-climb, turn rates, and energy maneuverability parameter values may be mapped in the Mach-altitude plane. Approximate take off and landing analyses are also performed. At high speeds, centrifugal lift effects are accounted for. Extensive turbojet and ramjet engine scaling procedures are incorporated in the code.

  10. Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

    NASA Astrophysics Data System (ADS)

    Velthuizen, Robert P.; Hall, Lawrence O.; Clarke, Laurence P.; Bensaid, Amine M.; Arrington, J. A.; Silbiger, Martin L.

    1993-07-01

    Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.

  11. Automatic segmentation of the puborectalis muscle in 3D transperineal ultrasound.

    PubMed

    van den Noort, Frieda; Grob, Anique T M; Slump, Cornelis H; van der Vaart, Carl H; van Stralen, Marijn

    2017-10-11

    The introduction of 3D analysis of the puborectalis muscle, for diagnostic purposes, into daily practice is hindered by the need for appropriate training of the observers. Automatic 3D segmentation of the puborectalis muscle in 3D transperineal ultrasound may aid to its adaption in clinical practice. A manual 3D segmentation protocol was developed to segment the puborectalis muscle. The data of 20 women, in their first trimester of pregnancy, was used to validate the reproducibility of this protocol. For automatic segmentation, active appearance models of the puborectalis muscle were developed. Those models were trained using manual segmentation data of 50 women. The performance of both manual and automatic segmentation was analyzed by measuring the overlap and distance between the segmentations. Also, the interclass correlation coefficients and their 95% confidence intervals were determined for mean echogenicity and volume of the puborectalis muscle. The ICC values of mean echogenicity (0.968-0.991) and volume (0.626-0.910) are good to very good for both automatic and manual segmentation. The results of overlap and distance for manual segmentation are as expected, showing only few pixels (2-3) mismatch on average and a reasonable overlap. Based on overlap and distance 5 mismatches in automatic segmentation were detected, resulting in an automatic segmentation a success rate of 90%. In conclusion, this study presents a reliable manual and automatic 3D segmentation of the puborectalis muscle. This will facilitate future investigation of the puborectalis muscle. It also allows for reliable measurements of clinically potentially valuable parameters like mean echogenicity. This article is protected by copyright. All rights reserved.

  12. Coronary artery analysis: Computer-assisted selection of best-quality segments in multiple-phase coronary CT angiography

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

    Zhou, Chuan, E-mail: chuan@umich.edu; Chan, Heang-

    Purpose: The authors are developing an automated method to identify the best-quality coronary arterial segment from multiple-phase coronary CT angiography (cCTA) acquisitions, which may be used by either interpreting physicians or computer-aided detection systems to optimally and efficiently utilize the diagnostic information available in multiple-phase cCTA for the detection of coronary artery disease. Methods: After initialization with a manually identified seed point, each coronary artery tree is automatically extracted from multiple cCTA phases using our multiscale coronary artery response enhancement and 3D rolling balloon region growing vessel segmentation and tracking method. The coronary artery trees from multiple phases are thenmore » aligned by a global registration using an affine transformation with quadratic terms and nonlinear simplex optimization, followed by a local registration using a cubic B-spline method with fast localized optimization. The corresponding coronary arteries among the available phases are identified using a recursive coronary segment matching method. Each of the identified vessel segments is transformed by the curved planar reformation (CPR) method. Four features are extracted from each corresponding segment as quality indicators in the original computed tomography volume and the straightened CPR volume, and each quality indicator is used as a voting classifier for the arterial segment. A weighted voting ensemble (WVE) classifier is designed to combine the votes of the four voting classifiers for each corresponding segment. The segment with the highest WVE vote is then selected as the best-quality segment. In this study, the training and test sets consisted of 6 and 20 cCTA cases, respectively, each with 6 phases, containing a total of 156 cCTA volumes and 312 coronary artery trees. An observer preference study was also conducted with one expert cardiothoracic radiologist and four nonradiologist readers to visually rank vessel

  13. Three-dimensional analysis of cervical spine segmental motion in rotation.

    PubMed

    Zhao, Xiong; Wu, Zi-Xiang; Han, Bao-Jun; Yan, Ya-Bo; Zhang, Yang; Lei, Wei

    2013-06-20

    The movements of the cervical spine during head rotation are too complicated to measure using conventional radiography or computed tomography (CT) techniques. In this study, we measure three-dimensional segmental motion of cervical spine rotation in vivo using a non-invasive measurement technique. Sixteen healthy volunteers underwent three-dimensional CT of the cervical spine during head rotation. Occiput (Oc) - T1 reconstructions were created of volunteers in each of 3 positions: supine and maximum left and right rotations of the head with respect to the bosom. Segmental motions were calculated using Euler angles and volume merge methods in three major planes. Mean maximum axial rotation of the cervical spine to one side was 1.6° to 38.5° at each level. Coupled lateral bending opposite to lateral bending was observed in the upper cervical levels, while in the subaxial cervical levels, it was observed in the same direction as axial rotation. Coupled extension was observed in the cervical levels of C5-T1, while coupled flexion was observed in the cervical levels of Oc-C5. The three-dimensional cervical segmental motions in rotation were accurately measured with the non-invasive measure. These findings will be helpful as the basis for understanding cervical spine movement in rotation and abnormal conditions. The presented data also provide baseline segmental motions for the design of prostheses for the cervical spine.

  14. Analysis and design of segment control system in segmented primary mirror

    NASA Astrophysics Data System (ADS)

    Yu, Wenhao; Li, Bin; Chen, Mo; Xian, Hao

    2017-10-01

    Segmented primary mirror will be adopted widely in giant telescopes in future, such as TMT, E-ELT and GMT. High-performance control technology of the segmented primary mirror is one of the difficult technologies for telescopes using segmented primary mirror. The control of each segment is the basis of control system in segmented mirror. Correcting the tilt and tip of single segment is the main work of this paper which is divided into two parts. Firstly, harmonic response done in finite element model of single segment matches the Bode diagram of a two-order system whose natural frequency is 45 hertz and damping ratio is 0.005. Secondly, a control system model is established, and speed feedback is introduced in control loop to suppress resonance point gain and increase the open-loop bandwidth, up to 30Hz or even higher. Corresponding controller is designed based on the control system model described above.

  15. A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine.

    PubMed

    Mastmeyer, André; Engelke, Klaus; Fuchs, Christina; Kalender, Willi A

    2006-08-01

    We have developed a new hierarchical 3D technique to segment the vertebral bodies in order to measure bone mineral density (BMD) with high trueness and precision in volumetric CT datasets. The hierarchical approach starts with a coarse separation of the individual vertebrae, applies a variety of techniques to segment the vertebral bodies with increasing detail and ends with the definition of an anatomic coordinate system for each vertebral body, relative to which up to 41 trabecular and cortical volumes of interest are positioned. In a pre-segmentation step constraints consisting of Boolean combinations of simple geometric shapes are determined that enclose each individual vertebral body. Bound by these constraints viscous deformable models are used to segment the main shape of the vertebral bodies. Volume growing and morphological operations then capture the fine details of the bone-soft tissue interface. In the volumes of interest bone mineral density and content are determined. In addition, in the segmented vertebral bodies geometric parameters such as volume or the length of the main axes of inertia can be measured. Intra- and inter-operator precision errors of the segmentation procedure were analyzed using existing clinical patient datasets. Results for segmented volume, BMD, and coordinate system position were below 2.0%, 0.6%, and 0.7%, respectively. Trueness was analyzed using phantom scans. The bias of the segmented volume was below 4%; for BMD it was below 1.5%. The long-term goal of this work is improved fracture prediction and patient monitoring in the field of osteoporosis. A true 3D segmentation also enables an accurate measurement of geometrical parameters that may augment the clinical value of a pure BMD analysis.

  16. Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

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

    Jurrus, Elizabeth R.; Watanabe, Shigeki; Giuly, Richard J.

    2013-01-01

    Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated processmore » first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.« less

  17. Semi-automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

    PubMed Central

    Jurrus, Elizabeth; Watanabe, Shigeki; Giuly, Richard J.; Paiva, Antonio R. C.; Ellisman, Mark H.; Jorgensen, Erik M.; Tasdizen, Tolga

    2013-01-01

    Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes. PMID:22644867

  18. Combining multi-atlas segmentation with brain surface estimation

    NASA Astrophysics Data System (ADS)

    Huo, Yuankai; Carass, Aaron; Resnick, Susan M.; Pham, Dzung L.; Prince, Jerry L.; Landman, Bennett A.

    2016-03-01

    Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitation in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

  19. Combining Multi-atlas Segmentation with Brain Surface Estimation.

    PubMed

    Huo, Yuankai; Carass, Aaron; Resnick, Susan M; Pham, Dzung L; Prince, Jerry L; Landman, Bennett A

    2016-02-27

    Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitations in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

  20. Automatic segmentation of solitary pulmonary nodules based on local intensity structure analysis and 3D neighborhood features in 3D chest CT images

    NASA Astrophysics Data System (ADS)

    Chen, Bin; Kitasaka, Takayuki; Honma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Mori, Kensaku

    2012-03-01

    This paper presents a solitary pulmonary nodule (SPN) segmentation method based on local intensity structure analysis and neighborhood feature analysis in chest CT images. Automated segmentation of SPNs is desirable for a chest computer-aided detection/diagnosis (CAS) system since a SPN may indicate early stage of lung cancer. Due to the similar intensities of SPNs and other chest structures such as blood vessels, many false positives (FPs) are generated by nodule detection methods. To reduce such FPs, we introduce two features that analyze the relation between each segmented nodule candidate and it neighborhood region. The proposed method utilizes a blob-like structure enhancement (BSE) filter based on Hessian analysis to augment the blob-like structures as initial nodule candidates. Then a fine segmentation is performed to segment much more accurate region of each nodule candidate. FP reduction is mainly addressed by investigating two neighborhood features based on volume ratio and eigenvector of Hessian that are calculates from the neighborhood region of each nodule candidate. We evaluated the proposed method by using 40 chest CT images, include 20 standard-dose CT images that we randomly chosen from a local database and 20 low-dose CT images that were randomly chosen from a public database: LIDC. The experimental results revealed that the average TP rate of proposed method was 93.6% with 12.3 FPs/case.

  1. Quantification of regional fat volume in rat MRI

    NASA Astrophysics Data System (ADS)

    Sacha, Jaroslaw P.; Cockman, Michael D.; Dufresne, Thomas E.; Trokhan, Darren

    2003-05-01

    Multiple initiatives in the pharmaceutical and beauty care industries are directed at identifying therapies for weight management. Body composition measurements are critical for such initiatives. Imaging technologies that can be used to measure body composition noninvasively include DXA (dual energy x-ray absorptiometry) and MRI (magnetic resonance imaging). Unlike other approaches, MRI provides the ability to perform localized measurements of fat distribution. Several factors complicate the automatic delineation of fat regions and quantification of fat volumes. These include motion artifacts, field non-uniformity, brightness and contrast variations, chemical shift misregistration, and ambiguity in delineating anatomical structures. We have developed an approach to deal practically with those challenges. The approach is implemented in a package, the Fat Volume Tool, for automatic detection of fat tissue in MR images of the rat abdomen, including automatic discrimination between abdominal and subcutaneous regions. We suppress motion artifacts using masking based on detection of implicit landmarks in the images. Adaptive object extraction is used to compensate for intensity variations. This approach enables us to perform fat tissue detection and quantification in a fully automated manner. The package can also operate in manual mode, which can be used for verification of the automatic analysis or for performing supervised segmentation. In supervised segmentation, the operator has the ability to interact with the automatic segmentation procedures to touch-up or completely overwrite intermediate segmentation steps. The operator's interventions steer the automatic segmentation steps that follow. This improves the efficiency and quality of the final segmentation. Semi-automatic segmentation tools (interactive region growing, live-wire, etc.) improve both the accuracy and throughput of the operator when working in manual mode. The quality of automatic segmentation has been

  2. Automated identification of best-quality coronary artery segments from multiple-phase coronary CT angiography (cCTA) for vessel analysis

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Chughtai, Aamer; Wei, Jun; Kazerooni, Ella A.

    2016-03-01

    We are developing an automated method to identify the best quality segment among the corresponding segments in multiple-phase cCTA. The coronary artery trees are automatically extracted from different cCTA phases using our multi-scale vessel segmentation and tracking method. An automated registration method is then used to align the multiple-phase artery trees. The corresponding coronary artery segments are identified in the registered vessel trees and are straightened by curved planar reformation (CPR). Four features are extracted from each segment in each phase as quality indicators in the original CT volume and the straightened CPR volume. Each quality indicator is used as a voting classifier to vote the corresponding segments. A newly designed weighted voting ensemble (WVE) classifier is finally used to determine the best-quality coronary segment. An observer preference study is conducted with three readers to visually rate the quality of the vessels in 1 to 6 rankings. Six and 10 cCTA cases are used as training and test set in this preliminary study. For the 10 test cases, the agreement between automatically identified best-quality (AI-BQ) segments and radiologist's top 2 rankings is 79.7%, and between AI-BQ and the other two readers are 74.8% and 83.7%, respectively. The results demonstrated that the performance of our automated method was comparable to those of experienced readers for identification of the best-quality coronary segments.

  3. The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

    NASA Technical Reports Server (NTRS)

    Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

    2008-01-01

    Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

  4. Evaluation of right ventricular function by coronary computed tomography angiography using a novel automated 3D right ventricle volume segmentation approach: a validation study.

    PubMed

    Burghard, Philipp; Plank, Fabian; Beyer, Christoph; Müller, Silvana; Dörler, Jakob; Zaruba, Marc-Michael; Pölzl, Leo; Pölzl, Gerhard; Klauser, Andrea; Rauch, Stefan; Barbieri, Fabian; Langer, Christian-Ekkehardt; Schgoer, Wilfried; Williamson, Eric E; Feuchtner, Gudrun

    2018-06-04

    To evaluate right ventricle (RV) function by coronary computed tomography angiography (CTA) using a novel automated three-dimensional (3D) RV volume segmentation tool in comparison with clinical reference modalities. Twenty-six patients with severe end-stage heart failure [left ventricle (LV) ejection fraction (EF) <35%] referred to CTA were enrolled. A specific individually tailored biphasic contrast agent injection protocol was designed (80%/20% high/low flow) was designed. Measurement of RV function [EF, end-diastolic volume (EDV), end-systolic volume (ESV)] by CTA was compared with tricuspid annular plane systolic excursion (TAPSE) by transthoracic echocardiography (TTE) and right heart invasive catheterisation (IC). Automated 3D RV volume segmentation was successful in 26 (100%) patients. Read-out time was 3 min 33 s (range, 1 min 50s-4 min 33s). RV EF by CTA was stronger correlated with right atrial pressure (RAP) by IC (r = -0.595; p = 0.006) but weaker with TAPSE (r = 0.366, p = 0.94). When comparing TAPSE with RAP by IC (r = -0.317, p = 0.231), a weak-to-moderate non-significant inverse correlation was found. Interobserver correlation was high with r = 0.96 (p < 0.001), r = 0.86 (p < 0.001) and r = 0.72 (p = 0.001) for RV EDV, ESV and EF, respectively. CT attenuation of the right atrium (RA) and right ventricle (RV) was 196.9 ± 75.3 and 217.5 ± 76.1 HU, respectively. Measurement of RV function by CTA using a novel 3D volumetric segmentation tool is fast and reliable by applying a dedicated biphasic injection protocol. The RV EF from CTA is a closer surrogate of RAP than TAPSE by TTE. • Evaluation of RV function by cardiac CTA by using a novel 3D volume segmentation tool is fast and reliable. • A biphasic contrast agent injection protocol ensures homogenous RV contrast attenuation. • Cardiac CT is a valuable alternative modality to CMR for the evaluation of RV function.

  5. A retrospective analysis of laparoscopic partial nephrectomy with segmental renal artery clamping and factors that predict postoperative renal function.

    PubMed

    Li, Pu; Qin, Chao; Cao, Qiang; Li, Jie; Lv, Qiang; Meng, Xiaoxin; Ju, Xiaobing; Tang, Lijun; Shao, Pengfei

    2016-10-01

    To evaluate the feasibility and efficiency of laparoscopic partial nephrectomy (LPN) with segmental renal artery clamping, and to analyse the factors affecting postoperative renal function. We conducted a retrospective analysis of 466 consecutive patients undergoing LPN using main renal artery clamping (group A, n = 152) or segmental artery clamping (group B, n = 314) between September 2007 and July 2015 in our department. Blood loss, operating time, warm ischaemia time (WIT) and renal function were compared between groups. Univariable and multivariable linear regression analyses were applied to assess the correlations of selected variables with postoperative glomerular filtration rate (GFR) reduction. Volumetric data and estimated GFR of a subset of 60 patients in group B were compared with GFR to evaluate the correlation between these functional variables and preserved renal function after LPN. The novel technique slightly increased operating time, WIT and intra-operative blood loss (P < 0.001), while it provided better postoperative renal function (P < 0.001) compared with the conventional technique. The blocking method and tumour characteristics were independent factors affecting GFR reduction, while WIT was not an independent factor. Correlation analysis showed that estimated GFR presented better correlation with GFR compared with kidney volume (R(2) = 0.794 cf. R(2) = 0.199) in predicting renal function after LPN. LPN with segmental artery clamping minimizes warm ischaemia injury and provides better early postoperative renal function compared with clamping the main renal artery. Kidney volume has a significantly inferior role compared with eGFR in predicting preserved renal function. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  6. Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

    PubMed

    Ren, Xuhua; Xiang, Lei; Nie, Dong; Shao, Yeqin; Zhang, Huan; Shen, Dinggang; Wang, Qian

    2018-05-01

    Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automatic segmentation provides an alternative solution, which, however, is often difficult for small tissues (i.e., chiasm and optic nerves in head and neck CT images) because of their small volumes and highly diverse appearance/shape information. In this work, we propose to interleave multiple 3D Convolutional Neural Networks (3D-CNNs) to attain automatic segmentation of small tissues in head and neck CT images. A 3D-CNN was designed to segment each structure of interest. To make full use of the image appearance information, multiscale patches are extracted to describe the center voxel under consideration and then input to the CNN architecture. Next, as neighboring tissues are often highly related in the physiological and anatomical perspectives, we interleave the CNNs designated for the individual tissues. In this way, the tentative segmentation result of a specific tissue can contribute to refine the segmentations of other neighboring tissues. Finally, as more CNNs are interleaved and cascaded, a complex network of CNNs can be derived, such that all tissues can be jointly segmented and iteratively refined. Our method was validated on a set of 48 CT images, obtained from the Medical Image Computing and Computer Assisted Intervention (MICCAI) Challenge 2015. The Dice coefficient (DC) and the 95% Hausdorff Distance (95HD) are computed to measure the accuracy of the segmentation results. The proposed method achieves higher segmentation accuracy (with the average DC: 0.58 ± 0.17 for optic chiasm, and 0.71 ± 0.08 for optic nerve; 95HD: 2.81 ± 1.56 mm for optic chiasm, and 2.23 ± 0.90 mm for optic nerve) than the MICCAI challenge winner (with the average DC: 0.38 for optic chiasm, and 0.68 for optic nerve; 95HD: 3.48 for

  7. Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

    NASA Astrophysics Data System (ADS)

    Neubert, A.; Fripp, J.; Engstrom, C.; Schwarz, R.; Lauer, L.; Salvado, O.; Crozier, S.

    2012-12-01

    Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.

  8. Multiclassifier fusion in human brain MR segmentation: modelling convergence.

    PubMed

    Heckemann, Rolf A; Hajnal, Joseph V; Aljabar, Paul; Rueckert, Daniel; Hammers, Alexander

    2006-01-01

    Segmentations of MR images of the human brain can be generated by propagating an existing atlas label volume to the target image. By fusing multiple propagated label volumes, the segmentation can be improved. We developed a model that predicts the improvement of labelling accuracy and precision based on the number of segmentations used as input. Using a cross-validation study on brain image data as well as numerical simulations, we verified the model. Fit parameters of this model are potential indicators of the quality of a given label propagation method or the consistency of the input segmentations used.

  9. Lung tumor segmentation in PET images using graph cuts.

    PubMed

    Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Feng, David Dagan

    2013-03-01

    The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  10. Assessing Variability in Brain Tumor Segmentation to Improve Volumetric Accuracy and Characterization of Change.

    PubMed

    Rios Piedra, Edgar A; Taira, Ricky K; El-Saden, Suzie; Ellingson, Benjamin M; Bui, Alex A T; Hsu, William

    2016-02-01

    Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), providing a more precise description of disease progression to better inform clinical decision-making and treatment planning. While a multitude of segmentation approaches exist, inherent variability in the results of these algorithms may incorrectly indicate changes in tumor volume. In this work, we present a systematic approach to characterize variability in tumor boundaries that utilizes equivalence tests as a means to determine whether a tumor volume has significantly changed over time. To demonstrate these concepts, 32 MRI studies from 8 patients were segmented using four different approaches (statistical classifier, region-based, edge-based, knowledge-based) to generate different regions of interest representing tumor extent. We showed that across all studies, the average Dice coefficient for the superset of the different methods was 0.754 (95% confidence interval 0.701-0.808) when compared to a reference standard. We illustrate how variability obtained by different segmentations can be used to identify significant changes in tumor volume between sequential time points. Our study demonstrates that variability is an inherent part of interpreting tumor segmentation results and should be considered as part of the interpretation process.

  11. Microfluidic device and method for focusing, segmenting, and dispensing of a fluid stream

    DOEpatents

    Jacobson, Stephen C [Knoxville, TN; Ramsey, J Michael [Knoxville, TN

    2008-09-09

    A microfluidic device and method for forming and dispensing minute volume segments of a material are described. In accordance with the present invention, a microfluidic device and method are provided for spatially confining the material in a focusing element. The device is also adapted for segmenting the confined material into minute volume segments, and dispensing a volume segment to a waste or collection channel. The device further includes means for driving the respective streams of sample and focusing fluids through respective channels into a chamber, such that the focusing fluid streams spatially confine the sample material. The device may also include additional means for driving a minute volume segment of the spatially confined sample material into a collection channel in fluid communication with the waste reservoir.

  12. A new method of cardiographic image segmentation based on grammar

    NASA Astrophysics Data System (ADS)

    Hamdi, Salah; Ben Abdallah, Asma; Bedoui, Mohamed H.; Alimi, Adel M.

    2011-10-01

    The measurement of the most common ultrasound parameters, such as aortic area, mitral area and left ventricle (LV) volume, requires the delineation of the organ in order to estimate the area. In terms of medical image processing this translates into the need to segment the image and define the contours as accurately as possible. The aim of this work is to segment an image and make an automated area estimation based on grammar. The entity "language" will be projected to the entity "image" to perform structural analysis and parsing of the image. We will show how the idea of segmentation and grammar-based area estimation is applied to real problems of cardio-graphic image processing.

  13. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

    PubMed

    Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

    2017-07-01

    Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis.

    PubMed

    Lee, Hyunkwang; Troschel, Fabian M; Tajmir, Shahein; Fuchs, Georg; Mario, Julia; Fintelmann, Florian J; Do, Synho

    2017-08-01

    Pretreatment risk stratification is key for personalized medicine. While many physicians rely on an "eyeball test" to assess whether patients will tolerate major surgery or chemotherapy, "eyeballing" is inherently subjective and difficult to quantify. The concept of morphometric age derived from cross-sectional imaging has been found to correlate well with outcomes such as length of stay, morbidity, and mortality. However, the determination of the morphometric age is time intensive and requires highly trained experts. In this study, we propose a fully automated deep learning system for the segmentation of skeletal muscle cross-sectional area (CSA) on an axial computed tomography image taken at the third lumbar vertebra. We utilized a fully automated deep segmentation model derived from an extended implementation of a fully convolutional network with weight initialization of an ImageNet pre-trained model, followed by post processing to eliminate intramuscular fat for a more accurate analysis. This experiment was conducted by varying window level (WL), window width (WW), and bit resolutions in order to better understand the effects of the parameters on the model performance. Our best model, fine-tuned on 250 training images and ground truth labels, achieves 0.93 ± 0.02 Dice similarity coefficient (DSC) and 3.68 ± 2.29% difference between predicted and ground truth muscle CSA on 150 held-out test cases. Ultimately, the fully automated segmentation system can be embedded into the clinical environment to accelerate the quantification of muscle and expanded to volume analysis of 3D datasets.

  15. 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.

  16. Design and validation of Segment--freely available software for cardiovascular image analysis.

    PubMed

    Heiberg, Einar; Sjögren, Jane; Ugander, Martin; Carlsson, Marcus; Engblom, Henrik; Arheden, Håkan

    2010-01-11

    Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page http://segment.heiberg.se. Segment is

  17. Segmentation-free image processing and analysis of precipitate shapes in 2D and 3D

    NASA Astrophysics Data System (ADS)

    Bales, Ben; Pollock, Tresa; Petzold, Linda

    2017-06-01

    Segmentation based image analysis techniques are routinely employed for quantitative analysis of complex microstructures containing two or more phases. The primary advantage of these approaches is that spatial information on the distribution of phases is retained, enabling subjective judgements of the quality of the segmentation and subsequent analysis process. The downside is that computing micrograph segmentations with data from morphologically complex microstructures gathered with error-prone detectors is challenging and, if no special care is taken, the artifacts of the segmentation will make any subsequent analysis and conclusions uncertain. In this paper we demonstrate, using a two phase nickel-base superalloy microstructure as a model system, a new methodology for analysis of precipitate shapes using a segmentation-free approach based on the histogram of oriented gradients feature descriptor, a classic tool in image analysis. The benefits of this methodology for analysis of microstructure in two and three-dimensions are demonstrated.

  18. SU-E-J-238: Monitoring Lymph Node Volumes During Radiotherapy Using Semi-Automatic Segmentation of MRI Images

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

    Veeraraghavan, H; Tyagi, N; Riaz, N

    2014-06-01

    Purpose: Identification and image-based monitoring of lymph nodes growing due to disease, could be an attractive alternative to prophylactic head and neck irradiation. We evaluated the accuracy of the user-interactive Grow Cut algorithm for volumetric segmentation of radiotherapy relevant lymph nodes from MRI taken weekly during radiotherapy. Method: The algorithm employs user drawn strokes in the image to volumetrically segment multiple structures of interest. We used a 3D T2-wturbo spin echo images with an isotropic resolution of 1 mm3 and FOV of 492×492×300 mm3 of head and neck cancer patients who underwent weekly MR imaging during the course of radiotherapy.more » Various lymph node (LN) levels (N2, N3, N4'5) were individually contoured on the weekly MR images by an expert physician and used as ground truth in our study. The segmentation results were compared with the physician drawn lymph nodes based on DICE similarity score. Results: Three head and neck patients with 6 weekly MR images were evaluated. Two patients had level 2 LN drawn and one patient had level N2, N3 and N4'5 drawn on each MR image. The algorithm took an average of a minute to segment the entire volume (512×512×300 mm3). The algorithm achieved an overall DICE similarity score of 0.78. The time taken for initializing and obtaining the volumetric mask was about 5 mins for cases with only N2 LN and about 15 mins for the case with N2,N3 and N4'5 level nodes. The longer initialization time for the latter case was due to the need for accurate user inputs to separate overlapping portions of the different LN. The standard deviation in segmentation accuracy at different time points was utmost 0.05. Conclusions: Our initial evaluation of the grow cut segmentation shows reasonably accurate and consistent volumetric segmentations of LN with minimal user effort and time.« less

  19. MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes.

    PubMed

    Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

    2014-03-21

    Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

  20. MRI brain tumor segmentation and necrosis detection using adaptive Sobolev snakes

    NASA Astrophysics Data System (ADS)

    Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

    2014-03-01

    Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at di erent points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D di usion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

  1. Integrated segmentation of cellular structures

    NASA Astrophysics Data System (ADS)

    Ajemba, Peter; Al-Kofahi, Yousef; Scott, Richard; Donovan, Michael; Fernandez, Gerardo

    2011-03-01

    Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.

  2. Automated segmentation of chronic stroke lesions using LINDA: Lesion Identification with Neighborhood Data Analysis

    PubMed Central

    Pustina, Dorian; Coslett, H. Branch; Turkeltaub, Peter E.; Tustison, Nicholas; Schwartz, Myrna F.; Avants, Brian

    2015-01-01

    The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1-weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k-fold and leave-one-out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696±0.16, Hausdorff distance of 17.9±9.8mm, and average displacement of 2.54±1.38mm. The manual and predicted lesion volumes correlated at r=0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross-institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion-to-symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro-cognitive maps, albeit with some discussed discrepancies. Of note, region-wise LSM was more robust to the prediction error than voxel-wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state-of-the-art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain-behavior relationships. LINDA is made available online with trained models from over 100 patients. PMID:26756101

  3. Microfluidic device and method for focusing, segmenting, and dispensing of a fluid stream

    DOEpatents

    Jacobson, Stephen C.; Ramsey, J. Michael

    2004-09-14

    A microfluidic device for forming and/or dispensing minute volume segments of a material is described. In accordance with one aspect of the present invention, a microfluidic device and method is provided for spatially confining the material in a focusing element. The device is also capable of segmenting the confined material into minute volume segments, and dispensing a volume segment to a waste or collection channel. The device further includes means for driving the respective streams of sample and focusing fluids through respective channels into a chamber, such that the focusing fluid streams spatially confine the sample material. The device may also include additional means for driving a minute volume segment of the spatially confined sample material into a collection channel in fluid communication with the waste reservoir.

  4. Boundary fitting based segmentation of fluorescence microscopy images

    NASA Astrophysics Data System (ADS)

    Lee, Soonam; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

    2015-03-01

    Segmentation is a fundamental step in quantifying characteristics, such as volume, shape, and orientation of cells and/or tissue. However, quantification of these characteristics still poses a challenge due to the unique properties of microscopy volumes. This paper proposes a 2D segmentation method that utilizes a combination of adaptive and global thresholding, potentials, z direction refinement, branch pruning, end point matching, and boundary fitting methods to delineate tubular objects in microscopy volumes. Experimental results demonstrate that the proposed method achieves better performance than an active contours based scheme.

  5. A segmentation/clustering model for the analysis of array CGH data.

    PubMed

    Picard, F; Robin, S; Lebarbier, E; Daudin, J-J

    2007-09-01

    Microarray-CGH (comparative genomic hybridization) experiments are used to detect and map chromosomal imbalances. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not provide a biological status for the detected segments. We propose a new model for this segmentation/clustering problem, combining a segmentation model with a mixture model. We present a new hybrid algorithm called dynamic programming-expectation maximization (DP-EM) to estimate the parameters of the model by maximum likelihood. This algorithm combines DP and the EM algorithm. We also propose a model selection heuristic to select the number of clusters and the number of segments. An example of our procedure is presented, based on publicly available data sets. We compare our method to segmentation methods and to hidden Markov models, and we show that the new segmentation/clustering model is a promising alternative that can be applied in the more general context of signal processing.

  6. Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from CT images.

    PubMed

    Tingelhoff, K; Moral, A I; Kunkel, M E; Rilk, M; Wagner, I; Eichhorn, K G; Wahl, F M; Bootz, F

    2007-01-01

    Segmentation of medical image data is getting more and more important over the last years. The results are used for diagnosis, surgical planning or workspace definition of robot-assisted systems. The purpose of this paper is to find out whether manual or semi-automatic segmentation is adequate for ENT surgical workflow or whether fully automatic segmentation of paranasal sinuses and nasal cavity is needed. We present a comparison of manual and semi-automatic segmentation of paranasal sinuses and the nasal cavity. Manual segmentation is performed by custom software whereas semi-automatic segmentation is realized by a commercial product (Amira). For this study we used a CT dataset of the paranasal sinuses which consists of 98 transversal slices, each 1.0 mm thick, with a resolution of 512 x 512 pixels. For the analysis of both segmentation procedures we used volume, extension (width, length and height), segmentation time and 3D-reconstruction. The segmentation time was reduced from 960 minutes with manual to 215 minutes with semi-automatic segmentation. We found highest variances segmenting nasal cavity. For the paranasal sinuses manual and semi-automatic volume differences are not significant. Dependent on the segmentation accuracy both approaches deliver useful results and could be used for e.g. robot-assisted systems. Nevertheless both procedures are not useful for everyday surgical workflow, because they take too much time. Fully automatic and reproducible segmentation algorithms are needed for segmentation of paranasal sinuses and nasal cavity.

  7. Vesselness propagation: a fast interactive vessel segmentation method

    NASA Astrophysics Data System (ADS)

    Cai, Wenli; Dachille, Frank; Harris, Gordon J.; Yoshida, Hiroyuki

    2006-03-01

    With the rapid development of multi-detector computed tomography (MDCT), resulting in increasing temporal and spatial resolution of data sets, clinical use of computed tomographic angiography (CTA) is rapidly increasing. Analysis of vascular structures is much needed in CTA images; however, the basis of the analysis, vessel segmentation, can still be a challenging problem. In this paper, we present a fast interactive method for CTA vessel segmentation, called vesselness propagation. This method is a two-step procedure, with a pre-processing step and an interactive step. During the pre-processing step, a vesselness volume is computed by application of a CTA transfer function followed by a multi-scale Hessian filtering. At the interactive stage, the propagation is controlled interactively in terms of the priority of the vesselness. This method was used successfully in many CTA applications such as the carotid artery, coronary artery, and peripheral arteries. It takes less than one minute for a user to segment the entire vascular structure. Thus, the proposed method provides an effective way of obtaining an overview of vascular structures.

  8. Fast approximation for joint optimization of segmentation, shape, and location priors, and its application in gallbladder segmentation.

    PubMed

    Saito, Atsushi; Nawano, Shigeru; Shimizu, Akinobu

    2017-05-01

    This paper addresses joint optimization for segmentation and shape priors, including translation, to overcome inter-subject variability in the location of an organ. Because a simple extension of the previous exact optimization method is too computationally complex, we propose a fast approximation for optimization. The effectiveness of the proposed approximation is validated in the context of gallbladder segmentation from a non-contrast computed tomography (CT) volume. After spatial standardization and estimation of the posterior probability of the target organ, simultaneous optimization of the segmentation, shape, and location priors is performed using a branch-and-bound method. Fast approximation is achieved by combining sampling in the eigenshape space to reduce the number of shape priors and an efficient computational technique for evaluating the lower bound. Performance was evaluated using threefold cross-validation of 27 CT volumes. Optimization in terms of translation of the shape prior significantly improved segmentation performance. The proposed method achieved a result of 0.623 on the Jaccard index in gallbladder segmentation, which is comparable to that of state-of-the-art methods. The computational efficiency of the algorithm is confirmed to be good enough to allow execution on a personal computer. Joint optimization of the segmentation, shape, and location priors was proposed, and it proved to be effective in gallbladder segmentation with high computational efficiency.

  9. Computerized tongue image segmentation via the double geo-vector flow

    PubMed Central

    2014-01-01

    Background Visual inspection for tongue analysis is a diagnostic method in traditional Chinese medicine (TCM). Owing to the variations in tongue features, such as color, texture, coating, and shape, it is difficult to precisely extract the tongue region in images. This study aims to quantitatively evaluate tongue diagnosis via automatic tongue segmentation. Methods Experiments were conducted using a clinical image dataset provided by the Laboratory of Traditional Medical Syndromes, Shanghai University of TCM. First, a clinical tongue image was refined by a saliency window. Second, we initialized the tongue area as the upper binary part and lower level set matrix. Third, a double geo-vector flow (DGF) was proposed to detect the tongue edge and segment the tongue region in the image, such that the geodesic flow was evaluated in the lower part, and the geo-gradient vector flow was evaluated in the upper part. Results The performance of the DGF was evaluated using 100 images. The DGF exhibited better results compared with other representative studies, with its true-positive volume fraction reaching 98.5%, its false-positive volume fraction being 1.51%, and its false-negative volume fraction being 1.42%. The errors between the proposed automatic segmentation results and manual contours were 0.29 and 1.43% in terms of the standard boundary error metrics of Hausdorff distance and mean distance, respectively. Conclusions By analyzing the time complexity of the DGF and evaluating its performance via standard boundary and area error metrics, we have shown both efficiency and effectiveness of the DGF for automatic tongue image segmentation. PMID:24507094

  10. Computerized tongue image segmentation via the double geo-vector flow.

    PubMed

    Shi, Miao-Jing; Li, Guo-Zheng; Li, Fu-Feng; Xu, Chao

    2014-02-08

    Visual inspection for tongue analysis is a diagnostic method in traditional Chinese medicine (TCM). Owing to the variations in tongue features, such as color, texture, coating, and shape, it is difficult to precisely extract the tongue region in images. This study aims to quantitatively evaluate tongue diagnosis via automatic tongue segmentation. Experiments were conducted using a clinical image dataset provided by the Laboratory of Traditional Medical Syndromes, Shanghai University of TCM. First, a clinical tongue image was refined by a saliency window. Second, we initialized the tongue area as the upper binary part and lower level set matrix. Third, a double geo-vector flow (DGF) was proposed to detect the tongue edge and segment the tongue region in the image, such that the geodesic flow was evaluated in the lower part, and the geo-gradient vector flow was evaluated in the upper part. The performance of the DGF was evaluated using 100 images. The DGF exhibited better results compared with other representative studies, with its true-positive volume fraction reaching 98.5%, its false-positive volume fraction being 1.51%, and its false-negative volume fraction being 1.42%. The errors between the proposed automatic segmentation results and manual contours were 0.29 and 1.43% in terms of the standard boundary error metrics of Hausdorff distance and mean distance, respectively. By analyzing the time complexity of the DGF and evaluating its performance via standard boundary and area error metrics, we have shown both efficiency and effectiveness of the DGF for automatic tongue image segmentation.

  11. Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.

    PubMed

    Rios Velazquez, Emmanuel; Meier, Raphael; Dunn, William D; Alexander, Brian; Wiest, Roland; Bauer, Stefan; Gutman, David A; Reyes, Mauricio; Aerts, Hugo J W L

    2015-11-18

    Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.

  12. A Comparison of Two Commercial Volumetry Software Programs in the Analysis of Pulmonary Ground-Glass Nodules: Segmentation Capability and Measurement Accuracy

    PubMed Central

    Kim, Hyungjin; Lee, Sang Min; Lee, Hyun-Ju; Goo, Jin Mo

    2013-01-01

    Objective To compare the segmentation capability of the 2 currently available commercial volumetry software programs with specific segmentation algorithms for pulmonary ground-glass nodules (GGNs) and to assess their measurement accuracy. Materials and Methods In this study, 55 patients with 66 GGNs underwent unenhanced low-dose CT. GGN segmentation was performed by using 2 volumetry software programs (LungCARE, Siemens Healthcare; LungVCAR, GE Healthcare). Successful nodule segmentation was assessed visually and morphologic features of GGNs were evaluated to determine factors affecting segmentation by both types of software. In addition, the measurement accuracy of the software programs was investigated by using an anthropomorphic chest phantom containing simulated GGNs. Results The successful nodule segmentation rate was significantly higher in LungCARE (90.9%) than in LungVCAR (72.7%) (p = 0.012). Vascular attachment was a negatively influencing morphologic feature of nodule segmentation for both software programs. As for measurement accuracy, mean relative volume measurement errors in nodules ≥ 10 mm were 14.89% with LungCARE and 19.96% with LungVCAR. The mean relative attenuation measurement errors in nodules ≥ 10 mm were 3.03% with LungCARE and 5.12% with LungVCAR. Conclusion LungCARE shows significantly higher segmentation success rates than LungVCAR. Measurement accuracy of volume and attenuation of GGNs is acceptable in GGNs ≥ 10 mm by both software programs. PMID:23901328

  13. General Staining and Segmentation Procedures for High Content Imaging and Analysis.

    PubMed

    Chambers, Kevin M; Mandavilli, Bhaskar S; Dolman, Nick J; Janes, Michael S

    2018-01-01

    Automated quantitative fluorescence microscopy, also known as high content imaging (HCI), is a rapidly growing analytical approach in cell biology. Because automated image analysis relies heavily on robust demarcation of cells and subcellular regions, reliable methods for labeling cells is a critical component of the HCI workflow. Labeling of cells for image segmentation is typically performed with fluorescent probes that bind DNA for nuclear-based cell demarcation or with those which react with proteins for image analysis based on whole cell staining. These reagents, along with instrument and software settings, play an important role in the successful segmentation of cells in a population for automated and quantitative image analysis. In this chapter, we describe standard procedures for labeling and image segmentation in both live and fixed cell samples. The chapter will also provide troubleshooting guidelines for some of the common problems associated with these aspects of HCI.

  14. Segmentation of cortical bone using fast level sets

    NASA Astrophysics Data System (ADS)

    Chowdhury, Manish; Jörgens, Daniel; Wang, Chunliang; Smedby, Årjan; Moreno, Rodrigo

    2017-02-01

    Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.

  15. Changes in Tear Volume after 3% Diquafosol Treatment in Patients with Dry Eye Syndrome: An Anterior Segment Spectral-domain Optical Coherence Tomography Study.

    PubMed

    Lee, Kwan Bok; Koh, Kyung Min; Kwon, Young A; Song, Sang Wroul; Kim, Byoung Yeop; Chung, Jae Lim

    2017-08-01

    To evaluate changes in the tear meniscus area and tear meniscus height over time in patients with dry eye syndrome, using anterior segment spectral-domain optical coherence tomography after the instillation of 3% diquafosol ophthalmic solution. Sixty eyes from 30 patients with mild to moderate dry eye syndrome were included. Tear meniscus images acquired by anterior segment spectral-domain optical coherence tomography were analyzed using National Institutes of Health's image-analysis software (ImageJ 1.44p). Tear meniscus area and tear meniscus height were measured at baseline, 5 minutes, 10 minutes, and 30 minutes after instillation of a drop of diquafosol in one eye and normal saline in the other eye. Changes in ocular surface disease index score, tear film break-up time, corneal staining score by Oxford schema, and meibomian expressibility were also evaluated at baseline, and after 1 week and 1 month of a diquafosol daily regimen. Sixty eyes from 30 subjects (mean age, 29.3 years; 8 men and 22 women) were included. In eyes receiving diquafosol, tear volume was increased at 5 and 10 minutes compared with baseline. It was also higher than saline instilled eyes at 5, 10, and 30 minutes. Changes in tear volume with respect to baseline were not statistically different after the use of diquafosol for 1 month. Ocular surface disease index score, tear film break-up time, and Oxford cornea stain score were significantly improved after 1 week and 1 month of daily diquafosol instillation, but meibomian expressibility did not change. Topical diquafosol ophthalmic solution effectively increased tear volume for up to 30 minutes, compared to normal saline in patients with dry eye syndrome. © 2017 The Korean Ophthalmological Society

  16. Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images

    PubMed Central

    Dysli, Chantal; Enzmann, Volker; Sznitman, Raphael; Zinkernagel, Martin S.

    2015-01-01

    Purpose Quantification of retinal layers using automated segmentation of optical coherence tomography (OCT) images allows for longitudinal studies of retinal and neurological disorders in mice. The purpose of this study was to compare the performance of automated retinal layer segmentation algorithms with data from manual segmentation in mice using the Spectralis OCT. Methods Spectral domain OCT images from 55 mice from three different mouse strains were analyzed in total. The OCT scans from 22 C57Bl/6, 22 BALBc, and 11 C3A.Cg-Pde6b+Prph2Rd2/J mice were automatically segmented using three commercially available automated retinal segmentation algorithms and compared to manual segmentation. Results Fully automated segmentation performed well in mice and showed coefficients of variation (CV) of below 5% for the total retinal volume. However, all three automated segmentation algorithms yielded much thicker total retinal thickness values compared to manual segmentation data (P < 0.0001) due to segmentation errors in the basement membrane. Conclusions Whereas the automated retinal segmentation algorithms performed well for the inner layers, the retinal pigmentation epithelium (RPE) was delineated within the sclera, leading to consistently thicker measurements of the photoreceptor layer and the total retina. Translational Relevance The introduction of spectral domain OCT allows for accurate imaging of the mouse retina. Exact quantification of retinal layer thicknesses in mice is important to study layers of interest under various pathological conditions. PMID:26336634

  17. Automated 3D Ultrasound Image Segmentation to Aid Breast Cancer Image Interpretation

    PubMed Central

    Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Yuan, Jie; Wang, Xueding; Carson, Paul L.

    2015-01-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer. PMID:26547117

  18. Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer

    NASA Astrophysics Data System (ADS)

    Wang, Yuxin; Gu, Peng; Lee, Won-Mean; Roubidoux, Marilyn A.; Du, Sidan; Yuan, Jie; Wang, Xueding; Carson, Paul L.

    2016-04-01

    Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

  19. Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer.

    PubMed

    Schinagl, Dominic A X; Vogel, Wouter V; Hoffmann, Aswin L; van Dalen, Jorn A; Oyen, Wim J; Kaanders, Johannes H A M

    2007-11-15

    Target-volume delineation for radiation treatment to the head and neck area traditionally is based on physical examination, computed tomography (CT), and magnetic resonance imaging. Additional molecular imaging with (18)F-fluoro-deoxy-glucose (FDG)-positron emission tomography (PET) may improve definition of the gross tumor volume (GTV). In this study, five methods for tumor delineation on FDG-PET are compared with CT-based delineation. Seventy-eight patients with Stages II-IV squamous cell carcinoma of the head and neck area underwent coregistered CT and FDG-PET. The primary tumor was delineated on CT, and five PET-based GTVs were obtained: visual interpretation, applying an isocontour of a standardized uptake value of 2.5, using a fixed threshold of 40% and 50% of the maximum signal intensity, and applying an adaptive threshold based on the signal-to-background ratio. Absolute GTV volumes were compared, and overlap analyses were performed. The GTV method of applying an isocontour of a standardized uptake value of 2.5 failed to provide successful delineation in 45% of cases. For the other PET delineation methods, volume and shape of the GTV were influenced heavily by the choice of segmentation tool. On average, all threshold-based PET-GTVs were smaller than on CT. Nevertheless, PET frequently detected significant tumor extension outside the GTV delineated on CT (15-34% of PET volume). The choice of segmentation tool for target-volume definition of head and neck cancer based on FDG-PET images is not trivial because it influences both volume and shape of the resulting GTV. With adequate delineation, PET may add significantly to CT- and physical examination-based GTV definition.

  20. Automated tumor volumetry using computer-aided image segmentation.

    PubMed

    Gaonkar, Bilwaj; Macyszyn, Luke; Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A; Ali, Zarina S; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M; Davatzikos, Christos

    2015-05-01

    Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0-5 rating scale where 5 indicated perfect segmentation. The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

  1. Automated Tumor Volumetry Using Computer-Aided Image Segmentation

    PubMed Central

    Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

    2015-01-01

    Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

  2. Image segmentation by iterative parallel region growing with application to data compression and image analysis

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    1988-01-01

    Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

  3. Issues about axial diffusion during segmental hair analysis.

    PubMed

    Kintz, Pascal

    2013-06-01

    matrix or changes in the hair structure due to cosmetic treatments might mislead the final result of hair analysis. To qualify for a single exposure in hair, the author proposes to consider that the highest drug concentration must be detected in the segment corresponding to the period of the alleged event (calculated with a hair growth rate at 1 cm/mo) and that the measured concentration be at least 3 times higher than those measured in the previous or the following segments. This must only be done using scalp hair after cutting the hair directly close to the scalp.

  4. Segmentation and learning in the quantitative analysis of microscopy images

    NASA Astrophysics Data System (ADS)

    Ruggiero, Christy; Ross, Amy; Porter, Reid

    2015-02-01

    In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

  5. Quantitative estimation of a ratio of intracranial cerebrospinal fluid volume to brain volume based on segmentation of CT images in patients with extra-axial hematoma.

    PubMed

    Nguyen, Ha Son; Patel, Mohit; Li, Luyuan; Kurpad, Shekar; Mueller, Wade

    2017-02-01

    Background Diminishing volume of intracranial cerebrospinal fluid (CSF) in patients with space-occupying masses have been attributed to unfavorable outcome associated with reduction of cerebral perfusion pressure and subsequent brain ischemia. Objective The objective of this article is to employ a ratio of CSF volume to brain volume for longitudinal assessment of space-volume relationships in patients with extra-axial hematoma and to determine variability of the ratio among patients with different types and stages of hematoma. Patients and methods In our retrospective study, we reviewed 113 patients with surgical extra-axial hematomas. We included 28 patients (age 61.7 +/- 17.7 years; 19 males, nine females) with an acute epidural hematoma (EDH) ( n = 5) and subacute/chronic subdural hematoma (SDH) ( n = 23). We excluded 85 patients, in order, due to acute SDH ( n = 76), concurrent intraparenchymal pathology ( n = 6), and bilateral pathology ( n = 3). Noncontrast CT images of the head were obtained using a CT scanner (2004 GE LightSpeed VCT CT system, tube voltage 140 kVp, tube current 310 mA, 5 mm section thickness) preoperatively, postoperatively (3.8 ± 5.8 hours from surgery), and at follow-up clinic visit (48.2 ± 27.7 days after surgery). Each CT scan was loaded into an OsiriX (Pixmeo, Switzerland) workstation to segment pixels based on radiodensity properties measured in Hounsfield units (HU). Based on HU values from -30 to 100, brain, CSF spaces, vascular structures, hematoma, and/or postsurgical fluid were segregated from bony structures, and subsequently hematoma and/or postsurgical fluid were manually selected and removed from the images. The remaining images represented overall brain volume-containing only CSF spaces, vascular structures, and brain parenchyma. Thereafter, the ratio between the total number of voxels representing CSF volume (based on values between 0 and 15 HU) to the total number of voxels

  6. Fuzzy pulmonary vessel segmentation in contrast enhanced CT data

    NASA Astrophysics Data System (ADS)

    Kaftan, Jens N.; Kiraly, Atilla P.; Bakai, Annemarie; Das, Marco; Novak, Carol L.; Aach, Til

    2008-03-01

    Pulmonary vascular tree segmentation has numerous applications in medical imaging and computer-aided diagnosis (CAD), including detection and visualization of pulmonary emboli (PE), improved lung nodule detection, and quantitative vessel analysis. We present a novel approach to pulmonary vessel segmentation based on a fuzzy segmentation concept, combining the strengths of both threshold and seed point based methods. The lungs of the original image are first segmented and a threshold-based approach identifies core vessel components with a high specificity. These components are then used to automatically identify reliable seed points for a fuzzy seed point based segmentation method, namely fuzzy connectedness. The output of the method consists of the probability of each voxel belonging to the vascular tree. Hence, our method provides the possibility to adjust the sensitivity/specificity of the segmentation result a posteriori according to application-specific requirements, through definition of a minimum vessel-probability required to classify a voxel as belonging to the vascular tree. The method has been evaluated on contrast-enhanced thoracic CT scans from clinical PE cases and demonstrates overall promising results. For quantitative validation we compare the segmentation results to randomly selected, semi-automatically segmented sub-volumes and present the resulting receiver operating characteristic (ROC) curves. Although we focus on contrast enhanced chest CT data, the method can be generalized to other regions of the body as well as to different imaging modalities.

  7. Spine segmentation from C-arm CT data sets: application to region-of-interest volumes for spinal interventions

    NASA Astrophysics Data System (ADS)

    Buerger, C.; Lorenz, C.; Babic, D.; Hoppenbrouwers, J.; Homan, R.; Nachabe, R.; Racadio, J. M.; Grass, M.

    2017-03-01

    Spinal fusion is a common procedure to stabilize the spinal column by fixating parts of the spine. In such procedures, metal screws are inserted through the patients back into a vertebra, and the screws of adjacent vertebrae are connected by metal rods to generate a fixed bridge. In these procedures, 3D image guidance for intervention planning and outcome control is required. Here, for anatomical guidance, an automated approach for vertebra segmentation from C-arm CT images of the spine is introduced and evaluated. As a prerequisite, 3D C-arm CT images are acquired covering the vertebrae of interest. An automatic model-based segmentation approach is applied to delineate the outline of the vertebrae of interest. The segmentation approach is based on 24 partial models of the cervical, thoracic and lumbar vertebrae which aggregate information about (i) the basic shape itself, (ii) trained features for image based adaptation, and (iii) potential shape variations. Since the volume data sets generated by the C-arm system are limited to a certain region of the spine the target vertebra and hence initial model position is assigned interactively. The approach was trained and tested on 21 human cadaver scans. A 3-fold cross validation to ground truth annotations yields overall mean segmentation errors of 0.5 mm for T1 to 1.1 mm for C6. The results are promising and show potential to support the clinician in pedicle screw path and rod planning to allow accurate and reproducible insertions.

  8. Automated segmentation of the lungs from high resolution CT images for quantitative study of chronic obstructive pulmonary diseases

    NASA Astrophysics Data System (ADS)

    Garg, Ishita; Karwoski, Ronald A.; Camp, Jon J.; Bartholmai, Brian J.; Robb, Richard A.

    2005-04-01

    Chronic obstructive pulmonary diseases (COPD) are debilitating conditions of the lung and are the fourth leading cause of death in the United States. Early diagnosis is critical for timely intervention and effective treatment. The ability to quantify particular imaging features of specific pathology and accurately assess progression or response to treatment with current imaging tools is relatively poor. The goal of this project was to develop automated segmentation techniques that would be clinically useful as computer assisted diagnostic tools for COPD. The lungs were segmented using an optimized segmentation threshold and the trachea was segmented using a fixed threshold characteristic of air. The segmented images were smoothed by a morphological close operation using spherical elements of different sizes. The results were compared to other segmentation approaches using an optimized threshold to segment the trachea. Comparison of the segmentation results from 10 datasets showed that the method of trachea segmentation using a fixed air threshold followed by morphological closing with spherical element of size 23x23x5 yielded the best results. Inclusion of greater number of pulmonary vessels in the lung volume is important for the development of computer assisted diagnostic tools because the physiological changes of COPD can result in quantifiable anatomic changes in pulmonary vessels. Using a fixed threshold to segment the trachea removed airways from the lungs to a better extent as compared to using an optimized threshold. Preliminary measurements gathered from patient"s CT scans suggest that segmented images can be used for accurate analysis of total lung volume and volumes of regional lung parenchyma. Additionally, reproducible segmentation allows for quantification of specific pathologic features, such as lower intensity pixels, which are characteristic of abnormal air spaces in diseases like emphysema.

  9. Automatic segmentation and reconstruction of the cortex from neonatal MRI.

    PubMed

    Xue, Hui; Srinivasan, Latha; Jiang, Shuzhou; Rutherford, Mary; Edwards, A David; Rueckert, Daniel; Hajnal, Joseph V

    2007-11-15

    Segmentation and reconstruction of cortical surfaces from magnetic resonance (MR) images are more challenging for developing neonates than adults. This is mainly due to the dynamic changes in the contrast between gray matter (GM) and white matter (WM) in both T1- and T2-weighted images (T1w and T2w) during brain maturation. In particular in neonatal T2w images WM typically has higher signal intensity than GM. This causes mislabeled voxels during cortical segmentation, especially in the cortical regions of the brain and in particular at the interface between GM and cerebrospinal fluid (CSF). We propose an automatic segmentation algorithm detecting these mislabeled voxels and correcting errors caused by partial volume effects. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme. Quantitative validation against manual segmentation demonstrates good performance (the mean Dice value: 0.758+/-0.037 for GM and 0.794+/-0.078 for WM). The inner, central and outer cortical surfaces are then reconstructed using implicit surface evolution. A landmark study is performed to verify the accuracy of the reconstructed cortex (the mean surface reconstruction error: 0.73 mm for inner surface and 0.63 mm for the outer). Both segmentation and reconstruction have been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. This preliminary analysis confirms previous findings that cortical surface area and curvature increase with age, and that surface area scales to cerebral volume according to a power law, while cortical thickness is not related to age or brain growth.

  10. CT-based manual segmentation and evaluation of paranasal sinuses.

    PubMed

    Pirner, S; Tingelhoff, K; Wagner, I; Westphal, R; Rilk, M; Wahl, F M; Bootz, F; Eichhorn, Klaus W G

    2009-04-01

    Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.

  11. Airway extraction from 3D chest CT volumes based on iterative extension of VOI enhanced by cavity enhancement filter

    NASA Astrophysics Data System (ADS)

    Meng, Qier; Kitasaka, Takayuki; Oda, Masahiro; Mori, Kensaku

    2017-03-01

    Airway segmentation is an important step in analyzing chest CT volumes for computerized lung cancer detection, emphysema diagnosis, asthma diagnosis, and pre- and intra-operative bronchoscope navigation. However, obtaining an integrated 3-D airway tree structure from a CT volume is a quite challenging task. This paper presents a novel airway segmentation method based on intensity structure analysis and bronchi shape structure analysis in volume of interest (VOI). This method segments the bronchial regions by applying the cavity enhancement filter (CEF) to trace the bronchial tree structure from the trachea. It uses the CEF in each VOI to segment each branch and to predict the positions of VOIs which envelope the bronchial regions in next level. At the same time, a leakage detection is performed to avoid the leakage by analysing the pixel information and the shape information of airway candidate regions extracted in the VOI. Bronchial regions are finally obtained by unifying the extracted airway regions. The experiments results showed that the proposed method can extract most of the bronchial region in each VOI and led good results of the airway segmentation.

  12. Extreme liver resections with preservation of segment 4 only

    PubMed Central

    Balzan, Silvio Marcio Pegoraro; Gava, Vinícius Grando; Magalhães, Marcelo Arbo; Dotto, Marcelo Luiz

    2017-01-01

    AIM To evaluate safety and outcomes of a new technique for extreme hepatic resections with preservation of segment 4 only. METHODS The new method of extreme liver resection consists of a two-stage hepatectomy. The first stage involves a right hepatectomy with middle hepatic vein preservation and induction of left lobe congestion; the second stage involves a left lobectomy. Thus, the remnant liver is represented by the segment 4 only (with or without segment 1, ± S1). Five patients underwent the new two-stage hepatectomy (congestion group). Data from volumetric assessment made before the second stage was compared with that of 10 matched patients (comparison group) that underwent a single-stage right hepatectomy with middle hepatic vein preservation. RESULTS The two stages of the procedure were successfully carried out on all 5 patients. For the congestion group, the overall volume of the left hemiliver had increased 103% (mean increase from 438 mL to 890 mL) at 4 wk after the first stage of the procedure. Hypertrophy of the future liver remnant (i.e., segment 4 ± S1) was higher than that of segments 2 and 3 (144% vs 54%, respectively, P < 0.05). The median remnant liver volume-to-body weight ratio was 0.3 (range, 0.28-0.40) before the first stage and 0.8 (range, 0.45-0.97) before the second stage. For the comparison group, the rate of hypertrophy of the left liver after right hepatectomy with middle hepatic vein preservation was 116% ± 34%. Hypertrophy rates of segments 2 and 3 (123% ± 47%) and of segment 4 (108% ± 60%, P > 0.05) were proportional. The mean preoperative volume of segments 2 and 3 was 256 ± 64 cc and increased to 572 ± 257 cc after right hepatectomy. Mean preoperative volume of segment 4 increased from 211 ± 75 cc to 439 ± 180 cc after surgery. CONCLUSION The proposed method for extreme hepatectomy with preservation of segment 4 only represents a technique that could allow complete resection of multiple bilateral liver metastases. PMID

  13. Statistical evaluation of manual segmentation of a diffuse low-grade glioma MRI dataset.

    PubMed

    Ben Abdallah, Meriem; Blonski, Marie; Wantz-Mezieres, Sophie; Gaudeau, Yann; Taillandier, Luc; Moureaux, Jean-Marie

    2016-08-01

    Software-based manual segmentation is critical to the supervision of diffuse low-grade glioma patients and to the optimal treatment's choice. However, manual segmentation being time-consuming, it is difficult to include it in the clinical routine. An alternative to circumvent the time cost of manual segmentation could be to share the task among different practitioners, providing it can be reproduced. The goal of our work is to assess diffuse low-grade gliomas' manual segmentation's reproducibility on MRI scans, with regard to practitioners, their experience and field of expertise. A panel of 13 experts manually segmented 12 diffuse low-grade glioma clinical MRI datasets using the OSIRIX software. A statistical analysis gave promising results, as the practitioner factor, the medical specialty and the years of experience seem to have no significant impact on the average values of the tumor volume variable.

  14. Hippocampal subfield segmentation in temporal lobe epilepsy: Relation to outcomes.

    PubMed

    Kreilkamp, B A K; Weber, B; Elkommos, S B; Richardson, M P; Keller, S S

    2018-06-01

    To investigate the clinical and surgical outcome correlates of preoperative hippocampal subfield volumes in patients with refractory temporal lobe epilepsy (TLE) using a new magnetic resonance imaging (MRI) multisequence segmentation technique. We recruited 106 patients with TLE and hippocampal sclerosis (HS) who underwent conventional T1-weighted and T2 short TI inversion recovery MRI. An automated hippocampal segmentation algorithm was used to identify twelve subfields in each hippocampus. A total of 76 patients underwent amygdalohippocampectomy and postoperative seizure outcome assessment using the standardized ILAE classification. Semiquantitative hippocampal internal architecture (HIA) ratings were correlated with hippocampal subfield volumes. Patients with left TLE had smaller volumes of the contralateral presubiculum and hippocampus-amygdala transition area compared to those with right TLE. Patients with right TLE had reduced contralateral hippocampal tail volumes and improved outcomes. In all patients, there were no significant relationships between hippocampal subfield volumes and clinical variables such as duration and age at onset of epilepsy. There were no significant differences in any hippocampal subfield volumes between patients who were rendered seizure free and those with persistent postoperative seizure symptoms. Ipsilateral but not contralateral HIA ratings were significantly correlated with gross hippocampal and subfield volumes. Our results suggest that ipsilateral hippocampal subfield volumes are not related to the chronicity/severity of TLE. We did not find any hippocampal subfield volume or HIA rating differences in patients with optimal and unfavorable outcomes. In patients with TLE and HS, sophisticated analysis of hippocampal architecture on MRI may have limited value for prediction of postoperative outcome. © 2018 The Authors. Acta Neurologica Scandinavica Published by John Wiley & Sons Ltd.

  15. SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES.

    PubMed

    Seyedhosseini, Mojtaba; Ellisman, Mark H; Tasdizen, Tolga

    2013-01-01

    High-resolution microscopy techniques have been used to generate large volumes of data with enough details for understanding the complex structure of the nervous system. However, automatic techniques are required to segment cells and intracellular structures in these multi-terabyte datasets and make anatomical analysis possible on a large scale. We propose a fully automated method that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy (EM) images. The main idea is to use algebraic curves to extract shape features together with texture features from image patches. Then, these powerful features are used to learn a random forest classifier, which can predict mitochondria locations precisely. Finally, the algebraic curves together with regional information are used to segment the mitochondria at the predicted locations. We demonstrate that our method outperforms the state-of-the-art algorithms in segmentation of mitochondria in EM images.

  16. An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis.

    PubMed

    Li, Kai; Rüdiger, Heinz; Haase, Rocco; Ziemssen, Tjalf

    2018-01-01

    Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool. Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS. Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations. Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.

  17. Segmental distribution of high-volume caudal anesthesia in neonates, infants, and toddlers as assessed by ultrasonography.

    PubMed

    Lundblad, Märit; Lönnqvist, Per-Arne; Eksborg, Staffan; Marhofer, Peter

    2011-02-01

    The aim of this prospective, age-stratified, observational study was to determine the cranial extent of spread of a large volume (1.5 ml·kg(-1) , ropivacaine 0.2%), single-shot caudal epidural injection using real-time ultrasonography. Fifty ASA I-III children were included in the study, stratified in three age groups; neonates, infants (1-12 months), and toddlers (1-4 years). The caudal blocks were performed during ultrasonographic observation of the spread of local anesthetic (LA) in the epidural space. A significant inverse relationship was found between age, weight, and height, and the maximal cranial level reached by 1.5 ml·kg(-1) of LA. In neonates, 93% of the blocks reached a cranial level of ≥Th12 vs 73% and 25% in infants and toddlers, respectively. Based on our data, a predictive equation of segmental spread was generated: Dose (ml/spinal segment) = 0.1539·(BW in kg)-0.0937. This study found an inverse relationship between age, weight, and height and the number of segments covered by a caudal injection of 1.5 ml·kg(-1) of ropivacaine 0.2% in children 0-4 years of age. However, the cranial spread of local anesthetics within the spinal canal as assessed by immediate ultrasound visualization was found to be in poor agreement with previously published predictive equations that are based on actual cutaneous dermatomal testing. © 2010 Blackwell Publishing Ltd.

  18. Segmental Musculoskeletal Examinations using Dual-Energy X-Ray Absorptiometry (DXA): Positioning and Analysis Considerations

    PubMed Central

    Hart, Nicolas H.; Nimphius, Sophia; Spiteri, Tania; Cochrane, Jodie L.; Newton, Robert U.

    2015-01-01

    Musculoskeletal examinations provide informative and valuable quantitative insight into muscle and bone health. DXA is one mainstream tool used to accurately and reliably determine body composition components and bone mass characteristics in-vivo. Presently, whole body scan models separate the body into axial and appendicular regions, however there is a need for localised appendicular segmentation models to further examine regions of interest within the upper and lower extremities. Similarly, inconsistencies pertaining to patient positioning exist in the literature which influence measurement precision and analysis outcomes highlighting a need for standardised procedure. This paper provides standardised and reproducible: 1) positioning and analysis procedures using DXA and 2) reliable segmental examinations through descriptive appendicular boundaries. Whole-body scans were performed on forty-six (n = 46) football athletes (age: 22.9 ± 4.3 yrs; height: 1.85 ± 0.07 cm; weight: 87.4 ± 10.3 kg; body fat: 11.4 ± 4.5 %) using DXA. All segments across all scans were analysed three times by the main investigator on three separate days, and by three independent investigators a week following the original analysis. To examine intra-rater and inter-rater, between day and researcher reliability, coefficients of variation (CV) and intraclass correlation coefficients (ICC) were determined. Positioning and segmental analysis procedures presented in this study produced very high, nearly perfect intra-tester (CV ≤ 2.0%; ICC ≥ 0.988) and inter-tester (CV ≤ 2.4%; ICC ≥ 0.980) reliability, demonstrating excellent reproducibility within and between practitioners. Standardised examinations of axial and appendicular segments are necessary. Future studies aiming to quantify and report segmental analyses of the upper- and lower-body musculoskeletal properties using whole-body DXA scans are encouraged to use the patient positioning and image analysis procedures outlined in this

  19. Automated segmentation and tracking for large-scale analysis of focal adhesion dynamics.

    PubMed

    Würflinger, T; Gamper, I; Aach, T; Sechi, A S

    2011-01-01

    Cell adhesion, a process mediated by the formation of discrete structures known as focal adhesions (FAs), is pivotal to many biological events including cell motility. Much is known about the molecular composition of FAs, although our knowledge of the spatio-temporal recruitment and the relative occupancy of the individual components present in the FAs is still incomplete. To fill this gap, an essential prerequisite is a highly reliable procedure for the recognition, segmentation and tracking of FAs. Although manual segmentation and tracking may provide some advantages when done by an expert, its performance is usually hampered by subjective judgement and the long time required in analysing large data sets. Here, we developed a model-based segmentation and tracking algorithm that overcomes these problems. In addition, we developed a dedicated computational approach to correct segmentation errors that may arise from the analysis of poorly defined FAs. Thus, by achieving accurate and consistent FA segmentation and tracking, our work establishes the basis for a comprehensive analysis of FA dynamics under various experimental regimes and the future development of mathematical models that simulate FA behaviour. © 2010 The Authors Journal of Microscopy © 2010 The Royal Microscopical Society.

  20. Multi-object segmentation framework using deformable models for medical imaging analysis.

    PubMed

    Namías, Rafael; D'Amato, Juan Pablo; Del Fresno, Mariana; Vénere, Marcelo; Pirró, Nicola; Bellemare, Marc-Emmanuel

    2016-08-01

    Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed

  1. Automatic comic page image understanding based on edge segment analysis

    NASA Astrophysics Data System (ADS)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  2. 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

  3. Segmentation of hepatic artery in multi-phase liver CT using directional dilation and connectivity analysis

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Schnurr, Alena-Kathrin; Zidowitz, Stephan; Georgii, Joachim; Zhao, Yue; Razavi, Mohammad; Schwier, Michael; Hahn, Horst K.; Hansen, Christian

    2016-03-01

    Segmentation of hepatic arteries in multi-phase computed tomography (CT) images is indispensable in liver surgery planning. During image acquisition, the hepatic artery is enhanced by the injection of contrast agent. The enhanced signals are often not stably acquired due to non-optimal contrast timing. Other vascular structure, such as hepatic vein or portal vein, can be enhanced as well in the arterial phase, which can adversely affect the segmentation results. Furthermore, the arteries might suffer from partial volume effects due to their small diameter. To overcome these difficulties, we propose a framework for robust hepatic artery segmentation requiring a minimal amount of user interaction. First, an efficient multi-scale Hessian-based vesselness filter is applied on the artery phase CT image, aiming to enhance vessel structures with specified diameter range. Second, the vesselness response is processed using a Bayesian classifier to identify the most probable vessel structures. Considering the vesselness filter normally performs not ideally on the vessel bifurcations or the segments corrupted by noise, two vessel-reconnection techniques are proposed. The first technique uses a directional morphological operator to dilate vessel segments along their centerline directions, attempting to fill the gap between broken vascular segments. The second technique analyzes the connectivity of vessel segments and reconnects disconnected segments and branches. Finally, a 3D vessel tree is reconstructed. The algorithm has been evaluated using 18 CT images of the liver. To quantitatively measure the similarities between segmented and reference vessel trees, the skeleton coverage and mean symmetric distance are calculated to quantify the agreement between reference and segmented vessel skeletons, resulting in an average of 0:55+/-0:27 and 12:7+/-7:9 mm (mean standard deviation), respectively.

  4. Analysis of radially cracked ring segments subject to forces and couples

    NASA Technical Reports Server (NTRS)

    Gross, B.; Srawley, J. E.

    1977-01-01

    Results of planar boundary collocation analysis are given for ring segment (C-shaped) specimens with radial cracks, subjected to combined forces and couples. Mode I stress intensity factors and crack mouth opening displacements were determined for ratios of outer to inner radius in the range 1.1 to 2.5 and ratios of crack length to segment width in the range 0.1 to 0.8.

  5. Analysis of radially cracked ring segments subject to forces and couples

    NASA Technical Reports Server (NTRS)

    Gross, B.; Strawley, J. E.

    1975-01-01

    Results of planar boundary collocation analysis are given for ring segment (C shaped) specimens with radial cracks, subjected to combined forces and couples. Mode I stress intensity factors and crack mouth opening displacements were determined for ratios of outer to inner radius in the range 1.1 to 2.5, and ratios of crack length to segment width in the range 0.1 to 0.8.

  6. Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction.

    PubMed

    Wang, Jinke; Guo, Haoyan

    2016-01-01

    This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm 3 , volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

  7. Improved inference in Bayesian segmentation using Monte Carlo sampling: application to hippocampal subfield volumetry.

    PubMed

    Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen

    2013-10-01

    Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the technique also allows one to compute informative "error bars" on the volume estimates of individual structures. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry

    PubMed Central

    Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Leemput, Koen Van

    2013-01-01

    Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the technique also allows one to compute informative “error bars” on the volume estimates of individual structures. PMID:23773521

  9. Atlas-Based Segmentation Improves Consistency and Decreases Time Required for Contouring Postoperative Endometrial Cancer Nodal Volumes

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

    Young, Amy V.; Department of Radiation Oncology, St. Luke's-Roosevelt Hospital, New York, NY; Wortham, Angela

    2011-03-01

    Purpose: Accurate target delineation of the nodal volumes is essential for three-dimensional conformal and intensity-modulated radiotherapy planning for endometrial cancer adjuvant therapy. We hypothesized that atlas-based segmentation ('autocontouring') would lead to time savings and more consistent contours among physicians. Methods and Materials: A reference anatomy atlas was constructed using the data from 15 postoperative endometrial cancer patients by contouring the pelvic nodal clinical target volume on the simulation computed tomography scan according to the Radiation Therapy Oncology Group 0418 trial using commercially available software. On the simulation computed tomography scans from 10 additional endometrial cancer patients, the nodal clinical targetmore » volume autocontours were generated. Three radiation oncologists corrected the autocontours and delineated the manual nodal contours under timed conditions while unaware of the other contours. The time difference was determined, and the overlap of the contours was calculated using Dice's coefficient. Results: For all physicians, manual contouring of the pelvic nodal target volumes and editing the autocontours required a mean {+-} standard deviation of 32 {+-} 9 vs. 23 {+-} 7 minutes, respectively (p = .000001), a 26% time savings. For each physician, the time required to delineate the manual contours vs. correcting the autocontours was 30 {+-} 3 vs. 21 {+-} 5 min (p = .003), 39 {+-} 12 vs. 30 {+-} 5 min (p = .055), and 29 {+-} 5 vs. 20 {+-} 5 min (p = .0002). The mean overlap increased from manual contouring (0.77) to correcting the autocontours (0.79; p = .038). Conclusion: The results of our study have shown that autocontouring leads to increased consistency and time savings when contouring the nodal target volumes for adjuvant treatment of endometrial cancer, although the autocontours still required careful editing to ensure that the lymph nodes at risk of recurrence are properly included in the

  10. Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation.

    PubMed

    Lee, Aaron Y; Lee, Cecilia S; Keane, Pearse A; Tufail, Adnan

    2016-01-01

    Purpose. To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography (SD-OCT) images using a MapReduce framework. Methods. A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to $0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface was created using custom HTML5 and JavaScript code, and data analysis was performed using R. An automated pipeline was developed to handle the map and reduce steps of the framework. Results. More than 93,500 data points were collected using this framework for the 61 images submitted. Pearson's correlation of interrater reliability was 0.995 (p < 0.0001) and coefficient of determination was 0.991. The cost of segmenting the macular volume was $1.21. A total of 22 individual Mechanical Turk users provided segmentations, each completing an average of 5.5 HITs. Each HIT was completed in an average of 4.43 minutes. Conclusions. Amazon Mechanical Turk provides a cost-effective, scalable, high-availability infrastructure for manual segmentation of OCT images.

  11. Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation

    PubMed Central

    Lee, Aaron Y.; Lee, Cecilia S.; Keane, Pearse A.; Tufail, Adnan

    2016-01-01

    Purpose. To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography (SD-OCT) images using a MapReduce framework. Methods. A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to $0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface was created using custom HTML5 and JavaScript code, and data analysis was performed using R. An automated pipeline was developed to handle the map and reduce steps of the framework. Results. More than 93,500 data points were collected using this framework for the 61 images submitted. Pearson's correlation of interrater reliability was 0.995 (p < 0.0001) and coefficient of determination was 0.991. The cost of segmenting the macular volume was $1.21. A total of 22 individual Mechanical Turk users provided segmentations, each completing an average of 5.5 HITs. Each HIT was completed in an average of 4.43 minutes. Conclusions. Amazon Mechanical Turk provides a cost-effective, scalable, high-availability infrastructure for manual segmentation of OCT images. PMID:27293877

  12. A Real Time System for Multi-Sensor Image Analysis through Pyramidal Segmentation

    DTIC Science & Technology

    1992-01-30

    A Real Time Syte for M~ulti- sensor Image Analysis S. E I0 through Pyramidal Segmentation/ / c •) L. Rudin, S. Osher, G. Koepfler, J.9. Morel 7. ytu...experiments with reconnaissance photography, multi- sensor satellite imagery, medical CT and MRI multi-band data have shown a great practi- cal potential...C ,SF _/ -- / WSM iS-I-0-d41-40450 $tltwt, kw" I (nor.- . Z-97- A real-time system for multi- sensor image analysis through pyramidal segmentation

  13. 3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities

    NASA Astrophysics Data System (ADS)

    Cha, Jung won; Dunlap, Neal; Wang, Brian; Amini, Amir

    2016-03-01

    Lung boundary image segmentation is important for many tasks including for example in development of radiation treatment plans for subjects with thoracic malignancies. In this paper, we describe a method and parameter settings for accurate 3D lung boundary segmentation based on graph-cuts from X-ray CT data1. Even though previously several researchers have used graph-cuts for image segmentation, to date, no systematic studies have been performed regarding the range of parameter that give accurate results. The energy function in the graph-cuts algorithm requires 3 suitable parameter settings: K, a large constant for assigning seed points, c, the similarity coefficient for n-links, and λ, the terminal coefficient for t-links. We analyzed the parameter sensitivity with four lung data sets from subjects with lung cancer using error metrics. Large values of K created artifacts on segmented images, and relatively much larger value of c than the value of λ influenced the balance between the boundary term and the data term in the energy function, leading to unacceptable segmentation results. For a range of parameter settings, we performed 3D image segmentation, and in each case compared the results with the expert-delineated lung boundaries. We used simple 6-neighborhood systems for n-link in 3D. The 3D image segmentation took 10 minutes for a 512x512x118 ~ 512x512x190 lung CT image volume. Our results indicate that the graph-cuts algorithm was more sensitive to the K and λ parameter settings than to the C parameter and furthermore that amongst the range of parameters tested, K=5 and λ=0.5 yielded good results.

  14. Automatic lumen and outer wall segmentation of the carotid artery using deformable three-dimensional models in MR angiography and vessel wall images.

    PubMed

    van 't Klooster, Ronald; de Koning, Patrick J H; Dehnavi, Reza Alizadeh; Tamsma, Jouke T; de Roos, Albert; Reiber, Johan H C; van der Geest, Rob J

    2012-01-01

    To develop and validate an automated segmentation technique for the detection of the lumen and outer wall boundaries in MR vessel wall studies of the common carotid artery. A new segmentation method was developed using a three-dimensional (3D) deformable vessel model requiring only one single user interaction by combining 3D MR angiography (MRA) and 2D vessel wall images. This vessel model is a 3D cylindrical Non-Uniform Rational B-Spline (NURBS) surface which can be deformed to fit the underlying image data. Image data of 45 subjects was used to validate the method by comparing manual and automatic segmentations. Vessel wall thickness and volume measurements obtained by both methods were compared. Substantial agreement was observed between manual and automatic segmentation; over 85% of the vessel wall contours were segmented successfully. The interclass correlation was 0.690 for the vessel wall thickness and 0.793 for the vessel wall volume. Compared with manual image analysis, the automated method demonstrated improved interobserver agreement and inter-scan reproducibility. Additionally, the proposed automated image analysis approach was substantially faster. This new automated method can reduce analysis time and enhance reproducibility of the quantification of vessel wall dimensions in clinical studies. Copyright © 2011 Wiley Periodicals, Inc.

  15. Electric utility market research monograph series, Monograph 3: Market segmentation research: Volume 1 (Concept) and Volume 2 (Application)

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

    Chakravarti, D.; Hendrix, P.E.; Wilkie, W.L.

    1987-01-01

    Maturing markets and the accompanying increase in competition, sophistication of customers, and differentiation of products and services have forced companies to focus their marketing efforts on segments in which they can prosper. The experience in these companies has revealed that market segmentation, although simple in concept, is not so easily implemented. It is reasonable to anticipate substantial benefits from additional market segmentation within each of the classes traditionally distinguished in the industry - residential, commercial, and industrial. Segmentation is also likely to prove useful for utilities facing quite different marketing environments, e.g., in terms of demand patterns (number of customers,more » winter- and summer-peaking, etc.), capacity, and degree of regulatory and competitive pressures. Within utilities, those charged with developing and implementing segmentation strategies face some difficult issues. The primary objective of this monograph is to provide some answers to these questions. This monograph is intended to provide utility researchers with a guide to the design and execution of market segmentation research in utility markets. Several composite cases, drawn from actual studies conducted by electric utilities, are used to illustrate the discussion.« less

  16. Salted and preserved duck eggs: a consumer market segmentation analysis.

    PubMed

    Arthur, Jennifer; Wiseman, Kelleen; Cheng, K M

    2015-08-01

    The combination of increasing ethnic diversity in North America and growing consumer support for local food products may present opportunities for local producers and processors in the ethnic foods product category. Our study examined the ethnic Chinese (pop. 402,000) market for salted and preserved duck eggs in Vancouver, British Columbia (BC), Canada. The objective of the study was to develop a segmentation model using survey data to categorize consumer groups based on their attitudes and the importance they placed on product attributes. We further used post-segmentation acculturation score, demographics and buyer behaviors to define these groups. Data were gathered via a survey of randomly selected Vancouver households with Chinese surnames (n = 410), targeting the adult responsible for grocery shopping. Results from principal component analysis and a 2-step cluster analysis suggest the existence of 4 market segments, described as Enthusiasts, Potentialists, Pragmatists, Health Skeptics (salted duck eggs), and Neutralists (preserved duck eggs). Kruskal Wallis tests and post hoc Mann-Whitney tests found significant differences between segments in terms of attitudes and the importance placed on product characteristics. Health Skeptics, preserved egg Potentialists, and Pragmatists of both egg products were significantly biased against Chinese imports compared to others. Except for Enthusiasts, segments disagreed that eggs are 'Healthy Products'. Preserved egg Enthusiasts had a significantly lower acculturation score (AS) compared to all others, while salted egg Enthusiasts had a lower AS compared to Health Skeptics. All segments rated "produced in BC, not mainland China" products in the "neutral to very likely" range for increasing their satisfaction with the eggs. Results also indicate that buyers of each egg type are willing to pay an average premium of at least 10% more for BC produced products versus imports, with all other characteristics equal. Overall

  17. Pulmonary vessel segmentation utilizing curved planar reformation and optimal path finding (CROP) in computed tomographic pulmonary angiography (CTPA) for CAD applications

    NASA Astrophysics Data System (ADS)

    Zhou, Chuan; Chan, Heang-Ping; Kuriakose, Jean W.; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir M.; Guo, Yanhui; Patel, Smita; Kazerooni, Ella A.

    2012-03-01

    Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately under suboptimal conditions, such as vessels occluded by PEs, surrounded by lymphoid tissues or lung diseases, and crossing with other vessels. In this study, we developed a new vessel refinement method utilizing curved planar reformation (CPR) technique combined with optimal path finding method (MHES-CROP). The MHES segmented vessels straightened in the CPR volume was refined using adaptive gray level thresholding where the local threshold was obtained from least-square estimation of a spline curve fitted to the gray levels of the vessel along the straightened volume. An optimal path finding method based on Dijkstra's algorithm was finally used to trace the correct path for the vessel of interest. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOIs) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 32.9+/-10.2% using the MHES method to 9.9+/-7.9% using the MHES-CROP method. The accuracy of vessel segmentation was improved significantly (p<0.05). The intraclass correlation coefficient (ICC) of the segmented vessel volume between the automated segmentation and the reference standard was improved from 0.919 to 0.988. Quantitative comparison of the MHES method and the MHES-CROP method with the

  18. Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines.

    PubMed

    Teodoro, George; Kurç, Tahsin M; Taveira, Luís F R; Melo, Alba C M A; Gao, Yi; Kong, Jun; Saltz, Joel H

    2017-04-01

    Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Source code: https://github.com/SBU-BMI/region-templates/ . teodoro@unb.br. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  19. Development of Image Segmentation Methods for Intracranial Aneurysms

    PubMed Central

    Qian, Yi; Morgan, Michael

    2013-01-01

    Though providing vital means for the visualization, diagnosis, and quantification of decision-making processes for the treatment of vascular pathologies, vascular segmentation remains a process that continues to be marred by numerous challenges. In this study, we validate eight aneurysms via the use of two existing segmentation methods; the Region Growing Threshold and Chan-Vese model. These methods were evaluated by comparison of the results obtained with a manual segmentation performed. Based upon this validation study, we propose a new Threshold-Based Level Set (TLS) method in order to overcome the existing problems. With divergent methods of segmentation, we discovered that the volumes of the aneurysm models reached a maximum difference of 24%. The local artery anatomical shapes of the aneurysms were likewise found to significantly influence the results of these simulations. In contrast, however, the volume differences calculated via use of the TLS method remained at a relatively low figure, at only around 5%, thereby revealing the existence of inherent limitations in the application of cerebrovascular segmentation. The proposed TLS method holds the potential for utilisation in automatic aneurysm segmentation without the setting of a seed point or intensity threshold. This technique will further enable the segmentation of anatomically complex cerebrovascular shapes, thereby allowing for more accurate and efficient simulations of medical imagery. PMID:23606905

  20. High volume data storage architecture analysis

    NASA Technical Reports Server (NTRS)

    Malik, James M.

    1990-01-01

    A High Volume Data Storage Architecture Analysis was conducted. The results, presented in this report, will be applied to problems of high volume data requirements such as those anticipated for the Space Station Control Center. High volume data storage systems at several different sites were analyzed for archive capacity, storage hierarchy and migration philosophy, and retrieval capabilities. Proposed architectures were solicited from the sites selected for in-depth analysis. Model architectures for a hypothetical data archiving system, for a high speed file server, and for high volume data storage are attached.

  1. Semiautomatic segmentation of liver metastases on volumetric CT images

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

    Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng, E-mail: bz2166@cumc.columbia.edu

    2015-11-15

    Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accuratelymore » delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic

  2. Patient Segmentation Analysis Offers Significant Benefits For Integrated Care And Support.

    PubMed

    Vuik, Sabine I; Mayer, Erik K; Darzi, Ara

    2016-05-01

    Integrated care aims to organize care around the patient instead of the provider. It is therefore crucial to understand differences across patients and their needs. Segmentation analysis that uses big data can help divide a patient population into distinct groups, which can then be targeted with care models and intervention programs tailored to their needs. In this article we explore the potential applications of patient segmentation in integrated care. We propose a framework for population strategies in integrated care-whole populations, subpopulations, and high-risk populations-and show how patient segmentation can support these strategies. Through international case examples, we illustrate practical considerations such as choosing a segmentation logic, accessing data, and tailoring care models. Important issues for policy makers to consider are trade-offs between simplicity and precision, trade-offs between customized and off-the-shelf solutions, and the availability of linked data sets. We conclude that segmentation can provide many benefits to integrated care, and we encourage policy makers to support its use. Project HOPE—The People-to-People Health Foundation, Inc.

  3. Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data.

    PubMed

    Suresh, Niraj; Stephens, Sean A; Adams, Lexor; Beck, Anthon N; McKinney, Adriana L; Varga, Tamas

    2016-04-26

    Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB(1), to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm(2)) and the volume (in mm(3)). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

  4. Geometric convex cone volume analysis

    NASA Astrophysics Data System (ADS)

    Li, Hsiao-Chi; Chang, Chein-I.

    2016-05-01

    Convexity is a major concept used to design and develop endmember finding algorithms (EFAs). For abundance unconstrained techniques, Pixel Purity Index (PPI) and Automatic Target Generation Process (ATGP) which use Orthogonal Projection (OP) as a criterion, are commonly used method. For abundance partially constrained techniques, Convex Cone Analysis is generally preferred which makes use of convex cones to impose Abundance Non-negativity Constraint (ANC). For abundance fully constrained N-FINDR and Simplex Growing Algorithm (SGA) are most popular methods which use simplex volume as a criterion to impose ANC and Abundance Sum-to-one Constraint (ASC). This paper analyze an issue encountered in volume calculation with a hyperplane introduced to illustrate an idea of bounded convex cone. Geometric Convex Cone Volume Analysis (GCCVA) projects the boundary vectors of a convex cone orthogonally on a hyperplane to reduce the effect of background signatures and a geometric volume approach is applied to address the issue arose from calculating volume and further improve the performance of convex cone-based EFAs.

  5. Pleural effusion segmentation in thin-slice CT

    NASA Astrophysics Data System (ADS)

    Donohue, Rory; Shearer, Andrew; Bruzzi, John; Khosa, Huma

    2009-02-01

    A pleural effusion is excess fluid that collects in the pleural cavity, the fluid-filled space that surrounds the lungs. Surplus amounts of such fluid can impair breathing by limiting the expansion of the lungs during inhalation. Measuring the fluid volume is indicative of the effectiveness of any treatment but, due to the similarity to surround regions, fragments of collapsed lung present and topological changes; accurate quantification of the effusion volume is a difficult imaging problem. A novel code is presented which performs conditional region growth to accurately segment the effusion shape across a dataset. We demonstrate the applicability of our technique in the segmentation of pleural effusion and pulmonary masses.

  6. Three-dimensional choroidal segmentation in spectral OCT volumes using optic disc prior information

    NASA Astrophysics Data System (ADS)

    Hu, Zhihong; Girkin, Christopher A.; Hariri, Amirhossein; Sadda, SriniVas R.

    2016-03-01

    Recently, much attention has been focused on determining the role of the peripapillary choroid - the layer between the outer retinal pigment epithelium (RPE)/Bruchs membrane (BM) and choroid-sclera (C-S) junction, whether primary or secondary in the pathogenesis of glaucoma. However, the automated choroidal segmentation in spectral-domain optical coherence tomography (SD-OCT) images of optic nerve head (ONH) has not been reported probably due to the fact that the presence of the BM opening (BMO, corresponding to the optic disc) can deflect the choroidal segmentation from its correct position. The purpose of this study is to develop a 3D graph-based approach to identify the 3D choroidal layer in ONH-centered SD-OCT images using the BMO prior information. More specifically, an initial 3D choroidal segmentation was first performed using the 3D graph search algorithm. Note that varying surface interaction constraints based on the choroidal morphological model were applied. To assist the choroidal segmentation, two other surfaces of internal limiting membrane and innerouter segment junction were also segmented. Based on the segmented layer between the RPE/BM and C-S junction, a 2D projection map was created. The BMO in the projection map was detected by a 2D graph search. The pre-defined BMO information was then incorporated into the surface interaction constraints of the 3D graph search to obtain more accurate choroidal segmentation. Twenty SD-OCT images from 20 healthy subjects were used. The mean differences of the choroidal borders between the algorithm and manual segmentation were at a sub-voxel level, indicating a high level segmentation accuracy.

  7. Automatic Segmentation of Invasive Breast Carcinomas from DCE-MRI using Time Series Analysis

    PubMed Central

    Jayender, Jagadaeesan; Chikarmane, Sona; Jolesz, Ferenc A.; Gombos, Eva

    2013-01-01

    Purpose Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise and fitting algorithms. To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. Methods We modeled the underlying dynamics of the tumor by a LDS and use the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist’s segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). Results The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared to the radiologist’s segmentation and 82.1% accuracy and 100% sensitivity when compared to the CADstream output. The overlap of the algorithm output with the radiologist’s segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72 respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC=0.95. Conclusion The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI. PMID:24115175

  8. Mathematical Analysis of Space Radiator Segmenting for Increased Reliability and Reduced Mass

    NASA Technical Reports Server (NTRS)

    Juhasz, Albert J.

    2001-01-01

    Spacecraft for long duration deep space missions will need to be designed to survive micrometeoroid bombardment of their surfaces some of which may actually be punctured. To avoid loss of the entire mission the damage due to such punctures must be limited to small, localized areas. This is especially true for power system radiators, which necessarily feature large surface areas to reject heat at relatively low temperature to the space environment by thermal radiation. It may be intuitively obvious that if a space radiator is composed of a large number of independently operating segments, such as heat pipes, a random micrometeoroid puncture will result only in the loss of the punctured segment, and not the entire radiator. Due to the redundancy achieved by independently operating segments, the wall thickness and consequently the weight of such segments can be drastically reduced. Probability theory is used to estimate the magnitude of such weight reductions as the number of segments is increased. An analysis of relevant parameter values required for minimum mass segmented radiators is also included.

  9. Lung lobe modeling and segmentation with individualized surface meshes

    NASA Astrophysics Data System (ADS)

    Blaffert, Thomas; Barschdorf, Hans; von Berg, Jens; Dries, Sebastian; Franz, Astrid; Klinder, Tobias; Lorenz, Cristian; Renisch, Steffen; Wiemker, Rafael

    2008-03-01

    An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

  10. Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study.

    PubMed

    Dolz, Jose; Betrouni, Nacim; Quidet, Mathilde; Kharroubi, Dris; Leroy, Henri A; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

    2016-09-01

    Delineation of organs at risk (OARs) is a crucial step in surgical and treatment planning in brain cancer, where precise OARs volume delineation is required. However, this task is still often manually performed, which is time-consuming and prone to observer variability. To tackle these issues a deep learning approach based on stacking denoising auto-encoders has been proposed to segment the brainstem on magnetic resonance images in brain cancer context. Additionally to classical features used in machine learning to segment brain structures, two new features are suggested. Four experts participated in this study by segmenting the brainstem on 9 patients who underwent radiosurgery. Analysis of variance on shape and volume similarity metrics indicated that there were significant differences (p<0.05) between the groups of manual annotations and automatic segmentations. Experimental evaluation also showed an overlapping higher than 90% with respect to the ground truth. These results are comparable, and often higher, to those of the state of the art segmentation methods but with a considerably reduction of the segmentation time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Fast Edge Detection and Segmentation of Terrestrial Laser Scans Through Normal Variation Analysis

    NASA Astrophysics Data System (ADS)

    Che, E.; Olsen, M. J.

    2017-09-01

    Terrestrial Laser Scanning (TLS) utilizes light detection and ranging (lidar) to effectively and efficiently acquire point cloud data for a wide variety of applications. Segmentation is a common procedure of post-processing to group the point cloud into a number of clusters to simplify the data for the sequential modelling and analysis needed for most applications. This paper presents a novel method to rapidly segment TLS data based on edge detection and region growing. First, by computing the projected incidence angles and performing the normal variation analysis, the silhouette edges and intersection edges are separated from the smooth surfaces. Then a modified region growing algorithm groups the points lying on the same smooth surface. The proposed method efficiently exploits the gridded scan pattern utilized during acquisition of TLS data from most sensors and takes advantage of parallel programming to process approximately 1 million points per second. Moreover, the proposed segmentation does not require estimation of the normal at each point, which limits the errors in normal estimation propagating to segmentation. Both an indoor and outdoor scene are used for an experiment to demonstrate and discuss the effectiveness and robustness of the proposed segmentation method.

  12. Analysis of manual segmentation in paranasal CT images.

    PubMed

    Tingelhoff, Kathrin; Eichhorn, Klaus W G; Wagner, Ingo; Kunkel, Maria E; Moral, Analia I; Rilk, Markus E; Wahl, Friedrich M; Bootz, Friedrich

    2008-09-01

    Manual segmentation is often used for evaluation of automatic or semi-automatic segmentation. The purpose of this paper is to describe the inter and intraindividual variability, the dubiety of manual segmentation as a gold standard and to find reasons for the discrepancy. We realized two experiments. In the first one ten ENT surgeons, ten medical students and one engineer outlined the right maxillary sinus and ethmoid sinuses manually on a standard CT dataset of a human head. In the second experiment two participants outlined maxillary sinus and ethmoid sinuses five times consecutively. Manual segmentation was accomplished with custom software using a line segmentation tool. The first experiment shows the interindividual variability of manual segmentation which is higher for ethmoidal sinuses than for maxillary sinuses. The variability can be caused by the level of experience, different interpretation of the CT data or different levels of accuracy. The second experiment shows intraindividual variability which is lower than interindividual variability. Most variances in both experiments appear during segmentation of ethmoidal sinuses and outlining hiatus semilunaris. Concerning the inter and intraindividual variances the segmentation result of one manual segmenter could not directly be used as gold standard for the evaluation of automatic segmentation algorithms.

  13. Video Segmentation Descriptors for Event Recognition

    DTIC Science & Technology

    2014-12-08

    Velastin, 3D Extended Histogram of Oriented Gradients (3DHOG) for Classification of Road Users in Urban Scenes , BMVC, 2009. [3] M.-Y. Chen and A. Hauptmann...computed on 3D volume outputted by the hierarchical segmentation . Each video is described as follows. Each supertube is temporally divided in n-frame...strength of these descriptors is their adaptability to the scene variations since they are grounded on a video segmentation . This makes them naturally robust

  14. White blood cell counting analysis of blood smear images using various segmentation strategies

    NASA Astrophysics Data System (ADS)

    Safuan, Syadia Nabilah Mohd; Tomari, Razali; Zakaria, Wan Nurshazwani Wan; Othman, Nurmiza

    2017-09-01

    In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.

  15. The Segmental Morphometric Properties of the Horse Cervical Spinal Cord: A Study of Cadaver

    PubMed Central

    Bahar, Sadullah; Bolat, Durmus; Selcuk, Muhammet Lutfi

    2013-01-01

    Although the cervical spinal cord (CSC) of the horse has particular importance in diseases of CNS, there is very little information about its segmental morphometry. The objective of the present study was to determine the morphometric features of the CSC segments in the horse and possible relationships among the morphometric features. The segmented CSC from five mature animals was used. Length, weight, diameter, and volume measurements of the segments were performed macroscopically. Lengths and diameters of segments were measured histologically, and area and volume measurements were performed using stereological methods. The length, weight, and volume of the CSC were 61.6 ± 3.2 cm, 107.2 ± 10.4 g, and 95.5 ± 8.3 cm3, respectively. The length of the segments was increased from C 1 to C 3, while it decreased from C 3 to C 8. The gross section (GS), white matter (WM), grey matter (GM), dorsal horn (DH), and ventral horn (VH) had the largest cross-section areas at C 8. The highest volume was found for the total segment and WM at C 4, GM, DH, and VH at C 7, and the central canal (CC) at C 3. The data obtained not only contribute to the knowledge of the normal anatomy of the CSC but may also provide reference data for veterinary pathologists and clinicians. PMID:23476145

  16. 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

  17. Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines

    PubMed Central

    Kurç, Tahsin M.; Taveira, Luís F. R.; Melo, Alba C. M. A.; Gao, Yi; Kong, Jun; Saltz, Joel H.

    2017-01-01

    Abstract Motivation: Sensitivity analysis and parameter tuning are important processes in large-scale image analysis. They are very costly because the image analysis workflows are required to be executed several times to systematically correlate output variations with parameter changes or to tune parameters. An integrated solution with minimum user interaction that uses effective methodologies and high performance computing is required to scale these studies to large imaging datasets and expensive analysis workflows. Results: The experiments with two segmentation workflows show that the proposed approach can (i) quickly identify and prune parameters that are non-influential; (ii) search a small fraction (about 100 points) of the parameter search space with billions to trillions of points and improve the quality of segmentation results (Dice and Jaccard metrics) by as much as 1.42× compared to the results from the default parameters; (iii) attain good scalability on a high performance cluster with several effective optimizations. Conclusions: Our work demonstrates the feasibility of performing sensitivity analyses, parameter studies and auto-tuning with large datasets. The proposed framework can enable the quantification of error estimations and output variations in image segmentation pipelines. Availability and Implementation: Source code: https://github.com/SBU-BMI/region-templates/. Contact: teodoro@unb.br Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28062445

  18. [The Role of Segmental Analysis of Clonazepam in Hair in Drug Facilitated Cases].

    PubMed

    Chen, H; Xiang, P; Shen, M

    2017-06-01

    To infer the frequency of dosage and medication history investigate of the victims in drug facilitated cases by the segmental analysis of clonazepam in hair. Freezing milling under liquid nitrogen environment combined with ultrasonic bath was used as sample pretreatment in this study, and liquid chromatography-tandem mass spectrometry was used for the segmental analysis of the hair samples collected from 6 victims in different cases. The concentrations of clonazepam and 7-aminoclonazepam were detected in each hair section. Clonazepam and its metabolite 7-aminoclonazepam were detected in parts of hair sections from the 6 victims. The occurrence time of drug peak concentration was consistent with the intake timing provided by victims. Segmental analysis of hair can provide the information of frequency of dosage and intake timing, which shows an unique evidential value in drug facilitated crimes. Copyright© by the Editorial Department of Journal of Forensic Medicine

  19. LANDSAT-D program. Volume 2: Ground segment

    NASA Technical Reports Server (NTRS)

    1984-01-01

    Raw digital data, as received from the LANDSAT spacecraft, cannot generate images that meet specifications. Radiometric corrections must be made to compensate for aging and for differences in sensitivity among the instrument sensors. Geometric corrections must be made to compensate for off-nadir look angle, and to calculate spacecraft drift from its prescribed path. Corrections must also be made for look-angle jitter caused by vibrations induced by spacecraft equipment. The major components of the LANDSAT ground segment and their functions are discussed.

  20. Pancreas segmentation from 3D abdominal CT images using patient-specific weighted subspatial probabilistic atlases

    NASA Astrophysics Data System (ADS)

    Karasawa, Kenichi; Oda, Masahiro; Hayashi, Yuichiro; Nimura, Yukitaka; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Rueckert, Daniel; Mori, Kensaku

    2015-03-01

    Abdominal organ segmentations from CT volumes are now widely used in the computer-aided diagnosis and surgery assistance systems. Among abdominal organs, the pancreas is especially difficult to segment because of its large individual differences of the shape and position. In this paper, we propose a new pancreas segmentation method from 3D abdominal CT volumes using patient-specific weighted-subspatial probabilistic atlases. First of all, we perform normalization of organ shapes in training volumes and an input volume. We extract the Volume Of Interest (VOI) of the pancreas from the training volumes and an input volume. We divide each training VOI and input VOI into some cubic regions. We use a nonrigid registration method to register these cubic regions of the training VOI to corresponding regions of the input VOI. Based on the registration results, we calculate similarities between each cubic region of the training VOI and corresponding region of the input VOI. We select cubic regions of training volumes having the top N similarities in each cubic region. We subspatially construct probabilistic atlases weighted by the similarities in each cubic region. After integrating these probabilistic atlases in cubic regions into one, we perform a rough-to-precise segmentation of the pancreas using the atlas. The results of the experiments showed that utilization of the training volumes having the top N similarities in each cubic region led good results of the pancreas segmentation. The Jaccard Index and the average surface distance of the result were 58.9% and 2.04mm on average, respectively.

  1. Automated measurements of metabolic tumor volume and metabolic parameters in lung PET/CT imaging

    NASA Astrophysics Data System (ADS)

    Orologas, F.; Saitis, P.; Kallergi, M.

    2017-11-01

    Patients with lung tumors or inflammatory lung disease could greatly benefit in terms of treatment and follow-up by PET/CT quantitative imaging, namely measurements of metabolic tumor volume (MTV), standardized uptake values (SUVs) and total lesion glycolysis (TLG). The purpose of this study was the development of an unsupervised or partially supervised algorithm using standard image processing tools for measuring MTV, SUV, and TLG from lung PET/CT scans. Automated metabolic lesion volume and metabolic parameter measurements were achieved through a 5 step algorithm: (i) The segmentation of the lung areas on the CT slices, (ii) the registration of the CT segmented lung regions on the PET images to define the anatomical boundaries of the lungs on the functional data, (iii) the segmentation of the regions of interest (ROIs) on the PET images based on adaptive thresholding and clinical criteria, (iv) the estimation of the number of pixels and pixel intensities in the PET slices of the segmented ROIs, (v) the estimation of MTV, SUVs, and TLG from the previous step and DICOM header data. Whole body PET/CT scans of patients with sarcoidosis were used for training and testing the algorithm. Lung area segmentation on the CT slices was better achieved with semi-supervised techniques that reduced false positive detections significantly. Lung segmentation results agreed with the lung volumes published in the literature while the agreement between experts and algorithm in the segmentation of the lesions was around 88%. Segmentation results depended on the image resolution selected for processing. The clinical parameters, SUV (either mean or max or peak) and TLG estimated by the segmented ROIs and DICOM header data provided a way to correlate imaging data to clinical and demographic data. In conclusion, automated MTV, SUV, and TLG measurements offer powerful analysis tools in PET/CT imaging of the lungs. Custom-made algorithms are often a better approach than the manufacturer

  2. Analysis of cerebral vessels dynamics using experimental data with missed segments

    NASA Astrophysics Data System (ADS)

    Pavlova, O. N.; Abdurashitov, A. S.; Ulanova, M. V.; Shihalov, G. M.; Semyachkina-Glushkovskaya, O. V.; Pavlov, A. N.

    2018-04-01

    Physiological signals often contain various bad segments that occur due to artifacts, failures of the recording equipment or varying experimental conditions. The related experimental data need to be preprocessed to avoid such parts of recordings. In the case of few bad segments, they can simply be removed from the signal and its analysis is further performed. However, when there are many extracted segments, the internal structure of the analyzed physiological process may be destroyed, and it is unclear whether such signal can be used in diagnostic-related studies. In this paper we address this problem for the case of cerebral vessels dynamics. We perform analysis of simulated data in order to reveal general features of quantifying scaling features of complex signals with distinct correlation properties and show that the effects of data loss are significantly different for experimental data with long-range correlations and anti-correlations. We conclude that the cerebral vessels dynamics is significantly less sensitive to missed data fragments as compared with signals with anti-correlated statistics.

  3. Preliminary Analysis of Effect of Random Segment Errors on Coronagraph Performance

    NASA Technical Reports Server (NTRS)

    Stahl, Mark T.; Shaklan, Stuart B.; Stahl, H. Philip

    2015-01-01

    Are we alone in the Universe is probably the most compelling science question of our generation. To answer it requires a large aperture telescope with extreme wavefront stability. To image and characterize Earth-like planets requires the ability to block 10(exp 10) of the host stars light with a 10(exp -11) stability. For an internal coronagraph, this requires correcting wavefront errors and keeping that correction stable to a few picometers rms for the duration of the science observation. This requirement places severe specifications upon the performance of the observatory, telescope and primary mirror. A key task of the AMTD project (initiated in FY12) is to define telescope level specifications traceable to science requirements and flow those specifications to the primary mirror. From a systems perspective, probably the most important question is: What is the telescope wavefront stability specification? Previously, we suggested this specification should be 10 picometers per 10 minutes; considered issues of how this specification relates to architecture, i.e. monolithic or segmented primary mirror; and asked whether it was better to have few or many segmented. This paper reviews the 10 picometers per 10 minutes specification; provides analysis related to the application of this specification to segmented apertures; and suggests that a 3 or 4 ring segmented aperture is more sensitive to segment rigid body motion that an aperture with fewer or more segments.

  4. Robust hepatic vessel segmentation using multi deep convolution network

    NASA Astrophysics Data System (ADS)

    Kitrungrotsakul, Titinunt; Han, Xian-Hua; Iwamoto, Yutaro; Foruzan, Amir Hossein; Lin, Lanfen; Chen, Yen-Wei

    2017-03-01

    Extraction of blood vessels of the organ is a challenging task in the area of medical image processing. It is really difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the hepatic vessels from computed tomography (CT) image. We proposed novel deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of three deep convolution neural networks to extract features from difference planes of CT data. The three networks have share features at the first convolution layer but will separately learn their own features in the second layer. All three networks will join again at the top layer. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 12 CT volumes which training data are randomly generate from 5 CT volumes and 7 using for test. Our network can yield an average dice coefficient 0.830, while 3D deep convolution neural network can yield around 0.7 and multi-scale can yield only 0.6.

  5. Incorporation of physical constraints in optimal surface search for renal cortex segmentation

    NASA Astrophysics Data System (ADS)

    Li, Xiuli; Chen, Xinjian; Yao, Jianhua; Zhang, Xing; Tian, Jie

    2012-02-01

    In this paper, we propose a novel approach for multiple surfaces segmentation based on the incorporation of physical constraints in optimal surface searching. We apply our new approach to solve the renal cortex segmentation problem, an important but not sufficiently researched issue. In this study, in order to better restrain the intensity proximity of the renal cortex and renal column, we extend the optimal surface search approach to allow for varying sampling distance and physical separation constraints, instead of the traditional fixed sampling distance and numerical separation constraints. The sampling distance of each vertex-column is computed according to the sparsity of the local triangular mesh. Then the physical constraint learned from a priori renal cortex thickness is applied to the inter-surface arcs as the separation constraints. Appropriate varying sampling distance and separation constraints were learnt from 6 clinical CT images. After training, the proposed approach was tested on a test set of 10 images. The manual segmentation of renal cortex was used as the reference standard. Quantitative analysis of the segmented renal cortex indicates that overall segmentation accuracy was increased after introducing the varying sampling distance and physical separation constraints (the average true positive volume fraction (TPVF) and false positive volume fraction (FPVF) were 83.96% and 2.80%, respectively, by using varying sampling distance and physical separation constraints compared to 74.10% and 0.18%, respectively, by using fixed sampling distance and numerical separation constraints). The experimental results demonstrated the effectiveness of the proposed approach.

  6. Segmental hair analysis in order to evaluate driving performance.

    PubMed

    Stramesi, C; Polla, M; Vignali, C; Zucchella, A; Groppi, A

    2008-03-21

    On the 31st of July 2002 the Lombardy local government issued a memorandum, C.R. 35/SAN, providing "guidelines to investigate drugs of abuse addiction in order to judge driving performance". About hair samples, this memorandum advises that the proximal lock of 6 cm-length would be analysed for opiates, cocaine, cannabinoids, amphetamine and derivatives, divided into two segments of 3 cm each. The Local Medical Driving Licence Commissions (CML) can decide whether or not to enforce these instructions; from our survey it resulted that most CMLs do not abide by the memorandum, not requiring segmental analysis. The purpose of our study was to verify whether this procedural discordance could affect analytical results and, consequently, the evaluation of the subject's driving performance. We analysed hair samples taken from subjects who were requesting the renewal of their driving licence in our Laboratory during the period from 1 August 2002 to 31 December 2006. We divided samples into two groups: (1) samples previously analysed in one single segment which resulted positive for at least one analyte, but under the cut-off (0.5 ng/mg), were re-analysed in accordance with the guidelines; (2) samples previously processed following guidelines which resulted positive in one of the segments were newly analysed in a single segment. Comparing the new results with the original ones, an increase of positive results emerged in the first group. The second set of results fully supported the first ones. These results underscore the importance of the 35/SAN memorandum, so if the guidelines had been followed there would have been a larger amount of driving licence renewal denied.

  7. Comparison of automatic and visual methods used for image segmentation in Endodontics: a microCT study.

    PubMed

    Queiroz, Polyane Mazucatto; Rovaris, Karla; Santaella, Gustavo Machado; Haiter-Neto, Francisco; Freitas, Deborah Queiroz

    2017-01-01

    To calculate root canal volume and surface area in microCT images, an image segmentation by selecting threshold values is required, which can be determined by visual or automatic methods. Visual determination is influenced by the operator's visual acuity, while the automatic method is done entirely by computer algorithms. To compare between visual and automatic segmentation, and to determine the influence of the operator's visual acuity on the reproducibility of root canal volume and area measurements. Images from 31 extracted human anterior teeth were scanned with a μCT scanner. Three experienced examiners performed visual image segmentation, and threshold values were recorded. Automatic segmentation was done using the "Automatic Threshold Tool" available in the dedicated software provided by the scanner's manufacturer. Volume and area measurements were performed using the threshold values determined both visually and automatically. The paired Student's t-test showed no significant difference between visual and automatic segmentation methods regarding root canal volume measurements (p=0.93) and root canal surface (p=0.79). Although visual and automatic segmentation methods can be used to determine the threshold and calculate root canal volume and surface, the automatic method may be the most suitable for ensuring the reproducibility of threshold determination.

  8. A spatiotemporal-based scheme for efficient registration-based segmentation of thoracic 4-D MRI.

    PubMed

    Yang, Y; Van Reeth, E; Poh, C L; Tan, C H; Tham, I W K

    2014-05-01

    Dynamic three-dimensional (3-D) (four-dimensional, 4-D) magnetic resonance (MR) imaging is gaining importance in the study of pulmonary motion for respiratory diseases and pulmonary tumor motion for radiotherapy. To perform quantitative analysis using 4-D MR images, segmentation of anatomical structures such as the lung and pulmonary tumor is required. Manual segmentation of entire thoracic 4-D MRI data that typically contains many 3-D volumes acquired over several breathing cycles is extremely tedious, time consuming, and suffers high user variability. This requires the development of new automated segmentation schemes for 4-D MRI data segmentation. Registration-based segmentation technique that uses automatic registration methods for segmentation has been shown to be an accurate method to segment structures for 4-D data series. However, directly applying registration-based segmentation to segment 4-D MRI series lacks efficiency. Here we propose an automated 4-D registration-based segmentation scheme that is based on spatiotemporal information for the segmentation of thoracic 4-D MR lung images. The proposed scheme saved up to 95% of computation amount while achieving comparable accurate segmentations compared to directly applying registration-based segmentation to 4-D dataset. The scheme facilitates rapid 3-D/4-D visualization of the lung and tumor motion and potentially the tracking of tumor during radiation delivery.

  9. Segmented Poincaré plot analysis for risk stratification in patients with dilated cardiomyopathy.

    PubMed

    Voss, A; Fischer, C; Schroeder, R; Figulla, H R; Goernig, M

    2010-01-01

    The prognostic value of heart rate variability in patients with dilated cardiomyopathy (DCM) is limited and does not contribute to risk stratification although the dynamics of ventricular repolarization differs considerably between DCM patients and healthy subjects. Neither linear nor nonlinear methods of heart rate variability analysis could discriminate between patients at high and low risk for sudden cardiac death. The aim of this study was to analyze the suitability of the new developed segmented Poincaré plot analysis (SPPA) to enhance risk stratification in DCM. In contrast to the usual applied Poincaré plot analysis the SPPA retains nonlinear features from investigated beat-to-beat interval time series. Main features of SPPA are the rotation of cloud of points and their succeeded variability depended segmentation. Significant row and column probabilities were calculated from the segments and led to discrimination (up to p<0.005) between low and high risk in DCM patients. For the first time an index from Poincaré plot analysis of heart rate variability was able to contribute to risk stratification in patients suffering from DCM.

  10. Three-Dimensions Segmentation of Pulmonary Vascular Trees for Low Dose CT Scans

    NASA Astrophysics Data System (ADS)

    Lai, Jun; Huang, Ying; Wang, Ying; Wang, Jun

    2016-12-01

    Due to the low contrast and the partial volume effects, providing an accurate and in vivo analysis for pulmonary vascular trees from low dose CT scans is a challenging task. This paper proposes an automatic integration segmentation approach for the vascular trees in low dose CT scans. It consists of the following steps: firstly, lung volumes are acquired by the knowledge based method from the CT scans, and then the data are smoothed by the 3D Gaussian filter; secondly, two or three seeds are gotten by the adaptive 2D segmentation and the maximum area selecting from different position scans; thirdly, each seed as the start voxel is inputted for a quick multi-seeds 3D region growing to get vascular trees; finally, the trees are refined by the smooth filter. Through skeleton analyzing for the vascular trees, the results show that the proposed method can provide much better and lower level vascular branches.

  11. SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

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

    Verburg, E; Waard, SN de; Veldhuis, WB

    Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selectedmore » participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.« less

  12. Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation

    NASA Astrophysics Data System (ADS)

    Irshad, Mehreen; Muhammad, Nazeer; Sharif, Muhammad; Yasmeen, Mussarat

    2018-04-01

    Conventionally, cardiac MR image analysis is done manually. Automatic examination for analyzing images can replace the monotonous tasks of massive amounts of data to analyze the global and regional functions of the cardiac left ventricle (LV). This task is performed using MR images to calculate the analytic cardiac parameter like end-systolic volume, end-diastolic volume, ejection fraction, and myocardial mass, respectively. These analytic parameters depend upon genuine delineation of epicardial, endocardial, papillary muscle, and trabeculations contours. In this paper, we propose an automatic segmentation method using the sum of absolute differences technique to localize the left ventricle. Blind morphological operations are proposed to segment and detect the LV contours of the epicardium and endocardium, automatically. We test the benchmark Sunny Brook dataset for evaluation of the proposed work. Contours of epicardium and endocardium are compared quantitatively to determine contour's accuracy and observe high matching values. Similarity or overlapping of an automatic examination to the given ground truth analysis by an expert are observed with high accuracy as with an index value of 91.30% . The proposed method for automatic segmentation gives better performance relative to existing techniques in terms of accuracy.

  13. Segmentation and Quantitative Analysis of Epithelial Tissues.

    PubMed

    Aigouy, Benoit; Umetsu, Daiki; Eaton, Suzanne

    2016-01-01

    Epithelia are tissues that regulate exchanges with the environment. They are very dynamic and can acquire virtually any shape; at the cellular level, they are composed of cells tightly connected by junctions. Most often epithelia are amenable to live imaging; however, the large number of cells composing an epithelium and the absence of informatics tools dedicated to epithelial analysis largely prevented tissue scale studies. Here we present Tissue Analyzer, a free tool that can be used to segment and analyze epithelial cells and monitor tissue dynamics.

  14. 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.

  15. Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets

    PubMed Central

    Jeong, Won-Ki; Beyer, Johanna; Hadwiger, Markus; Vazquez, Amelio; Pfister, Hanspeter; Whitaker, Ross T.

    2011-01-01

    Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes. PMID:19834227

  16. Survey of contemporary trends in color image segmentation

    NASA Astrophysics Data System (ADS)

    Vantaram, Sreenath Rao; Saber, Eli

    2012-10-01

    In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

  17. A semi-automatic method for left ventricle volume estimate: an in vivo validation study

    NASA Technical Reports Server (NTRS)

    Corsi, C.; Lamberti, C.; Sarti, A.; Saracino, G.; Shiota, T.; Thomas, J. D.

    2001-01-01

    This study aims to the validation of the left ventricular (LV) volume estimates obtained by processing volumetric data utilizing a segmentation model based on level set technique. The validation has been performed by comparing real-time volumetric echo data (RT3DE) and magnetic resonance (MRI) data. A validation protocol has been defined. The validation protocol was applied to twenty-four estimates (range 61-467 ml) obtained from normal and pathologic subjects, which underwent both RT3DE and MRI. A statistical analysis was performed on each estimate and on clinical parameters as stroke volume (SV) and ejection fraction (EF). Assuming MRI estimates (x) as a reference, an excellent correlation was found with volume measured by utilizing the segmentation procedure (y) (y=0.89x + 13.78, r=0.98). The mean error on SV was 8 ml and the mean error on EF was 2%. This study demonstrated that the segmentation technique is reliably applicable on human hearts in clinical practice.

  18. Mimicking human expert interpretation of remotely sensed raster imagery by using a novel segmentation analysis within ArcGIS

    NASA Astrophysics Data System (ADS)

    Le Bas, Tim; Scarth, Anthony; Bunting, Peter

    2015-04-01

    Traditional computer based methods for the interpretation of remotely sensed imagery use each pixel individually or the average of a small window of pixels to calculate a class or thematic value, which provides an interpretation. However when a human expert interprets imagery, the human eye is excellent at finding coherent and homogenous areas and edge features. It may therefore be advantageous for computer analysis to mimic human interpretation. A new toolbox for ArcGIS 10.x will be presented that segments the data layers into a set of polygons. Each polygon is defined by a K-means clustering and region growing algorithm, thus finding areas, their edges and any lineations in the imagery. Attached to each polygon are the characteristics of the imagery such as mean and standard deviation of the pixel values, within the polygon. The segmentation of imagery into a jigsaw of polygons also has the advantage that the human interpreter does not need to spend hours digitising the boundaries. The segmentation process has been taken from the RSGIS library of analysis and classification routines (Bunting et al., 2014). These routines are freeware and have been modified to be available in the ArcToolbox under the Windows (v7) operating system. Input to the segmentation process is a multi-layered raster image, for example; a Landsat image, or a set of raster datasets made up from derivatives of topography. The size and number of polygons are set by the user and are dependent on the imagery used. Examples will be presented of data from the marine environment utilising bathymetric depth, slope, rugosity and backscatter from a multibeam system. Meaningful classification of the polygons using their numerical characteristics is the next goal. Object based image analysis (OBIA) should help this workflow. Fully calibrated imagery systems will allow numerical classification to be translated into more readily understandable terms. Peter Bunting, Daniel Clewley, Richard M. Lucas and Sam

  19. Profiling the different needs and expectations of patients for population-based medicine: a case study using segmentation analysis

    PubMed Central

    2012-01-01

    Background This study illustrates an evidence-based method for the segmentation analysis of patients that could greatly improve the approach to population-based medicine, by filling a gap in the empirical analysis of this topic. Segmentation facilitates individual patient care in the context of the culture, health status, and the health needs of the entire population to which that patient belongs. Because many health systems are engaged in developing better chronic care management initiatives, patient profiles are critical to understanding whether some patients can move toward effective self-management and can play a central role in determining their own care, which fosters a sense of responsibility for their own health. A review of the literature on patient segmentation provided the background for this research. Method First, we conducted a literature review on patient satisfaction and segmentation to build a survey. Then, we performed 3,461 surveys of outpatient services users. The key structures on which the subjects’ perception of outpatient services was based were extrapolated using principal component factor analysis with varimax rotation. After the factor analysis, segmentation was performed through cluster analysis to better analyze the influence of individual attitudes on the results. Results Four segments were identified through factor and cluster analysis: the “unpretentious,” the “informed and supported,” the “experts” and the “advanced” patients. Their policies and managerial implications are outlined. Conclusions With this research, we provide the following: – a method for profiling patients based on common patient satisfaction surveys that is easily replicable in all health systems and contexts; – a proposal for segments based on the results of a broad-based analysis conducted in the Italian National Health System (INHS). Segments represent profiles of patients requiring different strategies for delivering health services. Their

  20. 3D morphometry using automated aortic segmentation in native MR angiography: an alternative to contrast enhanced MRA?

    PubMed

    Müller-Eschner, Matthias; Müller, Tobias; Biesdorf, Andreas; Wörz, Stefan; Rengier, Fabian; Böckler, Dittmar; Kauczor, Hans-Ulrich; Rohr, Karl; von Tengg-Kobligk, Hendrik

    2014-04-01

    Native-MR angiography (N-MRA) is considered an imaging alternative to contrast enhanced MR angiography (CE-MRA) for patients with renal insufficiency. Lower intraluminal contrast in N-MRA often leads to failure of the segmentation process in commercial algorithms. This study introduces an in-house 3D model-based segmentation approach used to compare both sequences by automatic 3D lumen segmentation, allowing for evaluation of differences of aortic lumen diameters as well as differences in length comparing both acquisition techniques at every possible location. Sixteen healthy volunteers underwent 1.5-T-MR Angiography (MRA). For each volunteer, two different MR sequences were performed, CE-MRA: gradient echo Turbo FLASH sequence and N-MRA: respiratory-and-cardiac-gated, T2-weighted 3D SSFP. Datasets were segmented using a 3D model-based ellipse-fitting approach with a single seed point placed manually above the celiac trunk. The segmented volumes were manually cropped from left subclavian artery to celiac trunk to avoid error due to side branches. Diameters, volumes and centerline length were computed for intraindividual comparison. For statistical analysis the Wilcoxon-Signed-Ranked-Test was used. Average centerline length obtained based on N-MRA was 239.0±23.4 mm compared to 238.6±23.5 mm for CE-MRA without significant difference (P=0.877). Average maximum diameter obtained based on N-MRA was 25.7±3.3 mm compared to 24.1±3.2 mm for CE-MRA (P<0.001). In agreement with the difference in diameters, volumes obtained based on N-MRA (100.1±35.4 cm(3)) were consistently and significantly larger compared to CE-MRA (89.2±30.0 cm(3)) (P<0.001). 3D morphometry shows highly similar centerline lengths for N-MRA and CE-MRA, but systematically higher diameters and volumes for N-MRA.

  1. Study of tracking and data acquisition system for the 1990's. Volume 4: TDAS space segment architecture

    NASA Technical Reports Server (NTRS)

    Orr, R. S.

    1984-01-01

    Tracking and data acquisition system (TDAS) requirements, TDAS architectural goals, enhanced TDAS subsystems, constellation and networking options, TDAS spacecraft options, crosslink implementation, baseline TDAS space segment architecture, and treat model development/security analysis are addressed.

  2. Template-based automatic breast segmentation on MRI by excluding the chest region

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

    Lin, Muqing; Chen, Jeon-Hor; Wang, Xiaoyong

    2013-12-15

    Purpose: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. Methods: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary ofmore » the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. Results: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1

  3. A combined learning algorithm for prostate segmentation on 3D CT images.

    PubMed

    Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

    2017-11-01

    Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is

  4. Automatic cortical segmentation in the developing brain.

    PubMed

    Xue, Hui; Srinivasan, Latha; Jiang, Shuzhou; Rutherford, Mary; Edwards, A David; Rueckert, Daniel; Hajnal, Jo V

    2007-01-01

    The segmentation of neonatal cortex from magnetic resonance (MR) images is much more challenging than the segmentation of cortex in adults. The main reason is the inverted contrast between grey matter (GM) and white matter (WM) that occurs when myelination is incomplete. This causes mislabeled partial volume voxels, especially at the interface between GM and cerebrospinal fluid (CSF). We propose a fully automatic cortical segmentation algorithm, detecting these mislabeled voxels using a knowledge-based approach and correcting errors by adjusting local priors to favor the correct classification. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic EM scheme. The segmentation algorithm has been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. Quantitative comparison to the manual segmentation demonstrates good performance of the method (mean Dice similarity: 0.758 +/- 0.037 for GM and 0.794 +/- 0.078 for WM).

  5. Discriminative parameter estimation for random walks segmentation.

    PubMed

    Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

    2013-01-01

    The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

  6. Recurrence interval analysis of trading volumes

    NASA Astrophysics Data System (ADS)

    Ren, Fei; Zhou, Wei-Xing

    2010-06-01

    We study the statistical properties of the recurrence intervals τ between successive trading volumes exceeding a certain threshold q . The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2009. Similar to the recurrence interval distribution of the price returns, the tail of the recurrence interval distribution of the trading volumes follows a power-law scaling, and the results are verified by the goodness-of-fit tests using the Kolmogorov-Smirnov (KS) statistic, the weighted KS statistic and the Cramér-von Mises criterion. The measurements of the conditional probability distribution and the detrended fluctuation function show that both short-term and long-term memory effects exist in the recurrence intervals between trading volumes. We further study the relationship between trading volumes and price returns based on the recurrence interval analysis method. It is found that large trading volumes are more likely to occur following large price returns, and the comovement between trading volumes and price returns is more pronounced for large trading volumes.

  7. Recurrence interval analysis of trading volumes.

    PubMed

    Ren, Fei; Zhou, Wei-Xing

    2010-06-01

    We study the statistical properties of the recurrence intervals τ between successive trading volumes exceeding a certain threshold q. The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2009. Similar to the recurrence interval distribution of the price returns, the tail of the recurrence interval distribution of the trading volumes follows a power-law scaling, and the results are verified by the goodness-of-fit tests using the Kolmogorov-Smirnov (KS) statistic, the weighted KS statistic and the Cramér-von Mises criterion. The measurements of the conditional probability distribution and the detrended fluctuation function show that both short-term and long-term memory effects exist in the recurrence intervals between trading volumes. We further study the relationship between trading volumes and price returns based on the recurrence interval analysis method. It is found that large trading volumes are more likely to occur following large price returns, and the comovement between trading volumes and price returns is more pronounced for large trading volumes.

  8. Performance evaluation of automated segmentation software on optical coherence tomography volume data

    PubMed Central

    Tian, Jing; Varga, Boglarka; Tatrai, Erika; Fanni, Palya; Somfai, Gabor Mark; Smiddy, William E.

    2016-01-01

    Over the past two decades a significant number of OCT segmentation approaches have been proposed in the literature. Each methodology has been conceived for and/or evaluated using specific datasets that do not reflect the complexities of the majority of widely available retinal features observed in clinical settings. In addition, there does not exist an appropriate OCT dataset with ground truth that reflects the realities of everyday retinal features observed in clinical settings. While the need for unbiased performance evaluation of automated segmentation algorithms is obvious, the validation process of segmentation algorithms have been usually performed by comparing with manual labelings from each study and there has been a lack of common ground truth. Therefore, a performance comparison of different algorithms using the same ground truth has never been performed. This paper reviews research-oriented tools for automated segmentation of the retinal tissue on OCT images. It also evaluates and compares the performance of these software tools with a common ground truth. PMID:27159849

  9. A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.

    PubMed

    Kalpathy-Cramer, Jayashree; Zhao, Binsheng; Goldgof, Dmitry; Gu, Yuhua; Wang, Xingwei; Yang, Hao; Tan, Yongqiang; Gillies, Robert; Napel, Sandy

    2016-08-01

    Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

  10. Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM.

    PubMed

    Schlaeger, Sarah; Freitag, Friedemann; Klupp, Elisabeth; Dieckmeyer, Michael; Weidlich, Dominik; Inhuber, Stephanie; Deschauer, Marcus; Schoser, Benedikt; Bublitz, Sarah; Montagnese, Federica; Zimmer, Claus; Rummeny, Ernst J; Karampinos, Dimitrios C; Kirschke, Jan S; Baum, Thomas

    2018-01-01

    Magnetic resonance imaging (MRI) can non-invasively assess muscle anatomy, exercise effects and pathologies with different underlying causes such as neuromuscular diseases (NMD). Quantitative MRI including fat fraction mapping using chemical shift encoding-based water-fat MRI has emerged for reliable determination of muscle volume and fat composition. The data analysis of water-fat images requires segmentation of the different muscles which has been mainly performed manually in the past and is a very time consuming process, currently limiting the clinical applicability. An automatization of the segmentation process would lead to a more time-efficient analysis. In the present work, the manually segmented thigh magnetic resonance imaging database MyoSegmenTUM is presented. It hosts water-fat MR images of both thighs of 15 healthy subjects and 4 patients with NMD with a voxel size of 3.2x2x4 mm3 with the corresponding segmentation masks for four functional muscle groups: quadriceps femoris, sartorius, gracilis, hamstrings. The database is freely accessible online at https://osf.io/svwa7/?view_only=c2c980c17b3a40fca35d088a3cdd83e2. The database is mainly meant as ground truth which can be used as training and test dataset for automatic muscle segmentation algorithms. The segmentation allows extraction of muscle cross sectional area (CSA) and volume. Proton density fat fraction (PDFF) of the defined muscle groups from the corresponding images and quadriceps muscle strength measurements/neurological muscle strength rating can be used for benchmarking purposes.

  11. Local/non-local regularized image segmentation using graph-cuts: application to dynamic and multispectral MRI.

    PubMed

    Hanson, Erik A; Lundervold, Arvid

    2013-11-01

    Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested. Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations. The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes. The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.

  12. Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

    PubMed

    Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

    2016-01-01

    To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

  13. A coarse-to-fine approach for pericardial effusion localization and segmentation in chest CT scans

    NASA Astrophysics Data System (ADS)

    Liu, Jiamin; Chellamuthu, Karthik; Lu, Le; Bagheri, Mohammadhadi; Summers, Ronald M.

    2018-02-01

    Pericardial effusion on CT scans demonstrates very high shape and volume variability and very low contrast to adjacent structures. This inhibits traditional automated segmentation methods from achieving high accuracies. Deep neural networks have been widely used for image segmentation in CT scans. In this work, we present a two-stage method for pericardial effusion localization and segmentation. For the first step, we localize the pericardial area from the entire CT volume, providing a reliable bounding box for the more refined segmentation step. A coarse-scaled holistically-nested convolutional networks (HNN) model is trained on entire CT volume. The resulting HNN per-pixel probability maps are then threshold to produce a bounding box covering the pericardial area. For the second step, a fine-scaled HNN model is trained only on the bounding box region for effusion segmentation to reduce the background distraction. Quantitative evaluation is performed on a dataset of 25 CT scans of patient (1206 images) with pericardial effusion. The segmentation accuracy of our two-stage method, measured by Dice Similarity Coefficient (DSC), is 75.59+/-12.04%, which is significantly better than the segmentation accuracy (62.74+/-15.20%) of only using the coarse-scaled HNN model.

  14. New Software for Market Segmentation Analysis: A Chi-Square Interaction Detector. AIR 1983 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Lay, Robert S.

    The advantages and disadvantages of new software for market segmentation analysis are discussed, and the application of this new, chi-square based procedure (CHAID), is illustrated. A comparison is presented of an earlier, binary segmentation technique (THAID) and a multiple discriminant analysis. It is suggested that CHAID is superior to earlier…

  15. Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan

    NASA Astrophysics Data System (ADS)

    Keihaninejad, Shiva; Heckemann, Rolf A.; Gousias, Ioannis S.; Rueckert, Daniel; Aljabar, Paul; Hajnal, Joseph V.; Hammers, Alexander

    2009-02-01

    A robust model for the automatic segmentation of human brain images into anatomically defined regions across the human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related changes. We have developed a new method, based on established algorithms for automatic segmentation of young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into 83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases was registered to each target MR image. By using additional information from segmentation into tissue classes (GM, WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example linear correlation coefficients with age were, for lateral ventricular volume, rmale=0.76, rfemale=0.58 and, for hippocampal volume, rmale=-0.6, rfemale=-0.4 (allρ<0.01).

  16. Calibration of a semi-automated segmenting method for quantification of adipose tissue compartments from magnetic resonance images of mice.

    PubMed

    Garteiser, Philippe; Doblas, Sabrina; Towner, Rheal A; Griffin, Timothy M

    2013-11-01

    To use an automated water-suppressed magnetic resonance imaging (MRI) method to objectively assess adipose tissue (AT) volumes in whole body and specific regional body components (subcutaneous, thoracic and peritoneal) of obese and lean mice. Water-suppressed MR images were obtained on a 7T, horizontal-bore MRI system in whole bodies (excluding head) of 26 week old male C57BL6J mice fed a control (10% kcal fat) or high-fat diet (60% kcal fat) for 20 weeks. Manual (outlined regions) versus automated (Gaussian fitting applied to threshold-weighted images) segmentation procedures were compared for whole body AT and regional AT volumes (i.e., subcutaneous, thoracic, and peritoneal). The AT automated segmentation method was compared to dual-energy X-ray (DXA) analysis. The average AT volumes for whole body and individual compartments correlated well between the manual outlining and the automated methods (R2>0.77, p<0.05). Subcutaneous, peritoneal, and total body AT volumes were increased 2-3 fold and thoracic AT volume increased more than 5-fold in diet-induced obese mice versus controls (p<0.05). MRI and DXA-based method comparisons were highly correlative (R2=0.94, p<0.0001). Automated AT segmentation of water-suppressed MRI data using a global Gaussian filtering algorithm resulted in a fairly accurate assessment of total and regional AT volumes in a pre-clinical mouse model of obesity. © 2013 Elsevier Inc. All rights reserved.

  17. Brain segmentation and the generation of cortical surfaces

    NASA Technical Reports Server (NTRS)

    Joshi, M.; Cui, J.; Doolittle, K.; Joshi, S.; Van Essen, D.; Wang, L.; Miller, M. I.

    1999-01-01

    This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

  18. Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation

    NASA Astrophysics Data System (ADS)

    Ye, Xujiong; Siddique, Musib; Douiri, Abdel; Beddoe, Gareth; Slabaugh, Greg

    2009-02-01

    Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon.

  19. Lung vessel segmentation in CT images using graph-cuts

    NASA Astrophysics Data System (ADS)

    Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

    2016-03-01

    Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

  20. Knowledge-based segmentation and feature analysis of hand and wrist radiographs

    NASA Astrophysics Data System (ADS)

    Efford, Nicholas D.

    1993-07-01

    The segmentation of hand and wrist radiographs for applications such as skeletal maturity assessment is best achieved by model-driven approaches incorporating anatomical knowledge. The reasons for this are discussed, and a particular frame-based or 'blackboard' strategy for the simultaneous segmentation of the hand and estimation of bone age via the TW2 method is described. The new approach is structured for optimum robustness and computational efficiency: features of interest are detected and analyzes in order of their size and prominence in the image, the largest and most distinctive being dealt with first, and the evidence generated by feature analysis is used to update a model of hand anatomy and hence guide later stages of the segmentation. Closed bone boundaries are formed by a hybrid technique combining knowledge-based, one-dimensional edge detection with model-assisted heuristic tree searching.

  1. Test-retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer's disease populations.

    PubMed

    Worker, Amanda; Dima, Danai; Combes, Anna; Crum, William R; Streffer, Johannes; Einstein, Steven; Mehta, Mitul A; Barker, Gareth J; C R Williams, Steve; O'daly, Owen

    2018-04-01

    The hippocampal formation is a complex brain structure that is important in cognitive processes such as memory, mood, reward processing and other executive functions. Histological and neuroimaging studies have implicated the hippocampal region in neuropsychiatric disorders as well as in neurodegenerative diseases. This highly plastic limbic region is made up of several subregions that are believed to have different functional roles. Therefore, there is a growing interest in imaging the subregions of the hippocampal formation rather than modelling the hippocampus as a homogenous structure, driving the development of new automated analysis tools. Consequently, there is a pressing need to understand the stability of the measures derived from these new techniques. In this study, an automated hippocampal subregion segmentation pipeline, released as a developmental version of Freesurfer (v6.0), was applied to T1-weighted magnetic resonance imaging (MRI) scans of 22 healthy older participants, scanned on 3 separate occasions and a separate longitudinal dataset of 40 Alzheimer's disease (AD) patients. Test-retest reliability of hippocampal subregion volumes was assessed using the intra-class correlation coefficient (ICC), percentage volume difference and percentage volume overlap (Dice). Sensitivity of the regional estimates to longitudinal change was estimated using linear mixed effects (LME) modelling. The results show that out of the 24 hippocampal subregions, 20 had ICC scores of 0.9 or higher in both samples; these regions include the molecular layer, granule cell layer of the dentate gyrus, CA1, CA3 and the subiculum (ICC > 0.9), whilst the hippocampal fissure and fimbria had lower ICC scores (0.73-0.88). Furthermore, LME analysis of the independent AD dataset demonstrated sensitivity to group and individual differences in the rate of volume change over time in several hippocampal subregions (CA1, molecular layer, CA3, hippocampal tail, fissure and presubiculum

  2. Mining volume measurement system

    NASA Technical Reports Server (NTRS)

    Heyman, Joseph Saul (Inventor)

    1988-01-01

    In a shaft with a curved or straight primary segment and smaller off-shooting segments, at least one standing wave is generated in the primary segment. The shaft has either an open end or a closed end and approximates a cylindrical waveguide. A frequency of a standing wave that represents the fundamental mode characteristic of the primary segment can be measured. Alternatively, a frequency differential between two successive harmonic modes that are characteristic of the primary segment can be measured. In either event, the measured frequency or frequency differential is characteristic of the length and thus the volume of the shaft based on length times the bore area.

  3. MPCV Exercise Operational Volume Analysis

    NASA Technical Reports Server (NTRS)

    Godfrey, A.; Humphreys, B.; Funk, J.; Perusek, G.; Lewandowski, B. E.

    2017-01-01

    In order to minimize the loss of bone and muscle mass during spaceflight, the Multi-purpose Crew Vehicle (MPCV) will include an exercise device and enough free space within the cabin for astronauts to use the device effectively. The NASA Digital Astronaut Project (DAP) has been tasked with using computational modeling to aid in determining whether or not the available operational volume is sufficient for in-flight exercise.Motion capture data was acquired using a 12-camera Smart DX system (BTS Bioengineering, Brooklyn, NY), while exercisers performed 9 resistive exercises without volume restrictions in a 1g environment. Data were collected from two male subjects, one being in the 99th percentile of height and the other in the 50th percentile of height, using between 25 and 60 motion capture markers. Motion capture data was also recorded as a third subject, also near the 50th percentile in height, performed aerobic rowing during a parabolic flight. A motion capture system and algorithms developed previously and presented at last years HRP-IWS were utilized to collect and process the data from the parabolic flight [1]. These motions were applied to a scaled version of a biomechanical model within the biomechanical modeling software OpenSim [2], and the volume sweeps of the motions were visually assessed against an imported CAD model of the operational volume. Further numerical analysis was performed using Matlab (Mathworks, Natick, MA) and the OpenSim API. This analysis determined the location of every marker in space over the duration of the exercise motion, and the distance of each marker to the nearest surface of the volume. Containment of the exercise motions within the operational volume was determined on a per-exercise and per-subject basis. The orientation of the exerciser and the angle of the footplate were two important factors upon which containment was dependent. Regions where the exercise motion exceeds the bounds of the operational volume have been

  4. Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's Disease.

    PubMed

    Johnson, Eileanoir B; Gregory, Sarah; Johnson, Hans J; Durr, Alexandra; Leavitt, Blair R; Roos, Raymund A; Rees, Geraint; Tabrizi, Sarah J; Scahill, Rachael I

    2017-01-01

    The selection of an appropriate segmentation tool is a challenge facing any researcher aiming to measure gray matter (GM) volume. Many tools have been compared, yet there is currently no method that can be recommended above all others; in particular, there is a lack of validation in disease cohorts. This work utilizes a clinical dataset to conduct an extensive comparison of segmentation tools. Our results confirm that all tools have advantages and disadvantages, and we present a series of considerations that may be of use when selecting a GM segmentation method, rather than a ranking of these tools. Seven segmentation tools were compared using 3 T MRI data from 20 controls, 40 premanifest Huntington's disease (HD), and 40 early HD participants. Segmented volumes underwent detailed visual quality control. Reliability and repeatability of total, cortical, and lobular GM were investigated in repeated baseline scans. The relationship between each tool was also examined. Longitudinal within-group change over 3 years was assessed via generalized least squares regression to determine sensitivity of each tool to disease effects. Visual quality control and raw volumes highlighted large variability between tools, especially in occipital and temporal regions. Most tools showed reliable performance and the volumes were generally correlated. Results for longitudinal within-group change varied between tools, especially within lobular regions. These differences highlight the need for careful selection of segmentation methods in clinical neuroimaging studies. This guide acts as a primer aimed at the novice or non-technical imaging scientist providing recommendations for the selection of cohort-appropriate GM segmentation software.

  5. Application of taxonomy theory, Volume 1: Computing a Hopf bifurcation-related segment of the feasibility boundary. Final report

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

    Zaborszky, J.; Venkatasubramanian, V.

    1995-10-01

    Taxonomy Theory is the first precise comprehensive theory for large power system dynamics modeled in any detail. The motivation for this project is to show that it can be used, practically, for analyzing a disturbance that actually occurred on a large system, which affected a sizable portion of the Midwest with supercritical Hopf type oscillations. This event is well documented and studied. The report first summarizes Taxonomy Theory with an engineering flavor. Then various computational approaches are sighted and analyzed for desirability to use with Taxonomy Theory. Then working equations are developed for computing a segment of the feasibility boundarymore » that bounds the region of (operating) parameters throughout which the operating point can be moved without losing stability. Then experimental software incorporating large EPRI software packages PSAPAC is developed. After a summary of the events during the subject disturbance, numerous large scale computations, up to 7600 buses, are reported. These results are reduced into graphical and tabular forms, which then are analyzed and discussed. The report is divided into two volumes. This volume illustrates the use of the Taxonomy Theory for computing the feasibility boundary and presents evidence that the event indeed led to a Hopf type oscillation on the system. Furthermore it proves that the Feasibility Theory can indeed be used for practical computation work with very large systems. Volume 2, a separate volume, will show that the disturbance has led to a supercritical (that is stable oscillation) Hopf bifurcation.« less

  6. Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.

    PubMed

    Wang, Quanli; Niemi, Jarad; Tan, Chee-Meng; You, Lingchong; West, Mike

    2010-01-01

    An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.

  7. Ureter tracking and segmentation in CT urography (CTU) using COMPASS

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

    Hadjiiski, Lubomir, E-mail: lhadjisk@umich.edu; Zick, David; Chan, Heang-Ping

    2014-12-15

    Purpose: The authors are developing a computerized system for automated segmentation of ureters in CTU, referred to as combined model-guided path-finding analysis and segmentation system (COMPASS). Ureter segmentation is a critical component for computer-aided diagnosis of ureter cancer. Methods: COMPASS consists of three stages: (1) rule-based adaptive thresholding and region growing, (2) path-finding and propagation, and (3) edge profile extraction and feature analysis. With institutional review board approval, 79 CTU scans performed with intravenous (IV) contrast material enhancement were collected retrospectively from 79 patient files. One hundred twenty-four ureters were selected from the 79 CTU volumes. On average, the uretersmore » spanned 283 computed tomography slices (range: 116–399, median: 301). More than half of the ureters contained malignant or benign lesions and some had ureter wall thickening due to malignancy. A starting point for each of the 124 ureters was identified manually to initialize the tracking by COMPASS. In addition, the centerline of each ureter was manually marked and used as reference standard for evaluation of tracking performance. The performance of COMPASS was quantitatively assessed by estimating the percentage of the length that was successfully tracked and segmented for each ureter and by estimating the average distance and the average maximum distance between the computer and the manually tracked centerlines. Results: Of the 124 ureters, 120 (97%) were segmented completely (100%), 121 (98%) were segmented through at least 70%, and 123 (99%) were segmented through at least 50% of its length. In comparison, using our previous method, 85 (69%) ureters were segmented completely (100%), 100 (81%) were segmented through at least 70%, and 107 (86%) were segmented at least 50% of its length. With COMPASS, the average distance between the computer and the manually generated centerlines is 0.54 mm, and the average maximum distance

  8. Example based lesion segmentation

    NASA Astrophysics Data System (ADS)

    Roy, Snehashis; He, Qing; Carass, Aaron; Jog, Amod; Cuzzocreo, Jennifer L.; Reich, Daniel S.; Prince, Jerry; Pham, Dzung

    2014-03-01

    Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimer's disease or multiple sclerosis. Multi-modal MR images are often used to segment T2 white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.

  9. Association of Attorney Advertising and FDA Action with Prescription Claims: A Time Series Segmented Regression Analysis.

    PubMed

    Tippett, Elizabeth C; Chen, Brian K

    2015-12-01

    Attorneys sponsor television advertisements that include repeated warnings about adverse drug events to solicit consumers for lawsuits against drug manufacturers. The relationship between such advertising, safety actions by the US Food and Drug Administration (FDA), and healthcare use is unknown. To investigate the relationship between attorney advertising, FDA actions, and prescription drug claims. The study examined total users per month and prescription rates for seven drugs with substantial attorney advertising volume and FDA or other safety interventions during 2009. Segmented regression analysis was used to detect pre-intervention trends, post-intervention level changes, and changes in post-intervention trends relative to the pre-intervention trends in the use of these seven drugs, using advertising volume, media hits, and the number of Medicare enrollees as covariates. Data for these variables were obtained from the Center for Medicare and Medicaid Services, Kantar Media, and LexisNexis. Several types of safety actions were associated with reductions in drug users and/or prescription rates, particularly for fentanyl, varenicline, and paroxetine. In most cases, attorney advertising volume rose in conjunction with major safety actions. Attorney advertising volume was positively correlated with prescription rates in five of seven drugs, likely because advertising volume began rising before safety actions, when prescription rates were still increasing. On the other hand, attorney advertising had mixed associations with the number of users per month. Regulatory and safety actions likely reduced the number of users and/or prescription rates for some drugs. Attorneys may have strategically chosen to begin advertising adverse drug events prior to major safety actions, but we found little evidence that attorney advertising reduced drug use. Further research is needed to better understand how consumers and physicians respond to attorney advertising.

  10. Stress distribution pattern of screw-retained restorations with segmented vs. non-segmented abutments: A finite element analysis

    PubMed Central

    Aalaei, Shima; Rajabi Naraki, Zahra; Nematollahi, Fatemeh; Beyabanaki, Elaheh; Shahrokhi Rad, Afsaneh

    2017-01-01

    Background. Screw-retained restorations are favored in some clinical situations such as limited inter-occlusal spaces. This study was designed to compare stresses developed in the peri-implant bone in two different types of screw-retained restorations (segmented vs. non-segmented abutment) using a finite element model. Methods. An implant, 4.1 mm in diameter and 10 mm in length, was placed in the first molar site of a mandibular model with 1 mm of cortical bone on the buccal and lingual sides. Segmented and non-segmented screw abutments with their crowns were placed on the simulated implant in each model. After loading (100 N, axial and 45° non-axial), von Mises stress was recorded using ANSYS software, version 12.0.1. Results. The maximum stresses in the non-segmented abutment screw were less than those of segmented abutment (87 vs. 100, and 375 vs. 430 MPa under axial and non-axial loading, respectively). The maximum stresses in the peri-implant bone for the model with segmented abutment were less than those of non-segmented ones (21 vs. 24 MPa, and 31 vs. 126 MPa under vertical and angular loading, respectively). In addition, the micro-strain of peri-implant bone for the segmented abutment restoration was less than that of non-segmented abutment. Conclusion. Under axial and non-axial loadings, non-segmented abutment showed less stress concentration in the screw, while there was less stress and strain in the peri-implant bone in the segmented abutment. PMID:29184629

  11. Sampling-based ensemble segmentation against inter-operator variability

    NASA Astrophysics Data System (ADS)

    Huo, Jing; Okada, Kazunori; Pope, Whitney; Brown, Matthew

    2011-03-01

    Inconsistency and a lack of reproducibility are commonly associated with semi-automated segmentation methods. In this study, we developed an ensemble approach to improve reproducibility and applied it to glioblastoma multiforme (GBM) brain tumor segmentation on T1-weigted contrast enhanced MR volumes. The proposed approach combines samplingbased simulations and ensemble segmentation into a single framework; it generates a set of segmentations by perturbing user initialization and user-specified internal parameters, then fuses the set of segmentations into a single consensus result. Three combination algorithms were applied: majority voting, averaging and expectation-maximization (EM). The reproducibility of the proposed framework was evaluated by a controlled experiment on 16 tumor cases from a multicenter drug trial. The ensemble framework had significantly better reproducibility than the individual base Otsu thresholding method (p<.001).

  12. Rapid Phenotyping of Root Systems of Brachypodium Plants Using X-ray Computed Tomography: a Comparative Study of Soil Types and Segmentation Tools

    NASA Astrophysics Data System (ADS)

    Varga, T.; McKinney, A. L.; Bingham, E.; Handakumbura, P. P.; Jansson, C.

    2017-12-01

    Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as in processes with important implications to farming and thus human food supply. X-ray computed tomography (XCT) has been proven to be an effective tool for non-invasive root imaging and analysis. Selected Brachypodium distachyon phenotypes were grown in both natural and artificial soil mixes. The specimens were imaged by XCT, and the root architectures were extracted from the data using three different software-based methods; RooTrak, ImageJ-based WEKA segmentation, and the segmentation feature in VG Studio MAX. The 3D root image was successfully segmented at 30 µm resolution by all three methods. In this presentation, ease of segmentation and the accuracy of the extracted quantitative information (root volume and surface area) will be compared between soil types and segmentation methods. The best route to easy and accurate segmentation and root analysis will be highlighted.

  13. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

    NASA Astrophysics Data System (ADS)

    Lassen, B. C.; Jacobs, C.; Kuhnigk, J.-M.; van Ginneken, B.; van Rikxoort, E. M.

    2015-02-01

    The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of

  14. Self-Paced Physics, Segment 18.

    ERIC Educational Resources Information Center

    New York Inst. of Tech., Old Westbury.

    Eighty-seven problems are included in this volume which is arranged to match study segments 2 through 14. The subject matter is related to projectiles, simple harmonic motion, kinetic friction, multiple pulley arrangements, motion on inclined planes, circular motion, potential energy, kinetic energy, center of mass, Newton's laws, elastic and…

  15. Linear test bed. Volume 1: Test bed no. 1. [aerospike test bed with segmented combustor

    NASA Technical Reports Server (NTRS)

    1972-01-01

    The Linear Test Bed program was to design, fabricate, and evaluation test an advanced aerospike test bed which employed the segmented combustor concept. The system is designated as a linear aerospike system and consists of a thrust chamber assembly, a power package, and a thrust frame. It was designed as an experimental system to demonstrate the feasibility of the linear aerospike-segmented combustor concept. The overall dimensions are 120 inches long by 120 inches wide by 96 inches in height. The propellants are liquid oxygen/liquid hydrogen. The system was designed to operate at 1200-psia chamber pressure, at a mixture ratio of 5.5. At the design conditions, the sea level thrust is 200,000 pounds. The complete program including concept selection, design, fabrication, component test, system test, supporting analysis and posttest hardware inspection is described.

  16. TED: A Tolerant Edit Distance for segmentation evaluation.

    PubMed

    Funke, Jan; Klein, Jonas; Moreno-Noguer, Francesc; Cardona, Albert; Cook, Matthew

    2017-02-15

    In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods. Copyright © 2016. Published by Elsevier Inc.

  17. Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

    PubMed Central

    Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

    2013-01-01

    High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

  18. Validity of segmental bioelectrical impedance analysis for estimating fat-free mass in children including overweight individuals.

    PubMed

    Ohta, Megumi; Midorikawa, Taishi; Hikihara, Yuki; Masuo, Yoshihisa; Sakamoto, Shizuo; Torii, Suguru; Kawakami, Yasuo; Fukunaga, Tetsuo; Kanehisa, Hiroaki

    2017-02-01

    This study examined the validity of segmental bioelectrical impedance (BI) analysis for predicting the fat-free masses (FFMs) of whole-body and body segments in children including overweight individuals. The FFM and impedance (Z) values of arms, trunk, legs, and whole body were determined using a dual-energy X-ray absorptiometry and segmental BI analyses, respectively, in 149 boys and girls aged 6 to 12 years, who were divided into model-development (n = 74), cross-validation (n = 35), and overweight (n = 40) groups. Simple regression analysis was applied to (length) 2 /Z (BI index) for each of the whole-body and 3 segments to develop the prediction equations of the measured FFM of the related body part. In the model-development group, the BI index of each of the 3 segments and whole body was significantly correlated to the measured FFM (R 2 = 0.867-0.932, standard error of estimation = 0.18-1.44 kg (5.9%-8.7%)). There was no significant difference between the measured and predicted FFM values without systematic error. The application of each equation derived in the model-development group to the cross-validation and overweight groups did not produce significant differences between the measured and predicted FFM values and systematic errors, with an exception that the arm FFM in the overweight group was overestimated. Segmental bioelectrical impedance analysis is useful for predicting the FFM of each of whole-body and body segments in children including overweight individuals, although the application for estimating arm FFM in overweight individuals requires a certain modification.

  19. Semi-automated segmentation of a glioblastoma multiforme on brain MR images for radiotherapy planning.

    PubMed

    Hori, Daisuke; Katsuragawa, Shigehiko; Murakami, Ryuuji; Hirai, Toshinori

    2010-04-20

    We propose a computerized method for semi-automated segmentation of the gross tumor volume (GTV) of a glioblastoma multiforme (GBM) on brain MR images for radiotherapy planning (RTP). Three-dimensional (3D) MR images of 28 cases with a GBM were used in this study. First, a sphere volume of interest (VOI) including the GBM was selected by clicking a part of the GBM region in the 3D image. Then, the sphere VOI was transformed to a two-dimensional (2D) image by use of a spiral-scanning technique. We employed active contour models (ACM) to delineate an optimal outline of the GBM in the transformed 2D image. After inverse transform of the optimal outline to the 3D space, a morphological filter was applied to smooth the shape of the 3D segmented region. For evaluation of our computerized method, we compared the computer output with manually segmented regions, which were obtained by a therapeutic radiologist using a manual tracking method. In evaluating our segmentation method, we employed the Jaccard similarity coefficient (JSC) and the true segmentation coefficient (TSC) in volumes between the computer output and the manually segmented region. The mean and standard deviation of JSC and TSC were 74.2+/-9.8% and 84.1+/-7.1%, respectively. Our segmentation method provided a relatively accurate outline for GBM and would be useful for radiotherapy planning.

  20. Segmentation and detection of fluorescent 3D spots.

    PubMed

    Ram, Sundaresh; Rodríguez, Jeffrey J; Bosco, Giovanni

    2012-03-01

    The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in-situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top-hat filtering, and intensity thresholding, followed by 3D region-growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots as either true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques. Copyright © 2012 International Society for Advancement of Cytometry.

  1. Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

    PubMed

    Padma, A; Sukanesh, R

    2013-01-01

    A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

  2. A software tool for automatic classification and segmentation of 2D/3D medical images

    NASA Astrophysics Data System (ADS)

    Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

    2013-02-01

    Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

  3. Variational-based segmentation of bio-pores in tomographic images

    NASA Astrophysics Data System (ADS)

    Bauer, Benjamin; Cai, Xiaohao; Peth, Stephan; Schladitz, Katja; Steidl, Gabriele

    2017-01-01

    X-ray computed tomography (CT) combined with a quantitative analysis of the resulting volume images is a fruitful technique in soil science. However, the variations in X-ray attenuation due to different soil components keep the segmentation of single components within these highly heterogeneous samples a challenging problem. Particularly demanding are bio-pores due to their elongated shape and the low gray value difference to the surrounding soil structure. Recently, variational models in connection with algorithms from convex optimization were successfully applied for image segmentation. In this paper we apply these methods for the first time for the segmentation of bio-pores in CT images of soil samples. We introduce a novel convex model which enforces smooth boundaries of bio-pores and takes the varying attenuation values in the depth into account. Segmentation results are reported for different real-world 3D data sets as well as for simulated data. These results are compared with two gray value thresholding methods, namely indicator kriging and a global thresholding procedure, and with a morphological approach. Pros and cons of the methods are assessed by considering geometric features of the segmented bio-pore systems. The variational approach features well-connected smooth pores while not detecting smaller or shallower pores. This is an advantage in cases where the main bio-pores network is of interest and where infillings, e.g., excrements of earthworms, would result in losing pore connections as observed for the other thresholding methods.

  4. Automatic segmentation of invasive breast carcinomas from dynamic contrast-enhanced MRI using time series analysis.

    PubMed

    Jayender, Jagadaeesan; Chikarmane, Sona; Jolesz, Ferenc A; Gombos, Eva

    2014-08-01

    To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast-enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise, and fitting algorithms. We modeled the underlying dynamics of the tumor by an LDS and used the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist's segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared with the radiologist's segmentation and 82.1% accuracy and 100% sensitivity when compared with the CADstream output. The overlap of the algorithm output with the radiologist's segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72, respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC = 0.95. The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI. © 2013 Wiley Periodicals, Inc.

  5. Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington’s Disease

    PubMed Central

    Johnson, Eileanoir B.; Gregory, Sarah; Johnson, Hans J.; Durr, Alexandra; Leavitt, Blair R.; Roos, Raymund A.; Rees, Geraint; Tabrizi, Sarah J.; Scahill, Rachael I.

    2017-01-01

    The selection of an appropriate segmentation tool is a challenge facing any researcher aiming to measure gray matter (GM) volume. Many tools have been compared, yet there is currently no method that can be recommended above all others; in particular, there is a lack of validation in disease cohorts. This work utilizes a clinical dataset to conduct an extensive comparison of segmentation tools. Our results confirm that all tools have advantages and disadvantages, and we present a series of considerations that may be of use when selecting a GM segmentation method, rather than a ranking of these tools. Seven segmentation tools were compared using 3 T MRI data from 20 controls, 40 premanifest Huntington’s disease (HD), and 40 early HD participants. Segmented volumes underwent detailed visual quality control. Reliability and repeatability of total, cortical, and lobular GM were investigated in repeated baseline scans. The relationship between each tool was also examined. Longitudinal within-group change over 3 years was assessed via generalized least squares regression to determine sensitivity of each tool to disease effects. Visual quality control and raw volumes highlighted large variability between tools, especially in occipital and temporal regions. Most tools showed reliable performance and the volumes were generally correlated. Results for longitudinal within-group change varied between tools, especially within lobular regions. These differences highlight the need for careful selection of segmentation methods in clinical neuroimaging studies. This guide acts as a primer aimed at the novice or non-technical imaging scientist providing recommendations for the selection of cohort-appropriate GM segmentation software. PMID:29066997

  6. 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.

  7. A Scalable Framework For Segmenting Magnetic Resonance Images

    PubMed Central

    Hore, Prodip; Goldgof, Dmitry B.; Gu, Yuhua; Maudsley, Andrew A.; Darkazanli, Ammar

    2009-01-01

    A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data. PMID:20046893

  8. Automatic co-segmentation of lung tumor based on random forest in PET-CT images

    NASA Astrophysics Data System (ADS)

    Jiang, Xueqing; Xiang, Dehui; Zhang, Bin; Zhu, Weifang; Shi, Fei; Chen, Xinjian

    2016-03-01

    In this paper, a fully automatic method is proposed to segment the lung tumor in clinical 3D PET-CT images. The proposed method effectively combines PET and CT information to make full use of the high contrast of PET images and superior spatial resolution of CT images. Our approach consists of three main parts: (1) initial segmentation, in which spines are removed in CT images and initial connected regions achieved by thresholding based segmentation in PET images; (2) coarse segmentation, in which monotonic downhill function is applied to rule out structures which have similar standardized uptake values (SUV) to the lung tumor but do not satisfy a monotonic property in PET images; (3) fine segmentation, random forests method is applied to accurately segment the lung tumor by extracting effective features from PET and CT images simultaneously. We validated our algorithm on a dataset which consists of 24 3D PET-CT images from different patients with non-small cell lung cancer (NSCLC). The average TPVF, FPVF and accuracy rate (ACC) were 83.65%, 0.05% and 99.93%, respectively. The correlation analysis shows our segmented lung tumor volumes has strong correlation ( average 0.985) with the ground truth 1 and ground truth 2 labeled by a clinical expert.

  9. Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

    2017-12-01

    The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

  10. Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images.

    PubMed

    Du, Yuncheng; Budman, Hector M; Duever, Thomas A

    2017-06-01

    Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

  11. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images

    PubMed Central

    Jain, Saurabh; Sima, Diana M.; Ribbens, Annemie; Cambron, Melissa; Maertens, Anke; Van Hecke, Wim; De Mey, Johan; Barkhof, Frederik; Steenwijk, Martijn D.; Daams, Marita; Maes, Frederik; Van Huffel, Sabine; Vrenken, Hugo; Smeets, Dirk

    2015-01-01

    The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter

  12. Semi-automatic volume measurement for orbital fat and total extraocular muscles based on Cube FSE-flex sequence in patients with thyroid-associated ophthalmopathy.

    PubMed

    Tang, X; Liu, H; Chen, L; Wang, Q; Luo, B; Xiang, N; He, Y; Zhu, W; Zhang, J

    2018-05-24

    To investigate the accuracy of two semi-automatic segmentation measurements based on magnetic resonance imaging (MRI) three-dimensional (3D) Cube fast spin echo (FSE)-flex sequence in phantoms, and to evaluate the feasibility of determining the volumetric alterations of orbital fat (OF) and total extraocular muscles (TEM) in patients with thyroid-associated ophthalmopathy (TAO) by semi-automatic segmentation. Forty-four fatty (n=22) and lean (n=22) phantoms were scanned by using Cube FSE-flex sequence with a 3 T MRI system. Their volumes were measured by manual segmentation (MS) and two semi-automatic segmentation algorithms (regional growing [RG], multi-dimensional threshold [MDT]). Pearson correlation and Bland-Altman analysis were used to evaluate the measuring accuracy of MS, RG, and MDT in phantoms as compared with the true volume. Then, OF and TEM volumes of 15 TAO patients and 15 normal controls were measured using MDT. Paired-sample t-tests were used to compare the volumes and volume ratios of different orbital tissues between TAO patients and controls. Each segmentation (MS RG, MDT) has a significant correlation (p<0.01) with true volume. There was a minimal bias for MS, and a stronger agreement between MDT and the true volume than RG and the true volume both in fatty and lean phantoms. The reproducibility of Cube FSE-flex determined MDT was adequate. The volumetric ratios of OF/globe (p<0.01), TEM/globe (p<0.01), whole orbit/globe (p<0.01) and bone orbit/globe (p<0.01) were significantly greater in TAO patients than those in healthy controls. MRI Cube FSE-flex determined MDT is a relatively accurate semi-automatic segmentation that can be used to evaluate OF and TEM volumes in clinic. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  13. 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

  14. Rosacea assessment by erythema index and principal component analysis segmentation maps

    NASA Astrophysics Data System (ADS)

    Kuzmina, Ilona; Rubins, Uldis; Saknite, Inga; Spigulis, Janis

    2017-12-01

    RGB images of rosacea were analyzed using segmentation maps of principal component analysis (PCA) and erythema index (EI). Areas of segmented clusters were compared to Clinician's Erythema Assessment (CEA) values given by two dermatologists. The results show that visible blood vessels are segmented more precisely on maps of the erythema index and the third principal component (PC3). In many cases, a distribution of clusters on EI and PC3 maps are very similar. Mean values of clusters' areas on these maps show a decrease of the area of blood vessels and erythema and an increase of lighter skin area after the therapy for the patients with diagnosis CEA = 2 on the first visit and CEA=1 on the second visit. This study shows that EI and PC3 maps are more useful than the maps of the first (PC1) and second (PC2) principal components for indicating vascular structures and erythema on the skin of rosacea patients and therapy monitoring.

  15. Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods.

    PubMed

    Parkinson, Craig; Foley, Kieran; Whybra, Philip; Hills, Robert; Roberts, Ashley; Marshall, Chris; Staffurth, John; Spezi, Emiliano

    2018-04-11

    Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

  16. Improved disparity map analysis through the fusion of monocular image segmentations

    NASA Technical Reports Server (NTRS)

    Perlant, Frederic P.; Mckeown, David M.

    1991-01-01

    The focus is to examine how estimates of three dimensional scene structure, as encoded in a scene disparity map, can be improved by the analysis of the original monocular imagery. The utilization of surface illumination information is provided by the segmentation of the monocular image into fine surface patches of nearly homogeneous intensity to remove mismatches generated during stereo matching. These patches are used to guide a statistical analysis of the disparity map based on the assumption that such patches correspond closely with physical surfaces in the scene. Such a technique is quite independent of whether the initial disparity map was generated by automated area-based or feature-based stereo matching. Stereo analysis results are presented on a complex urban scene containing various man-made and natural features. This scene contains a variety of problems including low building height with respect to the stereo baseline, buildings and roads in complex terrain, and highly textured buildings and terrain. The improvements are demonstrated due to monocular fusion with a set of different region-based image segmentations. The generality of this approach to stereo analysis and its utility in the development of general three dimensional scene interpretation systems are also discussed.

  17. Stress Analysis of Bolted, Segmented Cylindrical Shells Exhibiting Flange Mating-Surface Waviness

    NASA Technical Reports Server (NTRS)

    Knight, Norman F., Jr.; Phillips, Dawn R.; Raju, Ivatury S.

    2009-01-01

    Bolted, segmented cylindrical shells are a common structural component in many engineering systems especially for aerospace launch vehicles. Segmented shells are often needed due to limitations of manufacturing capabilities or transportation issues related to very long, large-diameter cylindrical shells. These cylindrical shells typically have a flange or ring welded to opposite ends so that shell segments can be mated together and bolted to form a larger structural system. As the diameter of these shells increases, maintaining strict fabrication tolerances for the flanges to be flat and parallel on a welded structure is an extreme challenge. Local fit-up stresses develop in the structure due to flange mating-surface mismatch (flange waviness). These local stresses need to be considered when predicting a critical initial flaw size. Flange waviness is one contributor to the fit-up stress state. The present paper describes the modeling and analysis effort to simulate fit-up stresses due to flange waviness in a typical bolted, segmented cylindrical shell. Results from parametric studies are presented for various flange mating-surface waviness distributions and amplitudes.

  18. Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.

    PubMed

    Xu, Yupeng; Yan, Ke; Kim, Jinman; Wang, Xiuying; Li, Changyang; Su, Li; Yu, Suqin; Xu, Xun; Feng, Dagan David

    2017-09-01

    Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management.

  19. Improving left ventricular segmentation in four-dimensional flow MRI using intramodality image registration for cardiac blood flow analysis.

    PubMed

    Gupta, Vikas; Bustamante, Mariana; Fredriksson, Alexandru; Carlhäll, Carl-Johan; Ebbers, Tino

    2018-01-01

    Assessment of blood flow in the left ventricle using four-dimensional flow MRI requires accurate left ventricle segmentation that is often hampered by the low contrast between blood and the myocardium. The purpose of this work is to improve left-ventricular segmentation in four-dimensional flow MRI for reliable blood flow analysis. The left ventricle segmentations are first obtained using morphological cine-MRI with better in-plane resolution and contrast, and then aligned to four-dimensional flow MRI data. This alignment is, however, not trivial due to inter-slice misalignment errors caused by patient motion and respiratory drift during breath-hold based cine-MRI acquisition. A robust image registration based framework is proposed to mitigate such errors automatically. Data from 20 subjects, including healthy volunteers and patients, was used to evaluate its geometric accuracy and impact on blood flow analysis. High spatial correspondence was observed between manually and automatically aligned segmentations, and the improvements in alignment compared to uncorrected segmentations were significant (P < 0.01). Blood flow analysis from manual and automatically corrected segmentations did not differ significantly (P > 0.05). Our results demonstrate the efficacy of the proposed approach in improving left-ventricular segmentation in four-dimensional flow MRI, and its potential for reliable blood flow analysis. Magn Reson Med 79:554-560, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  20. Benefit-Cost Analysis of Integrated Paratransit Systems : Volume 3. Scenario Analysis.

    DOT National Transportation Integrated Search

    1979-09-01

    This is the third volume of a six-volume report documenting the results of a study entitled "Benefit-Cost Analysis of Integrated Paratransit Systems." This volume provides detailed results of a series of scenario analyses designed to determine the im...

  1. GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

    PubMed

    Bakas, Spyridon; Zeng, Ke; Sotiras, Aristeidis; Rathore, Saima; Akbari, Hamed; Gaonkar, Bilwaj; Rozycki, Martin; Pati, Sarthak; Davatzikos, Christos

    2016-01-01

    We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

  2. Isomap transform for segmenting human body shapes.

    PubMed

    Cerveri, P; Sarro, K J; Marchente, M; Barros, R M L

    2011-09-01

    Segmentation of the 3D human body is a very challenging problem in applications exploiting volume capture data. Direct clustering in the Euclidean space is usually complex or even unsolvable. This paper presents an original method based on the Isomap (isometric feature mapping) transform of the volume data-set. The 3D articulated posture is mapped by Isomap in the pose of Da Vinci's Vitruvian man. The limbs are unrolled from each other and separated from the trunk and pelvis, and the topology of the human body shape is recovered. In such a configuration, Hoshen-Kopelman clustering applied to concentric spherical shells is used to automatically group points into the labelled principal curves. Shepard interpolation is utilised to back-map points of the principal curves into the original volume space. The experimental results performed on many different postures have proved the validity of the proposed method. Reliability of less than 2 cm and 3° in the location of the joint centres and direction axes of rotations has been obtained, respectively, which qualifies this procedure as a potential tool for markerless motion analysis.

  3. 3D morphometry using automated aortic segmentation in native MR angiography: an alternative to contrast enhanced MRA?

    PubMed Central

    Müller-Eschner, Matthias; Müller, Tobias; Biesdorf, Andreas; Wörz, Stefan; Rengier, Fabian; Böckler, Dittmar; Kauczor, Hans-Ulrich; Rohr, Karl

    2014-01-01

    Introduction Native-MR angiography (N-MRA) is considered an imaging alternative to contrast enhanced MR angiography (CE-MRA) for patients with renal insufficiency. Lower intraluminal contrast in N-MRA often leads to failure of the segmentation process in commercial algorithms. This study introduces an in-house 3D model-based segmentation approach used to compare both sequences by automatic 3D lumen segmentation, allowing for evaluation of differences of aortic lumen diameters as well as differences in length comparing both acquisition techniques at every possible location. Methods and materials Sixteen healthy volunteers underwent 1.5-T-MR Angiography (MRA). For each volunteer, two different MR sequences were performed, CE-MRA: gradient echo Turbo FLASH sequence and N-MRA: respiratory-and-cardiac-gated, T2-weighted 3D SSFP. Datasets were segmented using a 3D model-based ellipse-fitting approach with a single seed point placed manually above the celiac trunk. The segmented volumes were manually cropped from left subclavian artery to celiac trunk to avoid error due to side branches. Diameters, volumes and centerline length were computed for intraindividual comparison. For statistical analysis the Wilcoxon-Signed-Ranked-Test was used. Results Average centerline length obtained based on N-MRA was 239.0±23.4 mm compared to 238.6±23.5 mm for CE-MRA without significant difference (P=0.877). Average maximum diameter obtained based on N-MRA was 25.7±3.3 mm compared to 24.1±3.2 mm for CE-MRA (P<0.001). In agreement with the difference in diameters, volumes obtained based on N-MRA (100.1±35.4 cm3) were consistently and significantly larger compared to CE-MRA (89.2±30.0 cm3) (P<0.001). Conclusions 3D morphometry shows highly similar centerline lengths for N-MRA and CE-MRA, but systematically higher diameters and volumes for N-MRA. PMID:24834406

  4. A new fractional order derivative based active contour model for colon wall segmentation

    NASA Astrophysics Data System (ADS)

    Chen, Bo; Li, Lihong C.; Wang, Huafeng; Wei, Xinzhou; Huang, Shan; Chen, Wensheng; Liang, Zhengrong

    2018-02-01

    Segmentation of colon wall plays an important role in advancing computed tomographic colonography (CTC) toward a screening modality. Due to the low contrast of CT attenuation around colon wall, accurate segmentation of the boundary of both inner and outer wall is very challenging. In this paper, based on the geodesic active contour model, we develop a new model for colon wall segmentation. First, tagged materials in CTC images were automatically removed via a partial volume (PV) based electronic colon cleansing (ECC) strategy. We then present a new fractional order derivative based active contour model to segment the volumetric colon wall from the cleansed CTC images. In this model, the regionbased Chan-Vese model is incorporated as an energy term to the whole model so that not only edge/gradient information but also region/volume information is taken into account in the segmentation process. Furthermore, a fractional order differentiation derivative energy term is also developed in the new model to preserve the low frequency information and improve the noise immunity of the new segmentation model. The proposed colon wall segmentation approach was validated on 16 patient CTC scans. Experimental results indicate that the present scheme is very promising towards automatically segmenting colon wall, thus facilitating computer aided detection of initial colonic polyp candidates via CTC.

  5. Large deep neural networks for MS lesion segmentation

    NASA Astrophysics Data System (ADS)

    Prieto, Juan C.; Cavallari, Michele; Palotai, Miklos; Morales Pinzon, Alfredo; Egorova, Svetlana; Styner, Martin; Guttmann, Charles R. G.

    2017-02-01

    Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.

  6. Validation tools for image segmentation

    NASA Astrophysics Data System (ADS)

    Padfield, Dirk; Ross, James

    2009-02-01

    A large variety of image analysis tasks require the segmentation of various regions in an image. For example, segmentation is required to generate accurate models of brain pathology that are important components of modern diagnosis and therapy. While the manual delineation of such structures gives accurate information, the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing the parameter settings for individual cases and across a collection of datasets using the Design of Experiment framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms. We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the statistical segmentation algorithm although they perform reasonably well considering their simplicity.

  7. Comparision between Brain Atrophy and Subdural Volume to Predict Chronic Subdural Hematoma: Volumetric CT Imaging Analysis

    PubMed Central

    Ju, Min-Wook; Kwon, Hyon-Jo; Choi, Seung-Won; Koh, Hyeon-Song; Youm, Jin-Young; Song, Shi-Hun

    2015-01-01

    Objective Brain atrophy and subdural hygroma were well known factors that enlarge the subdural space, which induced formation of chronic subdural hematoma (CSDH). Thus, we identified the subdural volume that could be used to predict the rate of future CSDH after head trauma using a computed tomography (CT) volumetric analysis. Methods A single institution case-control study was conducted involving 1,186 patients who visited our hospital after head trauma from January 1, 2010 to December 31, 2014. Fifty-one patients with delayed CSDH were identified, and 50 patients with age and sex matched for control. Intracranial volume (ICV), the brain parenchyme, and the subdural space were segmented using CT image-based software. To adjust for variations in head size, volume ratios were assessed as a percentage of ICV [brain volume index (BVI), subdural volume index (SVI)]. The maximum depth of the subdural space on both sides was used to estimate the SVI. Results Before adjusting for cranium size, brain volume tended to be smaller, and subdural space volume was significantly larger in the CSDH group (p=0.138, p=0.021, respectively). The BVI and SVI were significantly different (p=0.003, p=0.001, respectively). SVI [area under the curve (AUC), 77.3%; p=0.008] was a more reliable technique for predicting CSDH than BVI (AUC, 68.1%; p=0.001). Bilateral subdural depth (sum of subdural depth on both sides) increased linearly with SVI (p<0.0001). Conclusion Subdural space volume was significantly larger in CSDH groups. SVI was a more reliable technique for predicting CSDH. Bilateral subdural depth was useful to measure SVI. PMID:27169071

  8. Comparision between Brain Atrophy and Subdural Volume to Predict Chronic Subdural Hematoma: Volumetric CT Imaging Analysis.

    PubMed

    Ju, Min-Wook; Kim, Seon-Hwan; Kwon, Hyon-Jo; Choi, Seung-Won; Koh, Hyeon-Song; Youm, Jin-Young; Song, Shi-Hun

    2015-10-01

    Brain atrophy and subdural hygroma were well known factors that enlarge the subdural space, which induced formation of chronic subdural hematoma (CSDH). Thus, we identified the subdural volume that could be used to predict the rate of future CSDH after head trauma using a computed tomography (CT) volumetric analysis. A single institution case-control study was conducted involving 1,186 patients who visited our hospital after head trauma from January 1, 2010 to December 31, 2014. Fifty-one patients with delayed CSDH were identified, and 50 patients with age and sex matched for control. Intracranial volume (ICV), the brain parenchyme, and the subdural space were segmented using CT image-based software. To adjust for variations in head size, volume ratios were assessed as a percentage of ICV [brain volume index (BVI), subdural volume index (SVI)]. The maximum depth of the subdural space on both sides was used to estimate the SVI. Before adjusting for cranium size, brain volume tended to be smaller, and subdural space volume was significantly larger in the CSDH group (p=0.138, p=0.021, respectively). The BVI and SVI were significantly different (p=0.003, p=0.001, respectively). SVI [area under the curve (AUC), 77.3%; p=0.008] was a more reliable technique for predicting CSDH than BVI (AUC, 68.1%; p=0.001). Bilateral subdural depth (sum of subdural depth on both sides) increased linearly with SVI (p<0.0001). Subdural space volume was significantly larger in CSDH groups. SVI was a more reliable technique for predicting CSDH. Bilateral subdural depth was useful to measure SVI.

  9. Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks

    PubMed Central

    Huo, Yuankai; Xu, Zhoubing; Bao, Shunxing; Bermudez, Camilo; Plassard, Andrew J.; Liu, Jiaqi; Yao, Yuang; Assad, Albert; Abramson, Richard G.; Landman, Bennett A.

    2018-01-01

    Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly.

  10. A new model for the determination of limb segment mass in children.

    PubMed

    Kuemmerle-Deschner, J B; Hansmann, S; Rapp, H; Dannecker, G E

    2007-04-01

    The knowledge of limb segment masses is critical for the calculation of joint torques. Several methods for segment mass estimation have been described in the literature. They are either inaccurate or not applicable to the limb segments of children. Therefore, we developed a new cylinder brick model (CBM) to estimate segment mass in children. The aim of this study was to compare CBM and a model based on a polynomial regression equation (PRE) to volume measurement obtained by the water displacement method (WDM). We examined forearms, hands, lower legs, and feet of 121 children using CBM, PRE, and WDM. The differences between CBM and WDM or PRE and WDM were calculated and compared using a Bland-Altman plot of differences. Absolute limb segment mass measured by WDM ranged from 0.16+/-0.04 kg for hands in girls 5-6 years old, up to 2.72+/-1.03 kg for legs in girls 11-12 years old. The differences of normalised segment masses ranged from 0.0002+/-0.0021 to 0.0011+/-0.0036 for CBM-WDM and from 0.0023+/-0.0041 to 0.0127+/-0.036 for PRE-WDM (values are mean+/-2 S.D.). The CBM showed better agreement with WDM than PRE for all limb segments in girls and boys. CBM is accurate and superior to PRE for the estimation of individual limb segment mass of children. Therefore, CBM is a practical and useful tool for the analysis of kinetic parameters and the calculation of resulting forces to assess joint functionality in children.

  11. Adolescents and alcohol: an explorative audience segmentation analysis

    PubMed Central

    2012-01-01

    Background So far, audience segmentation of adolescents with respect to alcohol has been carried out mainly on the basis of socio-demographic characteristics. In this study we examined whether it is possible to segment adolescents according to their values and attitudes towards alcohol to use as guidance for prevention programmes. Methods A random sample of 7,000 adolescents aged 12 to 18 was drawn from the Municipal Basic Administration (MBA) of 29 Local Authorities in the province North-Brabant in the Netherlands. By means of an online questionnaire data were gathered on values and attitudes towards alcohol, alcohol consumption and socio-demographic characteristics. Results We were able to distinguish a total of five segments on the basis of five attitude factors. Moreover, the five segments also differed in drinking behavior independently of socio-demographic variables. Conclusions Our investigation was a first step in the search for possibilities of segmenting by factors other than socio-demographic characteristics. Further research is necessary in order to understand these results for alcohol prevention policy in concrete terms. PMID:22950946

  12. Three-Dimensional Morphometric Analysis of the Iris by Swept-Source Anterior Segment Optical Coherence Tomography in a Caucasian Population.

    PubMed

    Invernizzi, Alessandro; Giardini, Piero; Cigada, Mario; Viola, Francesco; Staurenghi, Giovanni

    2015-07-01

    We analyzed by swept-source anterior segment optical coherence tomography (SS-ASOCT) the three-dimensional iris morphology in a Caucasian population, and correlated the findings with iris color, iris sectors, subject age, and sex. One eye each from consecutive healthy emmetropic (refractive spherical equivalent ± 3 diopters) volunteers were selected for the study. The enrolled eye underwent standardized anterior segment photography to assess iris color. Iris images were assessed by SS-ASOCT for volume, thickness, width, and pupil size. Sectoral variations of morphometric data among the superior, nasal, inferior, and temporal sectors were recorded. A total of 135 eyes from 57 males and 78 females, age 49 ± 17 years, fulfilled the inclusion criteria. All iris morphometric parameters varied significantly among the different sectors (all P < 0.0001). Iris total volume and thickness were significantly correlated with increasingly darker pigmentation (P < 0.0001, P = 0.0384, respectively). Neither width nor pupil diameter was influenced by iris color. Age did not affect iris volume or thickness; iris width increased and pupil diameter decreased with age (rs = 0.52, rs = -0.58, respectively). There was no effect of sex on iris volume, thickness, or pupil diameter; iris width was significantly greater in males (P = 0.007). Morphology of the iris varied by iris sector, and iris color was associated with differences in iris volume and thickness. Morphological parameter variations associated with iris color, sector, age, and sex can be used to identify pathological changes in suspect eyes. To be effective in clinical settings, construction of iris morphological databases for different ethnic and racial populations is essential.

  13. A supervoxel-based segmentation method for prostate MR images.

    PubMed

    Tian, Zhiqiang; Liu, Lizhi; Zhang, Zhenfeng; Xue, Jianru; Fei, Baowei

    2017-02-01

    Segmentation of the prostate on MR images has many applications in prostate cancer management. In this work, we propose a supervoxel-based segmentation method for prostate MR images. A supervoxel is a set of pixels that have similar intensities, locations, and textures in a 3D image volume. The prostate segmentation problem is considered as assigning a binary label to each supervoxel, which is either the prostate or background. A supervoxel-based energy function with data and smoothness terms is used to model the label. The data term estimates the likelihood of a supervoxel belonging to the prostate by using a supervoxel-based shape feature. The geometric relationship between two neighboring supervoxels is used to build the smoothness term. The 3D graph cut is used to minimize the energy function to get the labels of the supervoxels, which yields the prostate segmentation. A 3D active contour model is then used to get a smooth surface by using the output of the graph cut as an initialization. The performance of the proposed algorithm was evaluated on 30 in-house MR image data and PROMISE12 dataset. The mean Dice similarity coefficients are 87.2 ± 2.3% and 88.2 ± 2.8% for our 30 in-house MR volumes and the PROMISE12 dataset, respectively. The proposed segmentation method yields a satisfactory result for prostate MR images. The proposed supervoxel-based method can accurately segment prostate MR images and can have a variety of application in prostate cancer diagnosis and therapy. © 2016 American Association of Physicists in Medicine.

  14. Boundary overlap for medical image segmentation evaluation

    NASA Astrophysics Data System (ADS)

    Yeghiazaryan, Varduhi; Voiculescu, Irina

    2017-03-01

    All medical image segmentation algorithms need to be validated and compared, and yet no evaluation framework is widely accepted within the imaging community. Collections of segmentation results often need to be compared and ranked by their effectiveness. Evaluation measures which are popular in the literature are based on region overlap or boundary distance. None of these are consistent in the way they rank segmentation results: they tend to be sensitive to one or another type of segmentation error (size, location, shape) but no single measure covers all error types. We introduce a new family of measures, with hybrid characteristics. These measures quantify similarity/difference of segmented regions by considering their overlap around the region boundaries. This family is more sensitive than other measures in the literature to combinations of segmentation error types. We compare measure performance on collections of segmentation results sourced from carefully compiled 2D synthetic data, and also on 3D medical image volumes. We show that our new measure: (1) penalises errors successfully, especially those around region boundaries; (2) gives a low similarity score when existing measures disagree, thus avoiding overly inflated scores; and (3) scores segmentation results over a wider range of values. We consider a representative measure from this family and the effect of its only free parameter on error sensitivity, typical value range, and running time.

  15. Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods.

    PubMed

    van der Kleij, Lisa A; de Bresser, Jeroen; Hendrikse, Jeroen; Siero, Jeroen C W; Petersen, Esben T; De Vis, Jill B

    2018-01-01

    In previous work we have developed a fast sequence that focusses on cerebrospinal fluid (CSF) based on the long T2 of CSF. By processing the data obtained with this CSF MRI sequence, brain parenchymal volume (BPV) and intracranial volume (ICV) can be automatically obtained. The aim of this study was to assess the precision of the BPV and ICV measurements of the CSF MRI sequence and to validate the CSF MRI sequence by comparison with 3D T1-based brain segmentation methods. Ten healthy volunteers (2 females; median age 28 years) were scanned (3T MRI) twice with repositioning in between. The scan protocol consisted of a low resolution (LR) CSF sequence (0:57min), a high resolution (HR) CSF sequence (3:21min) and a 3D T1-weighted sequence (6:47min). Data of the HR 3D-T1-weighted images were downsampled to obtain LR T1-weighted images (reconstructed imaging time: 1:59 min). Data of the CSF MRI sequences was automatically segmented using in-house software. The 3D T1-weighted images were segmented using FSL (5.0), SPM12 and FreeSurfer (5.3.0). The mean absolute differences for BPV and ICV between the first and second scan for CSF LR (BPV/ICV: 12±9/7±4cc) and CSF HR (5±5/4±2cc) were comparable to FSL HR (9±11/19±23cc), FSL LR (7±4, 6±5cc), FreeSurfer HR (5±3/14±8cc), FreeSurfer LR (9±8, 12±10cc), and SPM HR (5±3/4±7cc), and SPM LR (5±4, 5±3cc). The correlation between the measured volumes of the CSF sequences and that measured by FSL, FreeSurfer and SPM HR and LR was very good (all Pearson's correlation coefficients >0.83, R2 .67-.97). The results from the downsampled data and the high-resolution data were similar. Both CSF MRI sequences have a precision comparable to, and a very good correlation with established 3D T1-based automated segmentations methods for the segmentation of BPV and ICV. However, the short imaging time of the fast CSF MRI sequence is superior to the 3D T1 sequence on which segmentation with established methods is performed.

  16. Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease.

    PubMed

    Kim, Youngwoo; Ge, Yinghui; Tao, Cheng; Zhu, Jianbing; Chapman, Arlene B; Torres, Vicente E; Yu, Alan S L; Mrug, Michal; Bennett, William M; Flessner, Michael F; Landsittel, Doug P; Bae, Kyongtae T

    2016-04-07

    Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method. Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed. Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable. We have developed a fully automated method for segmentation of kidneys from abdominal

  17. Fully Automated Pulmonary Lobar Segmentation: Influence of Different Prototype Software Programs onto Quantitative Evaluation of Chronic Obstructive Lung Disease

    PubMed Central

    Lim, Hyun-ju; Weinheimer, Oliver; Wielpütz, Mark O.; Dinkel, Julien; Hielscher, Thomas; Gompelmann, Daniela; Kauczor, Hans-Ulrich; Heussel, Claus Peter

    2016-01-01

    Objectives Surgical or bronchoscopic lung volume reduction (BLVR) techniques can be beneficial for heterogeneous emphysema. Post-processing software tools for lobar emphysema quantification are useful for patient and target lobe selection, treatment planning and post-interventional follow-up. We aimed to evaluate the inter-software variability of emphysema quantification using fully automated lobar segmentation prototypes. Material and Methods 66 patients with moderate to severe COPD who underwent CT for planning of BLVR were included. Emphysema quantification was performed using 2 modified versions of in-house software (without and with prototype advanced lung vessel segmentation; programs 1 [YACTA v.2.3.0.2] and 2 [YACTA v.2.4.3.1]), as well as 1 commercial program 3 [Pulmo3D VA30A_HF2] and 1 pre-commercial prototype 4 [CT COPD ISP ver7.0]). The following parameters were computed for each segmented anatomical lung lobe and the whole lung: lobar volume (LV), mean lobar density (MLD), 15th percentile of lobar density (15th), emphysema volume (EV) and emphysema index (EI). Bland-Altman analysis (limits of agreement, LoA) and linear random effects models were used for comparison between the software. Results Segmentation using programs 1, 3 and 4 was unsuccessful in 1 (1%), 7 (10%) and 5 (7%) patients, respectively. Program 2 could analyze all datasets. The 53 patients with successful segmentation by all 4 programs were included for further analysis. For LV, program 1 and 4 showed the largest mean difference of 72 ml and the widest LoA of [-356, 499 ml] (p<0.05). Program 3 and 4 showed the largest mean difference of 4% and the widest LoA of [-7, 14%] for EI (p<0.001). Conclusions Only a single software program was able to successfully analyze all scheduled data-sets. Although mean bias of LV and EV were relatively low in lobar quantification, ranges of disagreement were substantial in both of them. For longitudinal emphysema monitoring, not only scanning protocol but

  18. 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.

  19. Analysis and testing of a soft actuation system for segmented reflector articulation and isolation

    NASA Technical Reports Server (NTRS)

    Jandura, Louise; Agronin, Michael L.

    1991-01-01

    Segmented reflectors have been proposed for space-based applications such as optical communication and large-diameter telescopes. An actuation system for mirrors in a space-based segmented mirror array has been developed as part of the National Aeronautics and Space Administration-sponsored Precision Segmented Reflector program. The actuation system, called the Articulated Panel Module (APM), articulates a mirror panel in 3 degrees of freedom in the submicron regime, isolates the panel from structural motion, and simplifies space assembly of the mirrors to the reflector backup truss. A breadboard of the APM has been built and is described. Three-axis modeling, analysis, and testing of the breadboard is discussed.

  20. Control volume based hydrocephalus research; analysis of human data

    NASA Astrophysics Data System (ADS)

    Cohen, Benjamin; Wei, Timothy; Voorhees, Abram; Madsen, Joseph; Anor, Tomer

    2010-11-01

    Hydrocephalus is a neuropathophysiological disorder primarily diagnosed by increased cerebrospinal fluid volume and pressure within the brain. To date, utilization of clinical measurements have been limited to understanding of the relative amplitude and timing of flow, volume and pressure waveforms; qualitative approaches without a clear framework for meaningful quantitative comparison. Pressure volume models and electric circuit analogs enforce volume conservation principles in terms of pressure. Control volume analysis, through the integral mass and momentum conservation equations, ensures that pressure and volume are accounted for using first principles fluid physics. This approach is able to directly incorporate the diverse measurements obtained by clinicians into a simple, direct and robust mechanics based framework. Clinical data obtained for analysis are discussed along with data processing techniques used to extract terms in the conservation equation. Control volume analysis provides a non-invasive, physics-based approach to extracting pressure information from magnetic resonance velocity data that cannot be measured directly by pressure instrumentation.

  1. A hybrid 3D region growing and 4D curvature analysis-based automatic abdominal blood vessel segmentation through contrast enhanced CT

    NASA Astrophysics Data System (ADS)

    Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Shimada, Mitsuo; Iinuma, Gen

    2017-03-01

    In abdominal disease diagnosis and various abdominal surgeries planning, segmentation of abdominal blood vessel (ABVs) is a very imperative task. Automatic segmentation enables fast and accurate processing of ABVs. We proposed a fully automatic approach for segmenting ABVs through contrast enhanced CT images by a hybrid of 3D region growing and 4D curvature analysis. The proposed method comprises three stages. First, candidates of bone, kidneys, ABVs and heart are segmented by an auto-adapted threshold. Second, bone is auto-segmented and classified into spine, ribs and pelvis. Third, ABVs are automatically segmented in two sub-steps: (1) kidneys and abdominal part of the heart are segmented, (2) ABVs are segmented by a hybrid approach that integrates a 3D region growing and 4D curvature analysis. Results are compared with two conventional methods. Results show that the proposed method is very promising in segmenting and classifying bone, segmenting whole ABVs and may have potential utility in clinical use.

  2. Anterior segment sparing to reduce charged particle radiotherapy complications in uveal melanoma

    NASA Technical Reports Server (NTRS)

    Daftari, I. K.; Char, D. H.; Verhey, L. J.; Castro, J. R.; Petti, P. L.; Meecham, W. J.; Kroll, S.; Blakely, E. A.; Chatterjee, A. (Principal Investigator)

    1997-01-01

    PURPOSE: The purpose of this investigation is to delineate the risk factors in the development of neovascular glaucoma (NVG) after helium-ion irradiation of uveal melanoma patients and to propose treatment technique that may reduce this risk. METHODS AND MATERIALS: 347 uveal melanoma patients were treated with helium-ions using a single-port treatment technique. Using univariate and multivariate statistics, the NVG complication rate was analyzed according to the percent of anterior chamber in the radiation field, tumor size, tumor location, sex, age, dose, and other risk factors. Several University of California San Francisco-Lawrence Berkeley National Laboratory (LBNL) patients in each size category (medium, large, and extralarge) were retrospectively replanned using two ports instead of a single port. By using appropriate polar and azimuthal gaze angles or by treating patients with two ports, the maximum dose to the anterior segment of the eye can often be reduced. Although a larger volume of anterior chamber may receive a lower dose by using two ports than a single port treatment. We hypothesize that this could reduce the level of complications that result from the irradiation of the anterior chamber of the eye. Dose-volume histograms were calculated for the lens, and compared for the single and two-port techniques. RESULTS: NVG developed in 121 (35%) patients. The risk of NVG peaked between 1 and 2.5 years posttreatment. By univariate and multivariate analysis, the percent of lens in the field was strongly correlated with the development of NVG. Other contributing factors were tumor height, history of diabetes, and vitreous hemorrhage. Dose-volume histogram analysis of single-port vs. two-port techniques demonstrate that for some patients in the medium and large category tumor groups, a significant decrease in dose to the structures in the anterior segment of the eye could have been achieved with the use of two ports. CONCLUSION: The development of NVG after

  3. Selecting exposure measures in crash rate prediction for two-lane highway segments.

    PubMed

    Qin, Xiao; Ivan, John N; Ravishanker, Nalini

    2004-03-01

    A critical part of any risk assessment is identifying how to represent exposure to the risk involved. Recent research shows that the relationship between crash count and traffic volume is non-linear; consequently, a simple crash rate computed as the ratio of crash count to volume is not proper for comparing the safety of sites with different traffic volumes. To solve this problem, we describe a new approach for relating traffic volume and crash incidence. Specifically, we disaggregate crashes into four types: (1) single-vehicle, (2) multi-vehicle same direction, (3) multi-vehicle opposite direction, and (4) multi-vehicle intersecting, and define candidate exposure measures for each that we hypothesize will be linear with respect to each crash type. This paper describes initial investigation using crash and physical characteristics data for highway segments in Michigan from the Highway Safety Information System (HSIS). We use zero-inflated-Poisson (ZIP) modeling to estimate models for predicting counts for each of the above crash types as a function of the daily volume, segment length, speed limit and roadway width. We found that the relationship between crashes and the daily volume (AADT) is non-linear and varies by crash type, and is significantly different from the relationship between crashes and segment length for all crash types. Our research will provide information to improve accuracy of crash predictions and, thus, facilitate more meaningful comparison of the safety record of seemingly similar highway locations.

  4. The change of adjacent segment after cervical disc arthroplasty compared with anterior cervical discectomy and fusion: a meta-analysis of randomized controlled trials.

    PubMed

    Dong, Liang; Xu, Zhengwei; Chen, Xiujin; Wang, Dongqi; Li, Dichen; Liu, Tuanjing; Hao, Dingjun

    2017-10-01

    Many meta-analyses have been performed to study the efficacy of cervical disc arthroplasty (CDA) compared with anterior cervical discectomy and fusion (ACDF); however, there are few data referring to adjacent segment within these meta-analyses, or investigators are unable to arrive at the same conclusion in the few meta-analyses about adjacent segment. With the increased concerns surrounding adjacent segment degeneration (ASDeg) and adjacent segment disease (ASDis) after anterior cervical surgery, it is necessary to perform a comprehensive meta-analysis to analyze adjacent segment parameters. To perform a comprehensive meta-analysis to elaborate adjacent segment motion, degeneration, disease, and reoperation of CDA compared with ACDF. Meta-analysis of randomized controlled trials (RCTs). PubMed, Embase, and Cochrane Library were searched for RCTs comparing CDA and ACDF before May 2016. The analysis parameters included follow-up time, operative segments, adjacent segment motion, ASDeg, ASDis, and adjacent segment reoperation. The risk of bias scale was used to assess the papers. Subgroup analysis and sensitivity analysis were used to analyze the reason for high heterogeneity. Twenty-nine RCTs fulfilled the inclusion criteria. Compared with ACDF, the rate of adjacent segment reoperation in the CDA group was significantly lower (p<.01), and the advantage of that group in reducing adjacent segment reoperation increases with increasing follow-up time by subgroup analysis. There was no statistically significant difference in ASDeg between CDA and ACDF within the 24-month follow-up period; however, the rate of ASDeg in CDA was significantly lower than that of ACDF with the increase in follow-up time (p<.01). There was no statistically significant difference in ASDis between CDA and ACDF (p>.05). Cervical disc arthroplasty provided a lower adjacent segment range of motion (ROM) than did ACDF, but the difference was not statistically significant. Compared with ACDF, the

  5. Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy

    PubMed Central

    Xu, Yupeng; Yan, Ke; Kim, Jinman; Wang, Xiuying; Li, Changyang; Su, Li; Yu, Suqin; Xu, Xun; Feng, Dagan David

    2017-01-01

    Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management. PMID:28966847

  6. Structure-properties relationships of novel poly(carbonate-co-amide) segmented copolymers with polyamide-6 as hard segments and polycarbonate as soft segments

    NASA Astrophysics Data System (ADS)

    Yang, Yunyun; Kong, Weibo; Yuan, Ye; Zhou, Changlin; Cai, Xufu

    2018-04-01

    Novel poly(carbonate-co-amide) (PCA) block copolymers are prepared with polycarbonate diol (PCD) as soft segments, polyamide-6 (PA6) as hard segments and 4,4'-diphenylmethane diisocyanate (MDI) as coupling agent through reactive processing. The reactive processing strategy is eco-friendly and resolve the incompatibility between polyamide segments and PCD segments in preparation processing. The chemical structure, crystalline properties, thermal properties, mechanical properties and water resistance were extensively studied by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Differential scanning calorimetry (DSC), Thermal gravity analysis (TGA), Dynamic mechanical analysis (DMA), tensile testing, water contact angle and water absorption, respectively. The as-prepared PCAs exhibit obvious microphase separation between the crystalline hard PA6 phase and amorphous PCD soft segments. Meanwhile, PCAs showed outstanding mechanical with the maximum tensile strength of 46.3 MPa and elongation at break of 909%. The contact angle and water absorption results indicate that PCAs demonstrate outstanding water resistance even though possess the hydrophilic surfaces. The TGA measurements prove that the thermal stability of PCA can satisfy the requirement of multiple-processing without decomposition.

  7. Motion-aware stroke volume quantification in 4D PC-MRI data of the human aorta.

    PubMed

    Köhler, Benjamin; Preim, Uta; Grothoff, Matthias; Gutberlet, Matthias; Fischbach, Katharina; Preim, Bernhard

    2016-02-01

    4D PC-MRI enables the noninvasive measurement of time-resolved, three-dimensional blood flow data that allow quantification of the hemodynamics. Stroke volumes are essential to assess the cardiac function and evolution of different cardiovascular diseases. The calculation depends on the wall position and vessel orientation, which both change during the cardiac cycle due to the heart muscle contraction and the pumped blood. However, current systems for the quantitative 4D PC-MRI data analysis neglect the dynamic character and instead employ a static 3D vessel approximation. We quantify differences between stroke volumes in the aorta obtained with and without consideration of its dynamics. We describe a method that uses the approximating 3D segmentation to automatically initialize segmentation algorithms that require regions inside and outside the vessel for each temporal position. This enables the use of graph cuts to obtain 4D segmentations, extract vessel surfaces including centerlines for each temporal position and derive motion information. The stroke volume quantification is compared using measuring planes in static (3D) vessels, planes with fixed angulation inside dynamic vessels (this corresponds to the common 2D PC-MRI) and moving planes inside dynamic vessels. Seven datasets with different pathologies such as aneurysms and coarctations were evaluated in close collaboration with radiologists. Compared to the experts' manual stroke volume estimations, motion-aware quantification performs, on average, 1.57% better than calculations without motion consideration. The mean difference between stroke volumes obtained with the different methods is 7.82%. Automatically obtained 4D segmentations overlap by 85.75% with manually generated ones. Incorporating motion information in the stroke volume quantification yields slight but not statistically significant improvements. The presented method is feasible for the clinical routine, since computation times are low and

  8. MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

    PubMed

    Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N; Satterthwaite, Theodore D; Muschelli, John; Calabresi, Peter A; Pham, Dzung L; Martin, Melissa Lynne; Shinohara, Russell T

    2018-03-08

    Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study. Copyright © 2018 by the American Society of Neuroimaging.

  9. A new image segmentation method based on multifractal detrended moving average analysis

    NASA Astrophysics Data System (ADS)

    Shi, Wen; Zou, Rui-biao; Wang, Fang; Su, Le

    2015-08-01

    In order to segment and delineate some regions of interest in an image, we propose a novel algorithm based on the multifractal detrended moving average analysis (MF-DMA). In this method, the generalized Hurst exponent h(q) is calculated for every pixel firstly and considered as the local feature of a surface. And then a multifractal detrended moving average spectrum (MF-DMS) D(h(q)) is defined by the idea of box-counting dimension method. Therefore, we call the new image segmentation method MF-DMS-based algorithm. The performance of the MF-DMS-based method is tested by two image segmentation experiments of rapeseed leaf image of potassium deficiency and magnesium deficiency under three cases, namely, backward (θ = 0), centered (θ = 0.5) and forward (θ = 1) with different q values. The comparison experiments are conducted between the MF-DMS method and other two multifractal segmentation methods, namely, the popular MFS-based and latest MF-DFS-based methods. The results show that our MF-DMS-based method is superior to the latter two methods. The best segmentation result for the rapeseed leaf image of potassium deficiency and magnesium deficiency is from the same parameter combination of θ = 0.5 and D(h(- 10)) when using the MF-DMS-based method. An interesting finding is that the D(h(- 10)) outperforms other parameters for both the MF-DMS-based method with centered case and MF-DFS-based algorithms. By comparing the multifractal nature between nutrient deficiency and non-nutrient deficiency areas determined by the segmentation results, an important finding is that the gray value's fluctuation in nutrient deficiency area is much severer than that in non-nutrient deficiency area.

  10. Interactive lung segmentation in abnormal human and animal chest CT scans

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

    Kockelkorn, Thessa T. J. P., E-mail: thessa@isi.uu.nl; Viergever, Max A.; Schaefer-Prokop, Cornelia M.

    2014-08-15

    Purpose: Many medical image analysis systems require segmentation of the structures of interest as a first step. For scans with gross pathology, automatic segmentation methods may fail. The authors’ aim is to develop a versatile, fast, and reliable interactive system to segment anatomical structures. In this study, this system was used for segmenting lungs in challenging thoracic computed tomography (CT) scans. Methods: In volumetric thoracic CT scans, the chest is segmented and divided into 3D volumes of interest (VOIs), containing voxels with similar densities. These VOIs are automatically labeled as either lung tissue or nonlung tissue. The automatic labeling resultsmore » can be corrected using an interactive or a supervised interactive approach. When using the supervised interactive system, the user is shown the classification results per slice, whereupon he/she can adjust incorrect labels. The system is retrained continuously, taking the corrections and approvals of the user into account. In this way, the system learns to make a better distinction between lung tissue and nonlung tissue. When using the interactive framework without supervised learning, the user corrects all incorrectly labeled VOIs manually. Both interactive segmentation tools were tested on 32 volumetric CT scans of pigs, mice and humans, containing pulmonary abnormalities. Results: On average, supervised interactive lung segmentation took under 9 min of user interaction. Algorithm computing time was 2 min on average, but can easily be reduced. On average, 2.0% of all VOIs in a scan had to be relabeled. Lung segmentation using the interactive segmentation method took on average 13 min and involved relabeling 3.0% of all VOIs on average. The resulting segmentations correspond well to manual delineations of eight axial slices per scan, with an average Dice similarity coefficient of 0.933. Conclusions: The authors have developed two fast and reliable methods for interactive lung segmentation

  11. A supervoxel-based segmentation method for prostate MR images

    NASA Astrophysics Data System (ADS)

    Tian, Zhiqiang; Liu, LiZhi; Fei, Baowei

    2015-03-01

    Accurate segmentation of the prostate has many applications in prostate cancer diagnosis and therapy. In this paper, we propose a "Supervoxel" based method for prostate segmentation. The prostate segmentation problem is considered as assigning a label to each supervoxel. An energy function with data and smoothness terms is used to model the labeling process. The data term estimates the likelihood of a supervoxel belongs to the prostate according to a shape feature. The geometric relationship between two neighboring supervoxels is used to construct a smoothness term. A threedimensional (3D) graph cut method is used to minimize the energy function in order to segment the prostate. A 3D level set is then used to get a smooth surface based on the output of the graph cut. The performance of the proposed segmentation algorithm was evaluated with respect to the manual segmentation ground truth. The experimental results on 12 prostate volumes showed that the proposed algorithm yields a mean Dice similarity coefficient of 86.9%+/-3.2%. The segmentation method can be used not only for the prostate but also for other organs.

  12. Collaborative SDOCT Segmentation and Analysis Software.

    PubMed

    Yun, Yeyi; Carass, Aaron; Lang, Andrew; Prince, Jerry L; Antony, Bhavna J

    2017-02-01

    Spectral domain optical coherence tomography (SDOCT) is routinely used in the management and diagnosis of a variety of ocular diseases. This imaging modality also finds widespread use in research, where quantitative measurements obtained from the images are used to track disease progression. In recent years, the number of available scanners and imaging protocols grown and there is a distinct absence of a unified tool that is capable of visualizing, segmenting, and analyzing the data. This is especially noteworthy in longitudinal studies, where data from older scanners and/or protocols may need to be analyzed. Here, we present a graphical user interface (GUI) that allows users to visualize and analyze SDOCT images obtained from two commonly used scanners. The retinal surfaces in the scans can be segmented using a previously described method, and the retinal layer thicknesses can be compared to a normative database. If necessary, the segmented surfaces can also be corrected and the changes applied. The interface also allows users to import and export retinal layer thickness data to an SQL database, thereby allowing for the collation of data from a number of collaborating sites.

  13. Epidermal segmentation in high-definition optical coherence tomography.

    PubMed

    Li, Annan; Cheng, Jun; Yow, Ai Ping; Wall, Carolin; Wong, Damon Wing Kee; Tey, Hong Liang; Liu, Jiang

    2015-01-01

    Epidermis segmentation is a crucial step in many dermatological applications. Recently, high-definition optical coherence tomography (HD-OCT) has been developed and applied to imaging subsurface skin tissues. In this paper, a novel epidermis segmentation method using HD-OCT is proposed in which the epidermis is segmented by 3 steps: the weighted least square-based pre-processing, the graph-based skin surface detection and the local integral projection-based dermal-epidermal junction detection respectively. Using a dataset of five 3D volumes, we found that this method correlates well with the conventional method of manually marking out the epidermis. This method can therefore serve to effectively and rapidly delineate the epidermis for study and clinical management of skin diseases.

  14. Influence of stapling the intersegmental planes on lung volume and function after segmentectomy.

    PubMed

    Tao, Hiroyuki; Tanaka, Toshiki; Hayashi, Tatsuro; Yoshida, Kumiko; Furukawa, Masashi; Yoshiyama, Koichi; Okabe, Kazunori

    2016-10-01

    Dividing the intersegmental planes with a stapler during pulmonary segmentectomy leads to volume loss in the remnant segment. The aim of this study was to assess the influence of segment division methods on preserved lung volume and pulmonary function after segmentectomy. Using image analysis software on computed tomography (CT) images of 41 patients, the ratio of remnant segment and ipsilateral lung volume to their preoperative values (R-seg and R-ips) was calculated. The ratio of postoperative actual forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) per those predicted values based on three-dimensional volumetry (R-FEV1 and R-FVC) was also calculated. Differences in actual/predicted ratios of lung volume and pulmonary function for each of the division methods were analysed. We also investigated the correlations of the actual/predicted ratio of remnant lung volume with that of postoperative pulmonary function. The intersegmental planes were divided by either electrocautery or with a stapler in 22 patients and with a stapler alone in 19 patients. Mean values of R-seg and R-ips were 82.7 (37.9-140.2) and 104.9 (77.5-129.2)%, respectively. The mean values of R-FEV1 and R-FVC were 103.9 (83.7-135.1) and 103.4 (82.2-125.1)%, respectively. There were no correlations between the actual/predicted ratio of remnant lung volume and pulmonary function based on the division method. Both R-FEV1 and R-FVC were correlated not with R-seg, but with R-ips. Stapling does not lead to less preserved volume or function than electrocautery in the division of the intersegmental planes. © The Author 2016. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

  15. Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU).

    PubMed

    Antila, Kari; Nieminen, Heikki J; Sequeiros, Roberto Blanco; Ehnholm, Gösta

    2014-07-01

    Up to 25% of women suffer from uterine fibroids (UF) that cause infertility, pain, and discomfort. MR-guided high intensity focused ultrasound (MR-HIFU) is an emerging technique for noninvasive, computer-guided thermal ablation of UFs. The volume of induced necrosis is a predictor of the success of the treatment. However, accurate volume assessment by hand can be time consuming, and quick tools produce biased results. Therefore, fast and reliable tools are required in order to estimate the technical treatment outcome during the therapy event so as to predict symptom relief. A novel technique has been developed for the segmentation and volume assessment of the treated region. Conventional algorithms typically require user interaction ora priori knowledge of the target. The developed algorithm exploits the treatment plan, the coordinates of the intended ablation, for fully automatic segmentation with no user input. A good similarity to an expert-segmented manual reference was achieved (Dice similarity coefficient = 0.880 ± 0.074). The average automatic segmentation time was 1.6 ± 0.7 min per patient against an order of tens of minutes when done manually. The results suggest that the segmentation algorithm developed, requiring no user-input, provides a feasible and practical approach for the automatic evaluation of the boundary and volume of the HIFU-treated region.

  16. Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation

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

    Akhbardeh, Alireza; Jacobs, Michael A.; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205

    2012-04-15

    Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), andmore » diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B{sub 1} inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and

  17. High-throughput microcoil NMR of compound libraries using zero-dispersion segmented flow analysis.

    PubMed

    Kautz, Roger A; Goetzinger, Wolfgang K; Karger, Barry L

    2005-01-01

    An automated system for loading samples into a microcoil NMR probe has been developed using segmented flow analysis. This approach enhanced 2-fold the throughput of the published direct injection and flow injection methods, improved sample utilization 3-fold, and was applicable to high-field NMR facilities with long transfer lines between the sample handler and NMR magnet. Sample volumes of 2 microL (10-30 mM, approximately 10 microg) were drawn from a 96-well microtiter plate by a sample handler, then pumped to a 0.5-microL microcoil NMR probe as a queue of closely spaced "plugs" separated by an immiscible fluorocarbon fluid. Individual sample plugs were detected by their NMR signal and automatically positioned for stopped-flow data acquisition. The sample in the NMR coil could be changed within 35 s by advancing the queue. The fluorocarbon liquid wetted the wall of the Teflon transfer line, preventing the DMSO samples from contacting the capillary wall and thus reducing sample losses to below 5% after passage through the 3-m transfer line. With a wash plug of solvent between samples, sample-to-sample carryover was <1%. Significantly, the samples did not disperse into the carrier liquid during loading or during acquisitions of several days for trace analysis. For automated high-throughput analysis using a 16-second acquisition time, spectra were recorded at a rate of 1.5 min/sample and total deuterated solvent consumption was <0.5 mL (1 US dollar) per 96-well plate.

  18. Sensitivity analysis for future space missions with segmented telescopes for high-contrast imaging

    NASA Astrophysics Data System (ADS)

    Leboulleux, Lucie; Pueyo, Laurent; Sauvage, Jean-François; Mazoyer, Johan; Soummer, Remi; Fusco, Thierry; Sivaramakrishnan, Anand

    2018-01-01

    The detection and analysis of biomarkers on earth-like planets using direct-imaging will require both high-contrast imaging and spectroscopy at very close angular separation (10^10 star to planet flux ratio at a few 0.1”). This goal can only be achieved with large telescopes in space to overcome atmospheric turbulence, often combined with a coronagraphic instrument with wavefront control. Large segmented space telescopes such as studied for the LUVOIR mission will generate segment-level instabilities and cophasing errors in addition to local mirror surface errors and other aberrations of the overall optical system. These effects contribute directly to the degradation of the final image quality and contrast. We present an analytical model that produces coronagraphic images of a segmented pupil telescope in the presence of segment phasing aberrations expressed as Zernike polynomials. This model relies on a pair-based projection of the segmented pupil and provides results that match an end-to-end simulation with an rms error on the final contrast of ~3%. This analytical model can be applied both to static and dynamic modes, and either in monochromatic or broadband light. It retires the need for end-to-end Monte-Carlo simulations that are otherwise needed to build a rigorous error budget, by enabling quasi-instantaneous analytical evaluations. The ability to invert directly the analytical model provides direct constraints and tolerances on all segments-level phasing and aberrations.

  19. Anthropometric and biomechanical characteristics on body segments of Koreans.

    PubMed

    Park, S J; Kim, C B; Park, S C

    1999-05-01

    This paper documents the physical measurements of the Korean population in order to construct a data base for ergonomic design. The dimension, volume, density, mass, and center of mass of Koreans whose ages range from 7 to 49 were investigated. Sixty-five male subjects and sixty-nine female subjects participated. Eight body segments (head with neck, trunk, thigh, shank, foot, upper arm, forearm and hand) were directly measured with a Martin-type anthropometer, and the immersion method was adopted to measure the volume of body segments. After this, densities were computed by the density equations in Drillis and Contini (1966). The reaction board method was employed for the measurement of the center of mass. Obtained data were compared with the results in the literature. The results in this paper showed different features on body segment parameters comparing with the results in the literature. The constructed data base can be applied to statistical guideline for product design, workspace design, design of clothing and tools, furniture design and construction of biomechanical models for Korean. Also, they can be extended to the application areas for Mongolian.

  20. Automated liver segmentation using a normalized probabilistic atlas

    NASA Astrophysics Data System (ADS)

    Linguraru, Marius George; Li, Zhixi; Shah, Furhawn; Chin, See; Summers, Ronald M.

    2009-02-01

    Probabilistic atlases of anatomical organs, especially the brain and the heart, have become popular in medical image analysis. We propose the construction of probabilistic atlases which retain structural variability by using a size-preserving modified affine registration. The organ positions are modeled in the physical space by normalizing the physical organ locations to an anatomical landmark. In this paper, a liver probabilistic atlas is constructed and exploited to automatically segment liver volumes from abdominal CT data. The atlas is aligned with the patient data through a succession of affine and non-linear registrations. The overlap and correlation with manual segmentations are 0.91 (0.93 DICE coefficient) and 0.99 respectively. Little work has taken place on the integration of volumetric measures of liver abnormality to clinical evaluations, which rely on linear estimates of liver height. Our application measures the liver height at the mid-hepatic line (0.94 correlation with manual measurements) and indicates that its combination with volumetric estimates could assist the development of a noninvasive tool to assess hepatomegaly.

  1. Magnetic resonance brain tissue segmentation based on sparse representations

    NASA Astrophysics Data System (ADS)

    Rueda, Andrea

    2015-12-01

    Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).

  2. A variational approach to liver segmentation using statistics from multiple sources

    NASA Astrophysics Data System (ADS)

    Zheng, Shenhai; Fang, Bin; Li, Laquan; Gao, Mingqi; Wang, Yi

    2018-01-01

    Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of 6.5 +/- 2.8 % , the best root mean square symmetric surface distance (RMSD) of 2.1 +/- 0.8 mm, the best maximum symmetric surface distance (MSD) of 18.9 +/- 8.3 mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of 0.8 +/- 0.5 mm, the best RMSD of 1.5 +/- 1.1 mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.

  3. Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

    PubMed

    Lu, Fang; Wu, Fa; Hu, Peijun; Peng, Zhiyi; Kong, Dexing

    2017-02-01

    Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

  4. Segmentation of 3d Models for Cultural Heritage Structural Analysis - Some Critical Issues

    NASA Astrophysics Data System (ADS)

    Gonizzi Barsanti, S.; Guidi, G.; De Luca, L.

    2017-08-01

    Cultural Heritage documentation and preservation has become a fundamental concern in this historical period. 3D modelling offers a perfect aid to record ancient buildings and artefacts and can be used as a valid starting point for restoration, conservation and structural analysis, which can be performed by using Finite Element Methods (FEA). The models derived from reality-based techniques, made up of the exterior surfaces of the objects captured at high resolution, are - for this reason - made of millions of polygons. Such meshes are not directly usable in structural analysis packages and need to be properly pre-processed in order to be transformed in volumetric meshes suitable for FEA. In addition, dealing with ancient objects, a proper segmentation of 3D volumetric models is needed to analyse the behaviour of the structure with the most suitable level of detail for the different sections of the structure under analysis. Segmentation of 3D models is still an open issue, especially when dealing with ancient, complicated and geometrically complex objects that imply the presence of anomalies and gaps, due to environmental agents such as earthquakes, pollution, wind and rain, or human factors. The aims of this paper is to critically analyse some of the different methodologies and algorithms available to segment a 3D point cloud or a mesh, identifying difficulties and problems by showing examples on different structures.

  5. Bayesian automated cortical segmentation for neonatal MRI

    NASA Astrophysics Data System (ADS)

    Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

    2017-11-01

    Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

  6. Segmentation of facial bone surfaces by patch growing from cone beam CT volumes

    PubMed Central

    Lilja, Mikko; Kalke, Martti

    2016-01-01

    Objectives: The motivation behind this work was to design an automatic algorithm capable of segmenting the exterior of the dental and facial bones including the mandible, teeth, maxilla and zygomatic bone with an open surface (a surface with a boundary) from CBCT images for the anatomy-based reconstruction of radiographs. Such an algorithm would provide speed, consistency and improved image quality for clinical workflows, for example, in planning of implants. Methods: We used CBCT images from two studies: first to develop (n = 19) and then to test (n = 30) a segmentation pipeline. The pipeline operates by parameterizing the topology and shape of the target, searching for potential points on the facial bone–soft tissue edge, reconstructing a triangular mesh by growing patches on from the edge points with good contrast and regularizing the result with a surface polynomial. This process is repeated for convergence. Results: The output of the algorithm was benchmarked against a hand-drawn reference and reached a 0.50 ± 1.0-mm average and 1.1-mm root mean squares error in Euclidean distance from the reference to our automatically segmented surface. These results were achieved with images affected by inhomogeneity, noise and metal artefacts that are typical for dental CBCT. Conclusions: Previously, this level of accuracy and precision in dental CBCT has been reported in segmenting only the mandible, a much easier target. The segmentation results were consistent throughout the data set and the pipeline was found fast enough (<1-min average computation time) to be considered for clinical use. PMID:27482878

  7. Unsupervised segmentation of MRI knees using image partition forests

    NASA Astrophysics Data System (ADS)

    Marčan, Marija; Voiculescu, Irina

    2016-03-01

    Nowadays many people are affected by arthritis, a condition of the joints with limited prevention measures, but with various options of treatment the most radical of which is surgical. In order for surgery to be successful, it can make use of careful analysis of patient-based models generated from medical images, usually by manual segmentation. In this work we show how to automate the segmentation of a crucial and complex joint -- the knee. To achieve this goal we rely on our novel way of representing a 3D voxel volume as a hierarchical structure of partitions which we have named Image Partition Forest (IPF). The IPF contains several partition layers of increasing coarseness, with partitions nested across layers in the form of adjacency graphs. On the basis of a set of properties (size, mean intensity, coordinates) of each node in the IPF we classify nodes into different features. Values indicating whether or not any particular node belongs to the femur or tibia are assigned through node filtering and node-based region growing. So far we have evaluated our method on 15 MRI knee images. Our unsupervised segmentation compared against a hand-segmented gold standard has achieved an average Dice similarity coefficient of 0.95 for femur and 0.93 for tibia, and an average symmetric surface distance of 0.98 mm for femur and 0.73 mm for tibia. The paper also discusses ways to introduce stricter morphological and spatial conditioning in the bone labelling process.

  8. Volumetric Assessment of Swallowing Muscles: A Comparison of CT and MRI Segmentation.

    PubMed

    Sporns, Kim Barbara; Hanning, Uta; Schmidt, Rene; Muhle, Paul; Wirth, Rainer; Zimmer, Sebastian; Dziewas, Rainer; Suntrup-Krueger, Sonja; Sporns, Peter Bernhard; Heindel, Walter; Schwindt, Wolfram

    2018-05-01

     Recent retrospective studies have proposed a high correlation between atrophy of swallowing muscles, age, severity of dysphagia and aspiration status based on computed tomography (CT). However, ionizing radiation poses an ethical barrier to research in prospective non-patient populations. Hence, there is a need to prove the efficacy of techniques that rely on noninvasive methods and produce high-resolution soft tissue images such as magnetic resonance imaging (MRI). The objective of this study was therefore to compare the segmentation results of swallowing muscles using CT and MRI.  Retrospective study of 21 patients (median age: 46.6; gender: 11 female) who underwent CT and MRI of the head and neck region within a time frame of less than 50 days because of suspected head and neck cancer using contrast agent. CT and MR images were segmented by two blinded readers using Medical Imaging Toolkit (MITK) and both modalities were tested (with the equivalence test) regarding the segmented muscle volumes. Adjustment for multiple testing was performed using the Bonferroni test and the potential time effect of the muscle volumes and the time interval between the modalities was assessed by a spearman correlation. The study was approved by the local ethics committee.  The median volumes for each muscle belly of the digastric muscle derived from CT were 3051 mm 3 (left) and 2969 mm 3 (right), and from MRI they were 3218 mm 3 (left) and 3027 mm 3 (right). The median volume of the geniohyoid muscle was 6580 mm 3 on CT and 6648 mm 3 on MRI. The interrater reliability was high for all segmented muscles. The mean time interval between the CT and MRI examinations was 34 days (IQR 25; 41). The muscle differences of each muscle between the two modalities did not reveal significant correlation to the time interval between the examinations (digastric left r = 0.003 and digastric right r = -0.008; geniohyoid muscle r = 0.075).  CT-based segmentation and

  9. Sensitivity analysis for high-contrast missions with segmented telescopes

    NASA Astrophysics Data System (ADS)

    Leboulleux, Lucie; Sauvage, Jean-François; Pueyo, Laurent; Fusco, Thierry; Soummer, Rémi; N'Diaye, Mamadou; St. Laurent, Kathryn

    2017-09-01

    Segmented telescopes 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. In this context, we present an analytical model that will enable to establish a comprehensive error budget to evaluate the constraints on the segments and the influence of the error terms on the final image and contrast. Indeed, the target contrast of 1010 to image Earth-like planets requires drastic conditions, both in term of segment alignment and telescope stability. Despite space telescopes evolving in a more friendly environment than ground-based telescopes, remaining vibrations and resonant modes on the segments can still deteriorate the contrast. In this communication, we develop and validate the analytical model, and compare its outputs to images issued from end-to-end simulations.

  10. A volumetric pulmonary CT segmentation method with applications in emphysema assessment

    NASA Astrophysics Data System (ADS)

    Silva, José Silvestre; Silva, Augusto; Santos, Beatriz S.

    2006-03-01

    A segmentation method is a mandatory pre-processing step in many automated or semi-automated analysis tasks such as region identification and densitometric analysis, or even for 3D visualization purposes. In this work we present a fully automated volumetric pulmonary segmentation algorithm based on intensity discrimination and morphologic procedures. Our method first identifies the trachea as well as primary bronchi and then the pulmonary region is identified by applying a threshold and morphologic operations. When both lungs are in contact, additional procedures are performed to obtain two separated lung volumes. To evaluate the performance of the method, we compared contours extracted from 3D lung surfaces with reference contours, using several figures of merit. Results show that the worst case generally occurs at the middle sections of high resolution CT exams, due the presence of aerial and vascular structures. Nevertheless, the average error is inferior to the average error associated with radiologist inter-observer variability, which suggests that our method produces lung contours similar to those drawn by radiologists. The information created by our segmentation algorithm is used by an identification and representation method in pulmonary emphysema that also classifies emphysema according to its severity degree. Two clinically proved thresholds are applied which identify regions with severe emphysema, and with highly severe emphysema. Based on this thresholding strategy, an application for volumetric emphysema assessment was developed offering new display paradigms concerning the visualization of classification results. This framework is easily extendable to accommodate other classifiers namely those related with texture based segmentation as it is often the case with interstitial diseases.

  11. Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age.

    PubMed

    Guo, Ting; Winterburn, Julie L; Pipitone, Jon; Duerden, Emma G; Park, Min Tae M; Chau, Vann; Poskitt, Kenneth J; Grunau, Ruth E; Synnes, Anne; Miller, Steven P; Mallar Chakravarty, M

    2015-01-01

    The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life. First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images. These manual segmentations are considered the gold standard in assessing the automatic segmentations. MAGeT-Brain, automatic hippocampal segmentation pipeline, requires only a small number of input atlases and reduces the registration and resampling errors by employing an intermediate template library. We assess the segmentation accuracy of MAGeT-Brain in three validation studies, evaluate the hippocampal growth from early-in-life to term-equivalent age, and study the effect of preterm birth on the hippocampal volume. The first experiment thoroughly validates MAGeT-Brain segmentation in three sets of 10-fold Monte Carlo cross-validation (MCCV) analyses with 187 different groups of input atlases and templates. The second experiment segments the neonatal hippocampi on 168 early-in-life and 154 term images and evaluates the hippocampal growth rate of 125 infants from early-in-life to term-equivalent age. The third experiment analyzes the effect of gestational age (GA) at birth on the average hippocampal volume at early-in-life and term-equivalent age using linear regression. The final segmentations demonstrate that MAGeT-Brain consistently provides accurate segmentations in comparison to manually

  12. Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age

    PubMed Central

    Guo, Ting; Winterburn, Julie L.; Pipitone, Jon; Duerden, Emma G.; Park, Min Tae M.; Chau, Vann; Poskitt, Kenneth J.; Grunau, Ruth E.; Synnes, Anne; Miller, Steven P.; Mallar Chakravarty, M.

    2015-01-01

    Introduction The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life. Methods First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images. These manual segmentations are considered the gold standard in assessing the automatic segmentations. MAGeT-Brain, automatic hippocampal segmentation pipeline, requires only a small number of input atlases and reduces the registration and resampling errors by employing an intermediate template library. We assess the segmentation accuracy of MAGeT-Brain in three validation studies, evaluate the hippocampal growth from early-in-life to term-equivalent age, and study the effect of preterm birth on the hippocampal volume. The first experiment thoroughly validates MAGeT-Brain segmentation in three sets of 10-fold Monte Carlo cross-validation (MCCV) analyses with 187 different groups of input atlases and templates. The second experiment segments the neonatal hippocampi on 168 early-in-life and 154 term images and evaluates the hippocampal growth rate of 125 infants from early-in-life to term-equivalent age. The third experiment analyzes the effect of gestational age (GA) at birth on the average hippocampal volume at early-in-life and term-equivalent age using linear regression. Results The final segmentations demonstrate that MAGeT-Brain consistently provides accurate segmentations

  13. Segmental Interactions between Polymers and Small Molecules in Batteries and Biofuel Purification

    NASA Astrophysics Data System (ADS)

    Balsara, Nitash

    2015-03-01

    Polymers such as poly(ethylene oxide) (PEO) and poly(dimethyl siloxane) (PDMS) have the potential to play an important role in the emerging clean energy landscape. Mixtures of PEO and lithium salts are the most widely studied non-flammable electrolyte for rechargeable lithium batteries. PDMS membranes are ideally suited for purifying bioethanol and biobutanol from fermentation broths. The ability of PEO and PDMS to function in these applications depends on segmental interactions between the polymeric host and small molecule guests. One experimental approach for studying these interactions is X-ray absorption spectroscopy (XAS). Models for interpreting XAS spectra of amorphous mixtures and charged species such as salts must quantify the effect of segmental interactions on the electronic structure of the atoms of interest (e.g. sulfur). This combination of experiment and theory is used to determine the species formed in during charging and discharging lithium-sulfur batteries; the theoretical specific energy of lithium-sulfur batteries is a factor of four larger than that of current lithium-ion batteries. Selective transport of alcohols in PDMS-containing membranes is controlled by the size, shape, and connectivity of sub-nanometer cavities or free volume that form and disappear spontaneously as the chain segments undergo Brownian motion. We demonstrate that self-assembly of PDMS-containing block copolymers can be used to control segmental relaxation, which, in turn, affects free volume. Positron annihilation was used to determine the size distribution of free volume cavities in the PDMS-containing block copolymers. The effect of this artificial free volume on selective permeation of alcohols formed by fermentation of sugars derived from lignocellulosic biomass is studied. Molecular dynamics simulations are needed to understand the relationship between self-assembly, free volume, and transport in block copolymers.

  14. Automated choroid segmentation of three-dimensional SD-OCT images by incorporating EDI-OCT images.

    PubMed

    Chen, Qiang; Niu, Sijie; Fang, Wangyi; Shuai, Yuanlu; Fan, Wen; Yuan, Songtao; Liu, Qinghuai

    2018-05-01

    The measurement of choroidal volume is more related with eye diseases than choroidal thickness, because the choroidal volume can reflect the diseases comprehensively. The purpose is to automatically segment choroid for three-dimensional (3D) spectral domain optical coherence tomography (SD-OCT) images. We present a novel choroid segmentation strategy for SD-OCT images by incorporating the enhanced depth imaging OCT (EDI-OCT) images. The down boundary of the choroid, namely choroid-sclera junction (CSJ), is almost invisible in SD-OCT images, while visible in EDI-OCT images. During the SD-OCT imaging, the EDI-OCT images can be generated for the same eye. Thus, we present an EDI-OCT-driven choroid segmentation method for SD-OCT images, where the choroid segmentation results of the EDI-OCT images are used to estimate the average choroidal thickness and to improve the construction of the CSJ feature space of the SD-OCT images. We also present a whole registration method between EDI-OCT and SD-OCT images based on retinal thickness and Bruch's Membrane (BM) position. The CSJ surface is obtained with a 3D graph search in the CSJ feature space. Experimental results with 768 images (6 cubes, 128 B-scan images for each cube) from 2 healthy persons, 2 age-related macular degeneration (AMD) and 2 diabetic retinopathy (DR) patients, and 210 B-scan images from other 8 healthy persons and 21 patients demonstrate that our method can achieve high segmentation accuracy. The mean choroid volume difference and overlap ratio for 6 cubes between our proposed method and outlines drawn by experts were -1.96µm3 and 88.56%, respectively. Our method is effective for the 3D choroid segmentation of SD-OCT images because the segmentation accuracy and stability are compared with the manual segmentation. Copyright © 2017. Published by Elsevier B.V.

  15. Development of a hip joint model for finite volume simulations.

    PubMed

    Cardiff, P; Karač, A; FitzPatrick, D; Ivanković, A

    2014-01-01

    This paper establishes a procedure for numerical analysis of a hip joint using the finite volume method. Patient-specific hip joint geometry is segmented directly from computed tomography and magnetic resonance imaging datasets and the resulting bone surfaces are processed into a form suitable for volume meshing. A high resolution continuum tetrahedral mesh has been generated, where a sandwich model approach is adopted; the bones are represented as a stiffer cortical shells surrounding more flexible cancellous cores. Cartilage is included as a uniform thickness extruded layer and the effect of layer thickness is investigated. To realistically position the bones, gait analysis has been performed giving the 3D positions of the bones for the full gait cycle. Three phases of the gait cycle are examined using a finite volume based custom structural contact solver implemented in open-source software OpenFOAM.

  16. A workflow for the automatic segmentation of organelles in electron microscopy image stacks

    PubMed Central

    Perez, Alex J.; Seyedhosseini, Mojtaba; Deerinck, Thomas J.; Bushong, Eric A.; Panda, Satchidananda; Tasdizen, Tolga; Ellisman, Mark H.

    2014-01-01

    Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime. PMID:25426032

  17. Finite Volume Methods: Foundation and Analysis

    NASA Technical Reports Server (NTRS)

    Barth, Timothy; Ohlberger, Mario

    2003-01-01

    Finite volume methods are a class of discretization schemes that have proven highly successful in approximating the solution of a wide variety of conservation law systems. They are extensively used in fluid mechanics, porous media flow, meteorology, electromagnetics, models of biological processes, semi-conductor device simulation and many other engineering areas governed by conservative systems that can be written in integral control volume form. This article reviews elements of the foundation and analysis of modern finite volume methods. The primary advantages of these methods are numerical robustness through the obtention of discrete maximum (minimum) principles, applicability on very general unstructured meshes, and the intrinsic local conservation properties of the resulting schemes. Throughout this article, specific attention is given to scalar nonlinear hyperbolic conservation laws and the development of high order accurate schemes for discretizing them. A key tool in the design and analysis of finite volume schemes suitable for non-oscillatory discontinuity capturing is discrete maximum principle analysis. A number of building blocks used in the development of numerical schemes possessing local discrete maximum principles are reviewed in one and several space dimensions, e.g. monotone fluxes, E-fluxes, TVD discretization, non-oscillatory reconstruction, slope limiters, positive coefficient schemes, etc. When available, theoretical results concerning a priori and a posteriori error estimates are given. Further advanced topics are then considered such as high order time integration, discretization of diffusion terms and the extension to systems of nonlinear conservation laws.

  18. Optical Coherence Tomography (OCT) Device Independent Intraretinal Layer Segmentation

    PubMed Central

    Ehnes, Alexander; Wenner, Yaroslava; Friedburg, Christoph; Preising, Markus N.; Bowl, Wadim; Sekundo, Walter; zu Bexten, Erdmuthe Meyer; Stieger, Knut; Lorenz, Birgit

    2014-01-01

    Purpose To develop and test an algorithm to segment intraretinal layers irrespectively of the actual Optical Coherence Tomography (OCT) device used. Methods The developed algorithm is based on the graph theory optimization. The algorithm's performance was evaluated against that of three expert graders for unsigned boundary position difference and thickness measurement of a retinal layer group in 50 and 41 B-scans, respectively. Reproducibility of the algorithm was tested in 30 C-scans of 10 healthy subjects each with the Spectralis and the Stratus OCT. Comparability between different devices was evaluated in 84 C-scans (volume or radial scans) obtained from 21 healthy subjects, two scans per subject with the Spectralis OCT, and one scan per subject each with the Stratus OCT and the RTVue-100 OCT. Each C-scan was segmented and the mean thickness for each retinal layer in sections of the early treatment of diabetic retinopathy study (ETDRS) grid was measured. Results The algorithm was able to segment up to 11 intraretinal layers. Measurements with the algorithm were within the 95% confidence interval of a single grader and the difference was smaller than the interindividual difference between the expert graders themselves. The cross-device examination of ETDRS-grid related layer thicknesses highly agreed between the three OCT devices. The algorithm correctly segmented a C-scan of a patient with X-linked retinitis pigmentosa. Conclusions The segmentation software provides device-independent, reliable, and reproducible analysis of intraretinal layers, similar to what is obtained from expert graders. Translational Relevance Potential application of the software includes routine clinical practice and multicenter clinical trials. PMID:24820053

  19. Three-Dimensional Eyeball and Orbit Volume Modification After LeFort III Midface Distraction.

    PubMed

    Smektala, Tomasz; Nysjö, Johan; Thor, Andreas; Homik, Aleksandra; Sporniak-Tutak, Katarzyna; Safranow, Krzysztof; Dowgierd, Krzysztof; Olszewski, Raphael

    2015-07-01

    The aim of our study was to evaluate orbital volume modification with LeFort III midface distraction in patients with craniosynostosis and its influence on eyeball volume and axial diameter modification. Orbital volume was assessed by the semiautomatic segmentation method based on deformable surface models and on 3-dimensional (3D) interaction with haptics. The eyeball volumes and diameters were automatically calculated after manual segmentation of computed tomographic scans with 3D slicer software. The mean, minimal, and maximal differences as well as the standard deviation and intraclass correlation coefficient (ICC) for intraobserver and interobserver measurements reliability were calculated. The Wilcoxon signed rank test was used to compare measured values before and after surgery. P < 0.05 was considered statistically significant. Intraobserver and interobserver ICC for haptic-aided semiautomatic orbital volume measurements were 0.98 and 0.99, respectively. The intraobserver and interobserver ICC values for manual segmentation of the eyeball volume were 0.87 and 0.86, respectively. The orbital volume increased significantly after surgery: 30.32% (mean, 5.96  mL) for the left orbit and 31.04% (mean, 6.31  mL) for the right orbit. The mean increase in eyeball volume was 12.3%. The mean increases in the eyeball axial dimensions were 7.3%, 9.3%, and 4.4% for the X-, Y-, and Z-axes, respectively. The Wilcoxon signed rank test showed that preoperative and postoperative eyeball volumes, as well as the diameters along the X- and Y-axes, were statistically significant. Midface distraction in patients with syndromic craniostenosis results in a significant increase (P < 0.05) in the orbit and eyeball volumes. The 2 methods (haptic-aided semiautomatic segmentation and manual 3D slicer segmentation) are reproducible techniques for orbit and eyeball volume measurements.

  20. Oil-spill risk analysis: Cook inlet outer continental shelf lease sale 149. Volume 2: Conditional risk contour maps of seasonal conditional probabilities. Final report

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

    Johnson, W.R.; Marshall, C.F.; Anderson, C.M.

    1994-08-01

    The Federal Government has proposed to offer Outer Continental Shelf (OCS) lands in Cook Inlet for oil and gas leasing. Because oil spills may occur from activities associated with offshore oil production, the Minerals Management Service conducts a formal risk assessment. In evaluating the significance of accidental oil spills, it is important to remember that the occurrence of such spills is fundamentally probabilistic. The effects of oil spills that could occur during oil and gas production must be considered. This report summarizes results of an oil-spill risk analysis conducted for the proposed Cook Inlet OCS Lease Sale 149. The objectivemore » of this analysis was to estimate relative risks associated with oil and gas production for the proposed lease sale. To aid the analysis, conditional risk contour maps of seasonal conditional probabilities of spill contact were generated for each environmental resource or land segment in the study area. This aspect is discussed in this volume of the two volume report.« less

  1. CT Urography: Segmentation of Urinary Bladder using CLASS with Local Contour Refinement

    PubMed Central

    Cha, Kenny; Hadjiiski, Lubomir; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Zhou, Chuan

    2016-01-01

    Purpose We are developing a computerized system for bladder segmentation on CT urography (CTU), as a critical component for computer-aided detection of bladder cancer. Methods The presence of regions filled with intravenous contrast and without contrast presents a challenge for bladder segmentation. Previously, we proposed a Conjoint Level set Analysis and Segmentation System (CLASS). In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast-filled (C) region separately and automatically conjoins the NC and C region contours; however, inaccuracies in the NC and C region contours may cause the conjoint contour to exclude portions of the bladder. To alleviate this problem, we implemented a local contour refinement (LCR) method that exploits model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP). MGR propagates the C region contours if the level set propagation in the C region stops prematurely due to substantial non-uniformity of the contrast. EDWP with regularized energies further propagates the conjoint contours to the correct bladder boundary. EDWP uses changes in energies, smoothness criteria of the contour, and previous slice contour to determine when to stop the propagation, following decision rules derived from training. A data set of 173 cases was collected for this study: 81 cases in the training set (42 lesions, 21 wall thickenings, 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, 13 normal bladders). For all cases, 3D hand segmented contours were obtained as reference standard and used for the evaluation of the computerized segmentation accuracy. Results For CLASS with LCR, the average volume intersection ratio, average volume error, absolute average volume error, average minimum distance and Jaccard index were 84.2±11.4%, 8.2±17.4%, 13.0±14.1%, 3.5±1.9 mm, 78.8±11.6%, respectively, for the training set and 78.0±14.7%, 16.4±16.9%, 18.2

  2. Reliability of Semi-Automated Segmentations in Glioblastoma.

    PubMed

    Huber, T; Alber, G; Bette, S; Boeckh-Behrens, T; Gempt, J; Ringel, F; Alberts, E; Zimmer, C; Bauer, J S

    2017-06-01

    In glioblastoma, quantitative volumetric measurements of contrast-enhancing or fluid-attenuated inversion recovery (FLAIR) hyperintense tumor compartments are needed for an objective assessment of therapy response. The aim of this study was to evaluate the reliability of a semi-automated, region-growing segmentation tool for determining tumor volume in patients with glioblastoma among different users of the software. A total of 320 segmentations of tumor-associated FLAIR changes and contrast-enhancing tumor tissue were performed by different raters (neuroradiologists, medical students, and volunteers). All patients underwent high-resolution magnetic resonance imaging including a 3D-FLAIR and a 3D-MPRage sequence. Segmentations were done using a semi-automated, region-growing segmentation tool. Intra- and inter-rater-reliability were addressed by intra-class-correlation (ICC). Root-mean-square error (RMSE) was used to determine the precision error. Dice score was calculated to measure the overlap between segmentations. Semi-automated segmentation showed a high ICC (> 0.985) for all groups indicating an excellent intra- and inter-rater-reliability. Significant smaller precision errors and higher Dice scores were observed for FLAIR segmentations compared with segmentations of contrast-enhancement. Single rater segmentations showed the lowest RMSE for FLAIR of 3.3 % (MPRage: 8.2 %). Both, single raters and neuroradiologists had the lowest precision error for longitudinal evaluation of FLAIR changes. Semi-automated volumetry of glioblastoma was reliably performed by all groups of raters, even without neuroradiologic expertise. Interestingly, segmentations of tumor-associated FLAIR changes were more reliable than segmentations of contrast enhancement. In longitudinal evaluations, an experienced rater can detect progressive FLAIR changes of less than 15 % reliably in a quantitative way which could help to detect progressive disease earlier.

  3. Automatic brain tissue segmentation based on graph filter.

    PubMed

    Kong, Youyong; Chen, Xiaopeng; Wu, Jiasong; Zhang, Pinzheng; Chen, Yang; Shu, Huazhong

    2018-05-09

    Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals. The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset. The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

  4. BDNF is Associated With Age-Related Decline in Hippocampal Volume

    PubMed Central

    Erickson, Kirk I.; Prakash, Ruchika Shaurya; Voss, Michelle W.; Chaddock, Laura; Heo, Susie; McLaren, Molly; Pence, Brandt D.; Martin, Stephen A.; Vieira, Victoria J.; Woods, Jeffrey A.; Kramer, Arthur F.

    2010-01-01

    Hippocampal volume shrinks in late adulthood, but the neuromolecular factors that trigger hippocampal decay in aging humans remains a matter of speculation. In rodents, brain derived neurotrophic factor (BDNF) promotes the growth and proliferation of cells in the hippocampus and is important in long-term potentiation and memory formation. In humans, circulating levels of BDNF decline with advancing age and a genetic polymorphism for BDNF has been related to gray matter volume loss in old age. In this study, we tested whether age-related reductions in serum levels of BDNF would be related to shrinkage of the hippocampus and memory deficits in older adults. Hippocampal volume was acquired by automated segmentation of magnetic resonance images in 142 older adults without dementia. The caudate nucleus was also segmented and examined in relation to levels of serum BDNF. Spatial memory was tested using a paradigm in which memory load was parametrically increased. We found that increasing age was associated with smaller hippocampal volumes, reduced levels of serum BDNF, and poorer memory performance. Lower levels of BDNF were associated with smaller hippocampi and poorer memory, even when controlling for the variation related to age. In an exploratory mediation analysis, hippocampal volume mediated the age-related decline in spatial memory and BDNF mediated the age-related decline in hippocampal volume. Caudate nucleus volume was unrelated to BDNF levels or spatial memory performance. Our results identify serum BDNF as a significant factor related to hippocampal shrinkage and memory decline in late adulthood. PMID:20392958

  5. A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

    NASA Astrophysics Data System (ADS)

    Agn, Mikael; Law, Ian; Munck af Rosenschöld, Per; Van Leemput, Koen

    2016-03-01

    We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machines to model tumor shape. The method is not tuned to any specific imaging protocol and can simultaneously segment the gross tumor volume, peritumoral edema and healthy tissue structures relevant for radiotherapy planning. We validate the method on a manually delineated clinical data set of glioblastoma patients by comparing segmentations of gross tumor volume, brainstem and hippocampus. The preliminary results demonstrate the feasibility of the method.

  6. 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

  7. Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods

    PubMed Central

    de Bresser, Jeroen; Hendrikse, Jeroen; Siero, Jeroen C. W.; Petersen, Esben T.; De Vis, Jill B.

    2018-01-01

    Objective In previous work we have developed a fast sequence that focusses on cerebrospinal fluid (CSF) based on the long T2 of CSF. By processing the data obtained with this CSF MRI sequence, brain parenchymal volume (BPV) and intracranial volume (ICV) can be automatically obtained. The aim of this study was to assess the precision of the BPV and ICV measurements of the CSF MRI sequence and to validate the CSF MRI sequence by comparison with 3D T1-based brain segmentation methods. Materials and methods Ten healthy volunteers (2 females; median age 28 years) were scanned (3T MRI) twice with repositioning in between. The scan protocol consisted of a low resolution (LR) CSF sequence (0:57min), a high resolution (HR) CSF sequence (3:21min) and a 3D T1-weighted sequence (6:47min). Data of the HR 3D-T1-weighted images were downsampled to obtain LR T1-weighted images (reconstructed imaging time: 1:59 min). Data of the CSF MRI sequences was automatically segmented using in-house software. The 3D T1-weighted images were segmented using FSL (5.0), SPM12 and FreeSurfer (5.3.0). Results The mean absolute differences for BPV and ICV between the first and second scan for CSF LR (BPV/ICV: 12±9/7±4cc) and CSF HR (5±5/4±2cc) were comparable to FSL HR (9±11/19±23cc), FSL LR (7±4, 6±5cc), FreeSurfer HR (5±3/14±8cc), FreeSurfer LR (9±8, 12±10cc), and SPM HR (5±3/4±7cc), and SPM LR (5±4, 5±3cc). The correlation between the measured volumes of the CSF sequences and that measured by FSL, FreeSurfer and SPM HR and LR was very good (all Pearson’s correlation coefficients >0.83, R2 .67–.97). The results from the downsampled data and the high-resolution data were similar. Conclusion Both CSF MRI sequences have a precision comparable to, and a very good correlation with established 3D T1-based automated segmentations methods for the segmentation of BPV and ICV. However, the short imaging time of the fast CSF MRI sequence is superior to the 3D T1 sequence on which

  8. Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease

    PubMed Central

    Kim, Youngwoo; Ge, Yinghui; Tao, Cheng; Zhu, Jianbing; Chapman, Arlene B.; Torres, Vicente E.; Yu, Alan S.L.; Mrug, Michal; Bennett, William M.; Flessner, Michael F.; Landsittel, Doug P.

    2016-01-01

    Background and objectives Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method. Design, setting, participants, & measurements Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2–weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed. Results Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable

  9. Segmentation-based retrospective shading correction in fluorescence microscopy E. coli images for quantitative analysis

    NASA Astrophysics Data System (ADS)

    Mai, Fei; Chang, Chunqi; Liu, Wenqing; Xu, Weichao; Hung, Yeung S.

    2009-10-01

    Due to the inherent imperfections in the imaging process, fluorescence microscopy images often suffer from spurious intensity variations, which is usually referred to as intensity inhomogeneity, intensity non uniformity, shading or bias field. In this paper, a retrospective shading correction method for fluorescence microscopy Escherichia coli (E. Coli) images is proposed based on segmentation result. Segmentation and shading correction are coupled together, so we iteratively correct the shading effects based on segmentation result and refine the segmentation by segmenting the image after shading correction. A fluorescence microscopy E. Coli image can be segmented (based on its intensity value) into two classes: the background and the cells, where the intensity variation within each class is close to zero if there is no shading. Therefore, we make use of this characteristics to correct the shading in each iteration. Shading is mathematically modeled as a multiplicative component and an additive noise component. The additive component is removed by a denoising process, and the multiplicative component is estimated using a fast algorithm to minimize the intra-class intensity variation. We tested our method on synthetic images and real fluorescence E.coli images. It works well not only for visual inspection, but also for numerical evaluation. Our proposed method should be useful for further quantitative analysis especially for protein expression value comparison.

  10. Effects of voxelization on dose volume histogram accuracy

    NASA Astrophysics Data System (ADS)

    Sunderland, Kyle; Pinter, Csaba; Lasso, Andras; Fichtinger, Gabor

    2016-03-01

    PURPOSE: In radiotherapy treatment planning systems, structures of interest such as targets and organs at risk are stored as 2D contours on evenly spaced planes. In order to be used in various algorithms, contours must be converted into binary labelmap volumes using voxelization. The voxelization process results in lost information, which has little effect on the volume of large structures, but has significant impact on small structures, which contain few voxels. Volume differences for segmented structures affects metrics such as dose volume histograms (DVH), which are used for treatment planning. Our goal is to evaluate the impact of voxelization on segmented structures, as well as how factors like voxel size affects metrics, such as DVH. METHODS: We create a series of implicit functions, which represent simulated structures. These structures are sampled at varying resolutions, and compared to labelmaps with high sub-millimeter resolutions. We generate DVH and evaluate voxelization error for the same structures at different resolutions by calculating the agreement acceptance percentage between the DVH. RESULTS: We implemented tools for analysis as modules in the SlicerRT toolkit based on the 3D Slicer platform. We found that there were large DVH variation from the baseline for small structures or for structures located in regions with a high dose gradient, potentially leading to the creation of suboptimal treatment plans. CONCLUSION: This work demonstrates that labelmap and dose volume voxel size is an important factor in DVH accuracy, which must be accounted for in order to ensure the development of accurate treatment plans.

  11. Comparison of in vivo 3D cone-beam computed tomography tooth volume measurement protocols.

    PubMed

    Forst, Darren; Nijjar, Simrit; Flores-Mir, Carlos; Carey, Jason; Secanell, Marc; Lagravere, Manuel

    2014-12-23

    The objective of this study is to analyze a set of previously developed and proposed image segmentation protocols for precision in both intra- and inter-rater reliability for in vivo tooth volume measurements using cone-beam computed tomography (CBCT) images. Six 3D volume segmentation procedures were proposed and tested for intra- and inter-rater reliability to quantify maxillary first molar volumes. Ten randomly selected maxillary first molars were measured in vivo in random order three times with 10 days separation between measurements. Intra- and inter-rater agreement for all segmentation procedures was attained using intra-class correlation coefficient (ICC). The highest precision was for automated thresholding with manual refinements. A tooth volume measurement protocol for CBCT images employing automated segmentation with manual human refinement on a 2D slice-by-slice basis in all three planes of space possessed excellent intra- and inter-rater reliability. Three-dimensional volume measurements of the entire tooth structure are more precise than 3D volume measurements of only the dental roots apical to the cemento-enamel junction (CEJ).

  12. Automated boundary segmentation and wound analysis for longitudinal corneal OCT images

    NASA Astrophysics Data System (ADS)

    Wang, Fei; Shi, Fei; Zhu, Weifang; Pan, Lingjiao; Chen, Haoyu; Huang, Haifan; Zheng, Kangkeng; Chen, Xinjian

    2017-03-01

    Optical coherence tomography (OCT) has been widely applied in the examination and diagnosis of corneal diseases, but the information directly achieved from the OCT images by manual inspection is limited. We propose an automatic processing method to assist ophthalmologists in locating the boundaries in corneal OCT images and analyzing the recovery of corneal wounds after treatment from longitudinal OCT images. It includes the following steps: preprocessing, epithelium and endothelium boundary segmentation and correction, wound detection, corneal boundary fitting and wound analysis. The method was tested on a data set with longitudinal corneal OCT images from 20 subjects. Each subject has five images acquired after corneal operation over a period of time. The segmentation and classification accuracy of the proposed algorithm is high and can be used for analyzing wound recovery after corneal surgery.

  13. Cognitive, Social, and Literacy Competencies: The Chelsea Bank Simulation Project. Year One: Final Report. [Volume 2]: Appendices.

    ERIC Educational Resources Information Center

    Duffy, Thomas; And Others

    This supplementary volume presents appendixes A-E associated with a 1-year study which determined what secondary school students were doing as they engaged in the Chelsea Bank computer software simulation activities. Appendixes present the SCANS Analysis Coding Sheet; coding problem analysis of 50 video segments; student and teacher interview…

  14. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

    PubMed

    Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

    2017-11-01

    1 -MTV 2 , MTV 1 -GBSV, MTV 2 -GBSV; gray-levels: 64-32, 64-128, and 64-256; reconstruction algorithms: OSEM-FORE-OSEM, OSEM-FOREFBP, and OSEM-3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland-Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High- ±1% ≤ U/LRL ≤ ±30%; Intermediate- ±30% < U/LRL ≤ ±45%; Low- ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV 1 -GBSV than MTV 2 -GBSV, gray-level pairs of 64-32 and 64-128 than 64-256, and reconstruction algorithm pairs of OSEM-FOREIR and OSEM-FOREFBP than OSEM-3DRP. Although the choice of cervical tumor segmentation method, gray-level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray-level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

  15. Concurrent Tumor Segmentation and Registration with Uncertainty-based Sparse non-Uniform Graphs

    PubMed Central

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

    2014-01-01

    In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. PMID:24717540

  16. Automatically measuring brain ventricular volume within PACS using artificial intelligence.

    PubMed

    Yepes-Calderon, Fernando; Nelson, Marvin D; McComb, J Gordon

    2018-01-01

    The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system's daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

  17. Twelve automated thresholding methods for segmentation of PET images: a phantom study.

    PubMed

    Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M

    2012-06-21

    Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical (18)F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

  18. Twelve automated thresholding methods for segmentation of PET images: a phantom study

    NASA Astrophysics Data System (ADS)

    Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M.

    2012-06-01

    Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

  19. Volume analysis of heat-induced cracks in human molars: A preliminary study

    PubMed Central

    Sandholzer, Michael A.; Baron, Katharina; Heimel, Patrick; Metscher, Brian D.

    2014-01-01

    Context: Only a few methods have been published dealing with the visualization of heat-induced cracks inside bones and teeth. Aims: As a novel approach this study used nondestructive X-ray microtomography (micro-CT) for volume analysis of heat-induced cracks to observe the reaction of human molars to various levels of thermal stress. Materials and Methods: Eighteen clinically extracted third molars were rehydrated and burned under controlled temperatures (400, 650, and 800°C) using an electric furnace adjusted with a 25°C increase/min. The subsequent high-resolution scans (voxel-size 17.7 μm) were made with a compact micro-CT scanner (SkyScan 1174). In total, 14 scans were automatically segmented with Definiens XD Developer 1.2 and three-dimensional (3D) models were computed with Visage Imaging Amira 5.2.2. The results of the automated segmentation were analyzed with an analysis of variance (ANOVA) and uncorrected post hoc least significant difference (LSD) tests using Statistical Package for Social Sciences (SPSS) 17. A probability level of P < 0.05 was used as an index of statistical significance. Results: A temperature-dependent increase of heat-induced cracks was observed between the three temperature groups (P < 0.05, ANOVA post hoc LSD). In addition, the distributions and shape of the heat-induced changes could be classified using the computed 3D models. Conclusion: The macroscopic heat-induced changes observed in this preliminary study correspond with previous observations of unrestored human teeth, yet the current observations also take into account the entire microscopic 3D expansions of heat-induced cracks within the dental hard tissues. Using the same experimental conditions proposed in the literature, this study confirms previous results, adds new observations, and offers new perspectives in the investigation of forensic evidence. PMID:25125923

  20. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

    PubMed Central

    Cha, Kenny H.; Hadjiiski, Lubomir; Samala, Ravi K.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.

    2016-01-01

    Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13

  1. Automatic characterization and segmentation of human skin using three-dimensional optical coherence tomography

    NASA Astrophysics Data System (ADS)

    Hori, Yasuaki; Yasuno, Yoshiaki; Sakai, Shingo; Matsumoto, Masayuki; Sugawara, Tomoko; Madjarova, Violeta; Yamanari, Masahiro; Makita, Shuichi; Yasui, Takeshi; Araki, Tsutomu; Itoh, Masahide; Yatagai, Toyohiko

    2006-03-01

    A set of fully automated algorithms that is specialized for analyzing a three-dimensional optical coherence tomography (OCT) volume of human skin is reported. The algorithm set first determines the skin surface of the OCT volume, and a depth-oriented algorithm provides the mean epidermal thickness, distribution map of the epidermis, and a segmented volume of the epidermis. Subsequently, an en face shadowgram is produced by an algorithm to visualize the infundibula in the skin with high contrast. The population and occupation ratio of the infundibula are provided by a histogram-based thresholding algorithm and a distance mapping algorithm. En face OCT slices at constant depths from the sample surface are extracted, and the histogram-based thresholding algorithm is again applied to these slices, yielding a three-dimensional segmented volume of the infundibula. The dermal attenuation coefficient is also calculated from the OCT volume in order to evaluate the skin texture. The algorithm set examines swept-source OCT volumes of the skins of several volunteers, and the results show the high stability, portability and reproducibility of the algorithm.

  2. Partial volume correction and image analysis methods for intersubject comparison of FDG-PET studies

    NASA Astrophysics Data System (ADS)

    Yang, Jun

    2000-12-01

    Partial volume effect is an artifact mainly due to the limited imaging sensor resolution. It creates bias in the measured activity in small structures and around tissue boundaries. In brain FDG-PET studies, especially for Alzheimer's disease study where there is serious gray matter atrophy, accurate estimate of cerebral metabolic rate of glucose is even more problematic due to large amount of partial volume effect. In this dissertation, we developed a framework enabling inter-subject comparison of partial volume corrected brain FDG-PET studies. The framework is composed of the following image processing steps: (1)MRI segmentation, (2)MR-PET registration, (3)MR based PVE correction, (4)MR 3D inter-subject elastic mapping. Through simulation studies, we showed that the newly developed partial volume correction methods, either pixel based or ROI based, performed better than previous methods. By applying this framework to a real Alzheimer's disease study, we demonstrated that the partial volume corrected glucose rates vary significantly among the control, at risk and disease patient groups and this framework is a promising tool useful for assisting early identification of Alzheimer's patients.

  3. Volcanoes Distribution in Linear Segmentation of Mariana Arc

    NASA Astrophysics Data System (ADS)

    Andikagumi, H.; Macpherson, C.; McCaffrey, K. J. W.

    2016-12-01

    A new method has been developed to describe better volcanoes distribution pattern within Mariana Arc. A previous study assumed the distribution of volcanoes in the Mariana Arc is described by a small circle distribution which reflects the melting processes in a curved subduction zone. The small circle fit to this dataset used in the study, comprised 12 -mainly subaerial- volcanoes from Smithsonian Institute Global Volcanism Program, was reassessed by us to have a root-mean-square misfit of 2.5 km. The same method applied to a more complete dataset from Baker et al. (2008), consisting 37 subaerial and submarine volcanoes, resulted in an 8.4 km misfit. However, using the Hough Transform method on the larger dataset, lower misfits of great circle segments were achieved (3.1 and 3.0 km) for two possible segments combination. The results indicate that the distribution of volcanoes in the Mariana Arc is better described by a great circle pattern, instead of small circle. Variogram and cross-variogram analysis on volcano spacing and volume shows that there is spatial correlation between volcanoes between 420 and 500 km which corresponds to the maximum segmentation lengths from Hough Transform (320 km). Further analysis of volcano spacing by the coefficient of variation (Cv), shows a tendency toward not-random distribution as the Cv values are closer to zero than one. These distributions are inferred to be associated with the development of normal faults at the back arc as their Cv values also tend towards zero. To analyse whether volcano spacing is random or not, Cv values were simulated using a Monte Carlo method with random input. Only the southernmost segment has allowed us to reject the null hypothesis that volcanoes are randomly spaced at 95% confidence level by 0.007 estimated probability. This result shows infrequent regularity in volcano spacing by chance so that controlling factor in lithospheric scale should be analysed with different approach (not from random

  4. A low-cost three-dimensional laser surface scanning approach for defining body segment parameters.

    PubMed

    Pandis, Petros; Bull, Anthony Mj

    2017-11-01

    Body segment parameters are used in many different applications in ergonomics as well as in dynamic modelling of the musculoskeletal system. Body segment parameters can be defined using different methods, including techniques that involve time-consuming manual measurements of the human body, used in conjunction with models or equations. In this study, a scanning technique for measuring subject-specific body segment parameters in an easy, fast, accurate and low-cost way was developed and validated. The scanner can obtain the body segment parameters in a single scanning operation, which takes between 8 and 10 s. The results obtained with the system show a standard deviation of 2.5% in volumetric measurements of the upper limb of a mannequin and 3.1% difference between scanning volume and actual volume. Finally, the maximum mean error for the moment of inertia by scanning a standard-sized homogeneous object was 2.2%. This study shows that a low-cost system can provide quick and accurate subject-specific body segment parameter estimates.

  5. 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

  6. Texture analysis based on the Hermite transform for image classification and segmentation

    NASA Astrophysics Data System (ADS)

    Estudillo-Romero, Alfonso; Escalante-Ramirez, Boris; Savage-Carmona, Jesus

    2012-06-01

    Texture analysis has become an important task in image processing because it is used as a preprocessing stage in different research areas including medical image analysis, industrial inspection, segmentation of remote sensed imaginary, multimedia indexing and retrieval. In order to extract visual texture features a texture image analysis technique is presented based on the Hermite transform. Psychovisual evidence suggests that the Gaussian derivatives fit the receptive field profiles of mammalian visual systems. The Hermite transform describes locally basic texture features in terms of Gaussian derivatives. Multiresolution combined with several analysis orders provides detection of patterns that characterizes every texture class. The analysis of the local maximum energy direction and steering of the transformation coefficients increase the method robustness against the texture orientation. This method presents an advantage over classical filter bank design because in the latter a fixed number of orientations for the analysis has to be selected. During the training stage, a subset of the Hermite analysis filters is chosen in order to improve the inter-class separability, reduce dimensionality of the feature vectors and computational cost during the classification stage. We exhaustively evaluated the correct classification rate of real randomly selected training and testing texture subsets using several kinds of common used texture features. A comparison between different distance measurements is also presented. Results of the unsupervised real texture segmentation using this approach and comparison with previous approaches showed the benefits of our proposal.

  7. Analysis of image thresholding segmentation algorithms based on swarm intelligence

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

    2013-03-01

    Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

  8. Radiotherapy volume delineation using 18F-FDG-PET/CT modifies gross node volume in patients with oesophageal cancer.

    PubMed

    Jimenez-Jimenez, E; Mateos, P; Aymar, N; Roncero, R; Ortiz, I; Gimenez, M; Pardo, J; Salinas, J; Sabater, S

    2018-05-02

    Evidence supporting the use of 18F-FDG-PET/CT in the segmentation process of oesophageal cancer for radiotherapy planning is limited. Our aim was to compare the volumes and tumour lengths defined by fused PET/CT vs. CT simulation. Twenty-nine patients were analyzed. All patients underwent a single PET/CT simulation scan. Two separate GTVs were defined: one based on CT data alone and another based on fused PET/CT data. Volume sizes for both data sets were compared and the spatial overlap was assessed by the Dice similarity coefficient (DSC). The gross tumour volume (GTVtumour) and maximum tumour diameter were greater by PET/CT, and length of primary tumour was greater by CT, but differences were not statistically significant. However, the gross node volume (GTVnode) was significantly greater by PET/CT. The DSC analysis showed excellent agreement for GTVtumour, 0.72, but was very low for GTVnode, 0.25. Our study shows that the volume definition by PET/CT and CT data differs. CT simulation, without taking into account PET/CT information, might leave cancer-involved nodes out of the radiotherapy-delineated volumes.

  9. Mis-segmentation in voxel-based morphometry due to a signal intensity change in the putamen.

    PubMed

    Goto, Masami; Abe, Osamu; Miyati, Tosiaki; Aoki, Shigeki; Gomi, Tsutomu; Takeda, Tohoru

    2017-12-01

    The aims of this study were to demonstrate an association between changes in the signal intensity of the putamen on three-dimensional T1-weighted magnetic resonance images (3D-T1WI) and mis-segmentation, using the voxel-based morphometry (VBM) 8 toolbox. The sagittal 3D-T1WIs of 22 healthy volunteers were obtained for VBM analysis using the 1.5-T MR scanner. We prepared five levels of 3D-T1WI signal intensity (baseline, same level, background level, low level, and high level) in regions of interest containing the putamen. Groups of smoothed, spatially normalized tissue images were compared to the baseline group using a paired t test. The baseline was compared to the other four levels. In all comparisons, significant volume changes were observed around and outside the area that included the signal intensity change. The present study demonstrated an association between a change in the signal intensity of the putamen on 3D-T1WI and changed volume in segmented tissue images.

  10. Segmentation and Visual Analysis of Whole-Body Mouse Skeleton microSPECT

    PubMed Central

    Khmelinskii, Artem; Groen, Harald C.; Baiker, Martin; de Jong, Marion; Lelieveldt, Boudewijn P. F.

    2012-01-01

    Whole-body SPECT small animal imaging is used to study cancer, and plays an important role in the development of new drugs. Comparing and exploring whole-body datasets can be a difficult and time-consuming task due to the inherent heterogeneity of the data (high volume/throughput, multi-modality, postural and positioning variability). The goal of this study was to provide a method to align and compare side-by-side multiple whole-body skeleton SPECT datasets in a common reference, thus eliminating acquisition variability that exists between the subjects in cross-sectional and multi-modal studies. Six whole-body SPECT/CT datasets of BALB/c mice injected with bone targeting tracers 99mTc-methylene diphosphonate (99mTc-MDP) and 99mTc-hydroxymethane diphosphonate (99mTc-HDP) were used to evaluate the proposed method. An articulated version of the MOBY whole-body mouse atlas was used as a common reference. Its individual bones were registered one-by-one to the skeleton extracted from the acquired SPECT data following an anatomical hierarchical tree. Sequential registration was used while constraining the local degrees of freedom (DoFs) of each bone in accordance to the type of joint and its range of motion. The Articulated Planar Reformation (APR) algorithm was applied to the segmented data for side-by-side change visualization and comparison of data. To quantitatively evaluate the proposed algorithm, bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances between each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance decreased from 11.5±12.1 to 2.6±2.1 voxels. The proposed approach yielded satisfactory segmentation results with minimal user intervention. It proved to be robust for “incomplete” data (large chunks of skeleton missing) and for an intuitive exploration and comparison of multi-modal SPECT/CT cross

  11. [Automated detection and volumetric segmentation of the spleen in CT scans].

    PubMed

    Hammon, M; Dankerl, P; Kramer, M; Seifert, S; Tsymbal, A; Costa, M J; Janka, R; Uder, M; Cavallaro, A

    2012-08-01

    To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like "marginal space learning" and "boosting algorithms". 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm³ + 0.58 (width × length × thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 (± 2.4) seconds. The average splenic volume measured by aV was 268.21 ± 114.67 cm³ compared to 281.58 ± 130.21 cm³ in mV and 268.93 ± 104.60 cm³ in eV. The correlation coefficient was 0.99 (coefficient of determination (R²) = 0.98) for aV and mV, 0.91 (R² = 0.83) for mV and eV and 0.91 (R² = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R² = 0.84), mV and eV (0.95; R² = 0.91) and aV and eV (0.83; R² = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. © Georg Thieme Verlag KG Stuttgart · New York.

  12. Analysis of gene expression levels in individual bacterial cells without image segmentation.

    PubMed

    Kwak, In Hae; Son, Minjun; Hagen, Stephen J

    2012-05-11

    Studies of stochasticity in gene expression typically make use of fluorescent protein reporters, which permit the measurement of expression levels within individual cells by fluorescence microscopy. Analysis of such microscopy images is almost invariably based on a segmentation algorithm, where the image of a cell or cluster is analyzed mathematically to delineate individual cell boundaries. However segmentation can be ineffective for studying bacterial cells or clusters, especially at lower magnification, where outlines of individual cells are poorly resolved. Here we demonstrate an alternative method for analyzing such images without segmentation. The method employs a comparison between the pixel brightness in phase contrast vs fluorescence microscopy images. By fitting the correlation between phase contrast and fluorescence intensity to a physical model, we obtain well-defined estimates for the different levels of gene expression that are present in the cell or cluster. The method reveals the boundaries of the individual cells, even if the source images lack the resolution to show these boundaries clearly. Copyright © 2012 Elsevier Inc. All rights reserved.

  13. Consistent interactive segmentation of pulmonary ground glass nodules identified in CT studies

    NASA Astrophysics Data System (ADS)

    Zhang, Li; Fang, Ming; Naidich, David P.; Novak, Carol L.

    2004-05-01

    Ground glass nodules (GGNs) have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they characteristically have extremely slow rates of growth. This problem is further magnified by the small size of many of these lesions now being routinely detected following the introduction of multislice CT scanners capable of acquiring contiguous high resolution 1 to 1.25 mm sections throughout the thorax in a single breathhold period. Although segmentation of solid nodules can be used clinically to determine volume doubling times quantitatively, reliable methods for segmentation of pure ground glass nodules have yet to be introduced. Our purpose is to evaluate a newly developed computer-based segmentation method for rapid and reproducible measurements of pure ground glass nodules. 23 pure or mixed ground glass nodules were identified in a total of 8 patients by a radiologist and subsequently segmented by our computer-based method using Markov random field and shape analysis. The computer-based segmentation was initialized by a click point. Methodological consistency was assessed using the overlap ratio between 3 segmentations initialized by 3 different click points for each nodule. The 95% confidence interval on the mean of the overlap ratios proved to be [0.984, 0.998]. The computer-based method failed on two nodules that were difficult to segment even manually either due to especially low contrast or markedly irregular margins. While achieving consistent manual segmentation of ground glass nodules has proven problematic most often due to indistinct boundaries and interobserver variability, our proposed method introduces a powerful new tool for obtaining reproducible quantitative measurements of these lesions. It is our intention to further document the value of this approach with a still larger set of ground glass nodules.

  14. 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.

  15. Model-Based Segmentation of Cortical Regions of Interest for Multi-subject Analysis of fMRI Data

    NASA Astrophysics Data System (ADS)

    Engel, Karin; Brechmann, Andr'e.; Toennies, Klaus

    The high inter-subject variability of human neuroanatomy complicates the analysis of functional imaging data across subjects. We propose a method for the correct segmentation of cortical regions of interest based on the cortical surface. First results on the segmentation of Heschl's gyrus indicate the capability of our approach for correct comparison of functional activations in relation to individual cortical patterns.

  16. 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.

  17. Spider phobia is associated with decreased left amygdala volume: a cross-sectional study

    PubMed Central

    2013-01-01

    Background Evidence from animal and human studies imply the amygdala as the most critical structure involved in processing of fear-relevant stimuli. In phobias, the amygdala seems to play a crucial role in the pathogenesis and maintenance of the disorder. However, the neuropathology of specific phobias remains poorly understood. In the present study, we investigated whether patients with spider phobia show altered amygdala volumes as compared to healthy control subjects. Methods Twenty female patients with spider phobia and twenty age-matched healthy female controls underwent magnetic resonance imaging to investigate amygdala volumes. The amygdalae were segmented using an automatic, model-based segmentation tool (FSL FIRST). Differences in amygdala volume were investigated by multivariate analysis of covariance with group as between-subject factor and left and right amygdala as dependent factors. The relation between amygdala volume and clinical features such as symptom severity, disgust sensitivity, trait anxiety and duration of illness was investigated by Spearman correlation analysis. Results Spider phobic patients showed significantly smaller left amygdala volume than healthy controls. No significant difference in right amygdala volume was detected. Furthermore, the diminished amygdala size in patients was related to higher symptom severity, but not to higher disgust sensitivity or trait anxiety and was independent of age. Conclusions In summary, the results reveal a relation between higher symptom severity and smaller left amygdala volume in patients with spider phobia. This relation was independent of other potential confounders such as the disgust sensitivity or trait anxiety. The findings suggest that greater spider phobic fear is associated with smaller left amygdala. However, the smaller left amygdala volume may either stand for a higher vulnerability to develop a phobic disorder or emerge as a consequence of the disorder. PMID:23442196

  18. SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells.

    PubMed

    Stylianidou, Stella; Brennan, Connor; Nissen, Silas B; Kuwada, Nathan J; Wiggins, Paul A

    2016-11-01

    Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution. © 2016 John Wiley & Sons Ltd.

  19. Computer Aided Segmentation Analysis: New Software for College Admissions Marketing.

    ERIC Educational Resources Information Center

    Lay, Robert S.; Maguire, John J.

    1983-01-01

    Compares segmentation solutions obtained using a binary segmentation algorithm (THAID) and a new chi-square-based procedure (CHAID) that segments the prospective pool of college applicants using application and matriculation as criteria. Results showed a higher number of estimated qualified inquiries and more accurate estimates with CHAID. (JAC)

  20. Analysis of a kinetic multi-segment foot model. Part I: Model repeatability and kinematic validity.

    PubMed

    Bruening, Dustin A; Cooney, Kevin M; Buczek, Frank L

    2012-04-01

    Kinematic multi-segment foot models are still evolving, but have seen increased use in clinical and research settings. The addition of kinetics may increase knowledge of foot and ankle function as well as influence multi-segment foot model evolution; however, previous kinetic models are too complex for clinical use. In this study we present a three-segment kinetic foot model and thorough evaluation of model performance during normal gait. In this first of two companion papers, model reference frames and joint centers are analyzed for repeatability, joint translations are measured, segment rigidity characterized, and sample joint angles presented. Within-tester and between-tester repeatability were first assessed using 10 healthy pediatric participants, while kinematic parameters were subsequently measured on 17 additional healthy pediatric participants. Repeatability errors were generally low for all sagittal plane measures as well as transverse plane Hindfoot and Forefoot segments (median<3°), while the least repeatable orientations were the Hindfoot coronal plane and Hallux transverse plane. Joint translations were generally less than 2mm in any one direction, while segment rigidity analysis suggested rigid body behavior for the Shank and Hindfoot, with the Forefoot violating the rigid body assumptions in terminal stance/pre-swing. Joint excursions were consistent with previously published studies. Copyright © 2012 Elsevier B.V. All rights reserved.

  1. Left atrial appendage segmentation and quantitative assisted diagnosis of atrial fibrillation based on fusion of temporal-spatial information.

    PubMed

    Jin, Cheng; Feng, Jianjiang; Wang, Lei; Yu, Heng; Liu, Jiang; Lu, Jiwen; Zhou, Jie

    2018-05-01

    In this paper, we present an approach for left atrial appendage (LAA) multi-phase fast segmentation and quantitative assisted diagnosis of atrial fibrillation (AF) based on 4D-CT data. We take full advantage of the temporal dimension information to segment the living, flailed LAA based on a parametric max-flow method and graph-cut approach to build 3-D model of each phase. To assist the diagnosis of AF, we calculate the volumes of 3-D models, and then generate a "volume-phase" curve to calculate the important dynamic metrics: ejection fraction, filling flux, and emptying flux of the LAA's blood by volume. This approach demonstrates more precise results than the conventional approaches that calculate metrics by area, and allows for the quick analysis of LAA-volume pattern changes of in a cardiac cycle. It may also provide insight into the individual differences in the lesions of the LAA. Furthermore, we apply support vector machines (SVMs) to achieve a quantitative auto-diagnosis of the AF by exploiting seven features from volume change ratios of the LAA, and perform multivariate logistic regression analysis for the risk of LAA thrombosis. The 100 cases utilized in this research were taken from the Philips 256-iCT. The experimental results demonstrate that our approach can construct the 3-D LAA geometries robustly compared to manual annotations, and reasonably infer that the LAA undergoes filling, emptying and re-filling, re-emptying in a cardiac cycle. This research provides a potential for exploring various physiological functions of the LAA and quantitatively estimating the risk of stroke in patients with AF. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. 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.

  3. 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.

  4. Anatomical pulmonary magnetic resonance imaging segmentation for regional structure-function measurements of asthma

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

    Guo, F.; Graduate Program in Biomedical Engineering, University of Western Ontario, London, Ontario N6A 5B9; Svenningsen, S.

    Purpose: Pulmonary magnetic-resonance-imaging (MRI) and x-ray computed-tomography have provided strong evidence of spatially and temporally persistent lung structure-function abnormalities in asthmatics. This has generated a shift in their understanding of lung disease and supports the use of imaging biomarkers as intermediate endpoints of asthma severity and control. In particular, pulmonary {sup 1}H MRI can be used to provide quantitative lung structure-function measurements longitudinally and in response to treatment. However, to translate such biomarkers of asthma, robust methods are required to segment the lung from pulmonary {sup 1}H MRI. Therefore, their objective was to develop a pulmonary {sup 1}H MRI segmentationmore » algorithm to provide regional measurements with the precision and speed required to support clinical studies. Methods: The authors developed a method to segment the left and right lung from {sup 1}H MRI acquired in 20 asthmatics including five well-controlled and 15 severe poorly controlled participants who provided written informed consent to a study protocol approved by Health Canada. Same-day spirometry and plethysmography measurements of lung function and volume were acquired as well as {sup 1}H MRI using a whole-body radiofrequency coil and fast spoiled gradient-recalled echo sequence at a fixed lung volume (functional residual capacity + 1 l). We incorporated the left-to-right lung volume proportion prior based on the Potts model and derived a volume-proportion preserved Potts model, which was approximated through convex relaxation and further represented by a dual volume-proportion preserved max-flow model. The max-flow model led to a linear problem with convex and linear equality constraints that implicitly encoded the proportion prior. To implement the algorithm, {sup 1}H MRI was resampled into ∼3 × 3 × 3 mm{sup 3} isotropic voxel space. Two observers placed seeds on each lung and on the background of 20 pulmonary {sup 1}H MR

  5. Innovative visualization and segmentation approaches for telemedicine

    NASA Astrophysics Data System (ADS)

    Nguyen, D.; Roehrig, Hans; Borders, Marisa H.; Fitzpatrick, Kimberly A.; Roveda, Janet

    2014-09-01

    In health care applications, we obtain, manage, store and communicate using high quality, large volume of image data through integrated devices. In this paper we propose several promising methods that can assist physicians in image data process and communication. We design a new semi-automated segmentation approach for radiological images, such as CT and MRI to clearly identify the areas of interest. This approach combines the advantages from both the region-based method and boundary-based methods. It has three key steps compose: coarse segmentation by using fuzzy affinity and homogeneity operator, image division and reclassification using the Voronoi Diagram, and refining boundary lines using the level set model.

  6. WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

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

    Lee, J; Nishikawa, R; Reiser, I

    Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benignmore » or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or

  7. CT liver volumetry using geodesic active contour segmentation with a level-set algorithm

    NASA Astrophysics Data System (ADS)

    Suzuki, Kenji; Epstein, Mark L.; Kohlbrenner, Ryan; Obajuluwa, Ademola; Xu, Jianwu; Hori, Masatoshi; Baron, Richard

    2010-03-01

    Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F<=f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.

  8. Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening.

    PubMed

    Kwak, Kichang; Yoon, Uicheul; Lee, Dong-Kyun; Kim, Geon Ha; Seo, Sang Won; Na, Duk L; Shim, Hack-Joon; Lee, Jong-Min

    2013-09-01

    The hippocampus has been known to be an important structure as a biomarker for Alzheimer's disease (AD) and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. In this study, an automated hippocampal segmentation method based on a graph-cuts algorithm combined with atlas-based segmentation and morphological opening was proposed. First of all, the atlas-based segmentation was applied to define initial hippocampal region for a priori information on graph-cuts. The definition of initial seeds was further elaborated by incorporating estimation of partial volume probabilities at each voxel. Finally, morphological opening was applied to reduce false positive of the result processed by graph-cuts. In the experiments with twenty-seven healthy normal subjects, the proposed method showed more reliable results (similarity index=0.81±0.03) than the conventional atlas-based segmentation method (0.72±0.04). Also as for segmentation accuracy which is measured in terms of the ratios of false positive and false negative, the proposed method (precision=0.76±0.04, recall=0.86±0.05) produced lower ratios than the conventional methods (0.73±0.05, 0.72±0.06) demonstrating its plausibility for accurate, robust and reliable segmentation of hippocampus. Copyright © 2013 Elsevier Inc. All rights reserved.

  9. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

    PubMed

    Rios Velazquez, Emmanuel; Aerts, Hugo J W L; Gu, Yuhua; Goldgof, Dmitry B; De Ruysscher, Dirk; Dekker, Andre; Korn, René; Gillies, Robert J; Lambin, Philippe

    2012-11-01

    To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC). For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org. High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96). Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  10. Segment and fit thresholding: a new method for image analysis applied to microarray and immunofluorescence data.

    PubMed

    Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E; Allen, Peter J; Sempere, Lorenzo F; Haab, Brian B

    2015-10-06

    Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu's method for selected images. SFT promises to advance the goal of full automation in image analysis.

  11. Segment and Fit Thresholding: A New Method for Image Analysis Applied to Microarray and Immunofluorescence Data

    PubMed Central

    Ensink, Elliot; Sinha, Jessica; Sinha, Arkadeep; Tang, Huiyuan; Calderone, Heather M.; Hostetter, Galen; Winter, Jordan; Cherba, David; Brand, Randall E.; Allen, Peter J.; Sempere, Lorenzo F.; Haab, Brian B.

    2016-01-01

    Certain experiments involve the high-throughput quantification of image data, thus requiring algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multi-color, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu’s method for selected images. SFT promises to advance the goal of full automation in image analysis. PMID:26339978

  12. Two and three-dimensional segmentation of hyperpolarized 3He magnetic resonance imaging of pulmonary gas distribution

    NASA Astrophysics Data System (ADS)

    Heydarian, Mohammadreza; Kirby, Miranda; Wheatley, Andrew; Fenster, Aaron; Parraga, Grace

    2012-03-01

    A semi-automated method for generating hyperpolarized helium-3 (3He) measurements of individual slice (2D) or whole lung (3D) gas distribution was developed. 3He MRI functional images were segmented using two-dimensional (2D) and three-dimensional (3D) hierarchical K-means clustering of the 3He MRI signal and in addition a seeded region-growing algorithm was employed for segmentation of the 1H MRI thoracic cavity volume. 3He MRI pulmonary function measurements were generated following two-dimensional landmark-based non-rigid registration of the 3He and 1H pulmonary images. We applied this method to MRI of healthy subjects and subjects with chronic obstructive lung disease (COPD). The results of hierarchical K-means 2D and 3D segmentation were compared to an expert observer's manual segmentation results using linear regression, Pearson correlations and the Dice similarity coefficient. 2D hierarchical K-means segmentation of ventilation volume (VV) and ventilation defect volume (VDV) was strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.97, p<.0001) and mean Dice coefficients were greater than 92% for all subjects. 3D hierarchical K-means segmentation of VV and VDV was also strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.64, p<.0001) and the mean Dice coefficients were greater than 91% for all subjects. Both 2D and 3D semi-automated segmentation of 3He MRI gas distribution provides a way to generate novel pulmonary function measurements.

  13. Using Predictability for Lexical Segmentation.

    PubMed

    Çöltekin, Çağrı

    2017-09-01

    This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic experiments as well as computational methods. However, despite strong empirical evidence, the explicit use of predictability of basic sub-lexical units in models of segmentation is underexplored. This paper presents an incremental computational model of lexical segmentation for exploring the usefulness of predictability for lexical segmentation. We show that the predictability cue is a strong cue for segmentation. Contrary to earlier reports in the literature, the strategy yields state-of-the-art segmentation performance with an incremental computational model that uses only this particular cue in a cognitively plausible setting. The paper also reports an in-depth analysis of the model, investigating the conditions affecting the usefulness of the strategy. Copyright © 2016 Cognitive Science Society, Inc.

  14. Connection method of separated luminal regions of intestine from CT volumes

    NASA Astrophysics Data System (ADS)

    Oda, Masahiro; Kitasaka, Takayuki; Furukawa, Kazuhiro; Watanabe, Osamu; Ando, Takafumi; Hirooka, Yoshiki; Goto, Hidemi; Mori, Kensaku

    2015-03-01

    This paper proposes a connection method of separated luminal regions of the intestine for Crohn's disease diagnosis. Crohn's disease is an inflammatory disease of the digestive tract. Capsule or conventional endoscopic diagnosis is performed for Crohn's disease diagnosis. However, parts of the intestines may not be observed in the endoscopic diagnosis if intestinal stenosis occurs. Endoscopes cannot pass through the stenosed parts. CT image-based diagnosis is developed as an alternative choice of the Crohn's disease. CT image-based diagnosis enables physicians to observe the entire intestines even if stenosed parts exist. CAD systems for Crohn's disease using CT volumes are recently developed. Such CAD systems need to reconstruct separated luminal regions of the intestines to analyze intestines. We propose a connection method of separated luminal regions of the intestines segmented from CT volumes. The luminal regions of the intestines are segmented from a CT volume. The centerlines of the luminal regions are calculated by using a thinning process. We enumerate all the possible sequences of the centerline segments. In this work, we newly introduce a condition using distance between connected ends points of the centerline segments. This condition eliminates unnatural connections of the centerline segments. Also, this condition reduces processing time. After generating a sequence list of the centerline segments, the correct sequence is obtained by using an evaluation function. We connect the luminal regions based on the correct sequence. Our experiments using four CT volumes showed that our method connected 6.5 out of 8.0 centerline segments per case. Processing times of the proposed method were reduced from the previous method.

  15. Automatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients.

    PubMed

    Wang, Jieqiong; Miao, Wen; Li, Jing; Li, Meng; Zhen, Zonglei; Sabel, Bernhard; Xian, Junfang; He, Huiguang

    2015-11-30

    The lateral geniculate nucleus (LGN) is a key relay center of the visual system. Because the LGN morphology is affected by different diseases, it is of interest to analyze its morphology by segmentation. However, existing LGN segmentation methods are non-automatic, inefficient and prone to experimenters' bias. To address these problems, we proposed an automatic LGN segmentation algorithm based on T1-weighted imaging. First, the prior information of LGN was used to create a prior mask. Then region growing was applied to delineate LGN. We evaluated this automatic LGN segmentation method by (1) comparison with manually segmented LGN, (2) anatomically locating LGN in the visual system via LGN-based tractography, (3) application to control and glaucoma patients. The similarity coefficients of automatic segmented LGN and manually segmented one are 0.72 (0.06) for the left LGN and 0.77 (0.07) for the right LGN. LGN-based tractography shows the subcortical pathway seeding from LGN passes the optic tract and also reaches V1 through the optic radiation, which is consistent with the LGN location in the visual system. In addition, LGN asymmetry as well as LGN atrophy along with age is observed in normal controls. The investigation of glaucoma effects on LGN volumes demonstrates that the bilateral LGN volumes shrink in patients. The automatic LGN segmentation is objective, efficient, valid and applicable. Experiment results proved the validity and applicability of the algorithm. Our method will speed up the research on visual system and greatly enhance studies of different vision-related diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

  16. Combining watershed and graph cuts methods to segment organs at risk in radiotherapy

    NASA Astrophysics Data System (ADS)

    Dolz, Jose; Kirisli, Hortense A.; Viard, Romain; Massoptier, Laurent

    2014-03-01

    Computer-aided segmentation of anatomical structures in medical images is a valuable tool for efficient radiation therapy planning (RTP). As delineation errors highly affect the radiation oncology treatment, it is crucial to delineate geometric structures accurately. In this paper, a semi-automatic segmentation approach for computed tomography (CT) images, based on watershed and graph-cuts methods, is presented. The watershed pre-segmentation groups small areas of similar intensities in homogeneous labels, which are subsequently used as input for the graph-cuts algorithm. This methodology does not require of prior knowledge of the structure to be segmented; even so, it performs well with complex shapes and low intensity. The presented method also allows the user to add foreground and background strokes in any of the three standard orthogonal views - axial, sagittal or coronal - making the interaction with the algorithm easy and fast. Hence, the segmentation information is propagated within the whole volume, providing a spatially coherent result. The proposed algorithm has been evaluated using 9 CT volumes, by comparing its segmentation performance over several organs - lungs, liver, spleen, heart and aorta - to those of manual delineation from experts. A Dicés coefficient higher than 0.89 was achieved in every case. That demonstrates that the proposed approach works well for all the anatomical structures analyzed. Due to the quality of the results, the introduction of the proposed approach in the RTP process will be a helpful tool for organs at risk (OARs) segmentation.

  17. Michigan urban trunkline segments safety performance functions (SPFs) : final report.

    DOT National Transportation Integrated Search

    2016-07-01

    This study involves the development of safety performance functions (SPFs) for urban and suburban trunkline segments in the : state of Michigan. Extensive databases were developed through the integration of traffic crash information, traffic volumes,...

  18. Market segmentation and analysis of Japan's residential post and beam construction market.

    Treesearch

    Joseph A. Roos; Ivan L. Eastin; Hisaaki Matsuguma

    2005-01-01

    A mail survey of Japanese post and beam builders was conducted to measure their level of ethnocentrism, market orientation, risk aversion, and price consciousness. The data were analyzed utilizing factor and cluster analysis. The results showed that Japanese post and beam builders can be divided into three distinct market segments: open to import...

  19. Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint.

    PubMed

    Liukkonen, Mimmi K; Mononen, Mika E; Tanska, Petri; Saarakkala, Simo; Nieminen, Miika T; Korhonen, Rami K

    2017-10-01

    Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

  20. Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT

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

    Yuan, Yading, E-mail: yading.yuan@mssm.edu; Chao, Ming; Sheu, Ren-Dih

    Purpose: This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. Methods: In order to track the fuzzy liver–chestwall and liver–heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border.more » The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors’ automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. Results: The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm{sup 3}, whereas it was 1719 cm{sup 3} for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. Conclusions: The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors’ results also

  1. Cost, volume and profitability analysis.

    PubMed

    Tarantino, David P

    2002-01-01

    If you want to increase your income by seeing more patients, it's important to figure out the financial impact such a move could have on your practice. Learn how to run a cost, volume, and profitability analysis to determine how business decisions can change your financial picture.

  2. Fully-automated segmentation of fluid regions in exudative age-related macular degeneration subjects: Kernel graph cut in neutrosophic domain

    PubMed Central

    Rashno, Abdolreza; Nazari, Behzad; Koozekanani, Dara D.; Drayna, Paul M.; Sadri, Saeed; Rabbani, Hossein

    2017-01-01

    A fully-automated method based on graph shortest path, graph cut and neutrosophic (NS) sets is presented for fluid segmentation in OCT volumes for exudative age related macular degeneration (EAMD) subjects. The proposed method includes three main steps: 1) The inner limiting membrane (ILM) and the retinal pigment epithelium (RPE) layers are segmented using proposed methods based on graph shortest path in NS domain. A flattened RPE boundary is calculated such that all three types of fluid regions, intra-retinal, sub-retinal and sub-RPE, are located above it. 2) Seed points for fluid (object) and tissue (background) are initialized for graph cut by the proposed automated method. 3) A new cost function is proposed in kernel space, and is minimized with max-flow/min-cut algorithms, leading to a binary segmentation. Important properties of the proposed steps are proven and quantitative performance of each step is analyzed separately. The proposed method is evaluated using a publicly available dataset referred as Optima and a local dataset from the UMN clinic. For fluid segmentation in 2D individual slices, the proposed method outperforms the previously proposed methods by 18%, 21% with respect to the dice coefficient and sensitivity, respectively, on the Optima dataset, and by 16%, 11% and 12% with respect to the dice coefficient, sensitivity and precision, respectively, on the local UMN dataset. Finally, for 3D fluid volume segmentation, the proposed method achieves true positive rate (TPR) and false positive rate (FPR) of 90% and 0.74%, respectively, with a correlation of 95% between automated and expert manual segmentations using linear regression analysis. PMID:29059257

  3. Texture analysis improves level set segmentation of the anterior abdominal wall

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

    Xu, Zhoubing; Allen, Wade M.; Baucom, Rebeccah B.

    2013-12-15

    Purpose: The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore,more » to optimize intervention.Methods: In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors’ approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall.Results: Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2–5 mm) for methods that did not use the texture.Conclusions: The authors’ approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and

  4. Automated detection and segmentation of follicles in 3D ultrasound for assisted reproduction

    NASA Astrophysics Data System (ADS)

    Narayan, Nikhil S.; Sivanandan, Srinivasan; Kudavelly, Srinivas; Patwardhan, Kedar A.; Ramaraju, G. A.

    2018-02-01

    Follicle quantification refers to the computation of the number and size of follicles in 3D ultrasound volumes of the ovary. This is one of the key factors in determining hormonal dosage during female infertility treatments. In this paper, we propose an automated algorithm to detect and segment follicles in 3D ultrasound volumes of the ovary for quantification. In a first of its kind attempt, we employ noise-robust phase symmetry feature maps as likelihood function to perform mean-shift based follicle center detection. Max-flow algorithm is used for segmentation and gray weighted distance transform is employed for post-processing the results. We have obtained state-of-the-art results with a true positive detection rate of >90% on 26 3D volumes with 323 follicles.

  5. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.

    PubMed

    Men, Kuo; Dai, Jianrong; Li, Yexiong

    2017-12-01

    Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures. Our DDCNN method was an end-to-end architecture enabling fast training and testing. Specifically, it employed a novel multiple-scale convolutional architecture to extract multiple-scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto-segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel-wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the planning computed tomography (CT) images. A total of 218 patients chosen randomly were used for training, and the remaining 60 for validation. The Dice similarity coefficient (DSC) was used to measure segmentation accuracy. Performance was evaluated on segmentation of the CTV and OARs. In addition, the performance of DDCNN was compared with that of U-Net. The proposed DDCNN method outperformed the U-Net for all segmentations, and the average DSC value of DDCNN was 3.8% higher than that of U-Net. Mean DSC values of DDCNN were 87.7% for the CTV, 93.4% for the bladder, 92.1% for the left femoral head, 92.3% for the right femoral head, 65.3% for the intestine, and 61.8% for the colon. The test time was 45 s per patient for segmentation of all the CTV, bladder, left and right femoral heads, colon, and intestine. We also assessed our approaches and results with those in the literature: our system showed superior

  6. Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla.

    PubMed

    Fallah, Faezeh; Machann, Jürgen; Martirosian, Petros; Bamberg, Fabian; Schick, Fritz; Yang, Bin

    2017-04-01

    To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE). The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m 2 (30.02 ± 6.63 kg/m 2 ) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes. Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images. Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.

  7. Market segmentation for multiple option healthcare delivery systems--an application of cluster analysis.

    PubMed

    Jarboe, G R; Gates, R H; McDaniel, C D

    1990-01-01

    Healthcare providers of multiple option plans may be confronted with special market segmentation problems. This study demonstrates how cluster analysis may be used for discovering distinct patterns of preference for multiple option plans. The availability of metric, as opposed to categorical or ordinal, data provides the ability to use sophisticated analysis techniques which may be superior to frequency distributions and cross-tabulations in revealing preference patterns.

  8. 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

  9. A Virtual Reality System for PTCD Simulation Using Direct Visuo-Haptic Rendering of Partially Segmented Image Data.

    PubMed

    Fortmeier, Dirk; Mastmeyer, Andre; Schröder, Julian; Handels, Heinz

    2016-01-01

    This study presents a new visuo-haptic virtual reality (VR) training and planning system for percutaneous transhepatic cholangio-drainage (PTCD) based on partially segmented virtual patient models. We only use partially segmented image data instead of a full segmentation and circumvent the necessity of surface or volume mesh models. Haptic interaction with the virtual patient during virtual palpation, ultrasound probing and needle insertion is provided. Furthermore, the VR simulator includes X-ray and ultrasound simulation for image-guided training. The visualization techniques are GPU-accelerated by implementation in Cuda and include real-time volume deformations computed on the grid of the image data. Computation on the image grid enables straightforward integration of the deformed image data into the visualization components. To provide shorter rendering times, the performance of the volume deformation algorithm is improved by a multigrid approach. To evaluate the VR training system, a user evaluation has been performed and deformation algorithms are analyzed in terms of convergence speed with respect to a fully converged solution. The user evaluation shows positive results with increased user confidence after a training session. It is shown that using partially segmented patient data and direct volume rendering is suitable for the simulation of needle insertion procedures such as PTCD.

  10. Fuzzy object models for newborn brain MR image segmentation

    NASA Astrophysics Data System (ADS)

    Kobashi, Syoji; Udupa, Jayaram K.

    2013-03-01

    Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

  11. Metric Learning for Hyperspectral Image Segmentation

    NASA Technical Reports Server (NTRS)

    Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

    2011-01-01

    We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

  12. Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

    NASA Astrophysics Data System (ADS)

    Zhu, Weifang; Zhang, Li; Shi, Fei; Xiang, Dehui; Wang, Lirong; Guo, Jingyun; Yang, Xiaoling; Chen, Haoyu; Chen, Xinjian

    2017-07-01

    Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate the initialization results. Second, an automated shape-constrained graph cut algorithm is applied to obtain the refined results. Finally, cyst area information is used to remove false positives (FPs). The method was evaluated on 19 eyes with coexistence of CMEs and MH from 18 subjects. The true positive volume fraction, FP volume fraction, dice similarity coefficient, and accuracy rate for CMEs segmentation were 81.0%±7.8%, 0.80%±0.63%, 80.9%±5.7%, and 99.7%±0.1%, respectively.

  13. Alzheimer's disease detection using 11C-PiB with improved partial volume effect correction

    NASA Astrophysics Data System (ADS)

    Raniga, Parnesh; Bourgeat, Pierrick; Fripp, Jurgen; Acosta, Oscar; Ourselin, Sebastien; Rowe, Christopher; Villemagne, Victor L.; Salvado, Olivier

    2009-02-01

    Despite the increasing use of 11C-PiB in research into Alzheimer's disease (AD), there are few standardized analysis procedures that have been reported or published. This is especially true with regards to partial volume effects (PVE) and partial volume correction. Due to the nature of PET physics and acquisition, PET images exhibit relatively low spatial resolution compared to other modalities, resulting in bias of quantitative results. Although previous studies have applied PVE correction techniques on 11C-PiB data, the results have not been quantitatively evaluated and compared against uncorrected data. The aim of this study is threefold. Firstly, a realistic synthetic phantom was created to quantify PVE. Secondly, MRI partial volume estimate segmentations were used to improve voxel-based PVE correction instead of using hard segmentations. Thirdly, quantification of PVE correction was evaluated on 34 subjects (AD=10, Normal Controls (NC)=24), including 12 PiB positive NC. Regional analysis was performed using the Anatomical Automatic Labeling (AAL) template, which was registered to each patient. Regions of interest were restricted to the gray matter (GM) defined by the MR segmentation. Average normalized intensity of the neocortex and selected regions were used to evaluate the discrimination power between AD and NC both with and without PVE correction. Receiver Operating Characteristic (ROC) curves were computed for the binary discrimination task. The phantom study revealed signal losses due to PVE between 10 to 40 % which were mostly recovered to within 5% after correction. Better classification was achieved after PVE correction, resulting in higher areas under ROC curves.

  14. Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity

    NASA Astrophysics Data System (ADS)

    He, Nana; Zhang, Xiaolong; Zhao, Juanjuan; Zhao, Huilan; Qiang, Yan

    2017-07-01

    While the popular thin layer scanning technology of spiral CT has helped to improve diagnoses of lung diseases, the large volumes of scanning images produced by the technology also dramatically increase the load of physicians in lesion detection. Computer-aided diagnosis techniques like lesions segmentation in thin CT sequences have been developed to address this issue, but it remains a challenge to achieve high segmentation efficiency and accuracy without much involvement of human manual intervention. In this paper, we present our research on automated segmentation of lung parenchyma with an improved geodesic active contour model that is geodesic active contour model based on similarity (GACBS). Combining spectral clustering algorithm based on Nystrom (SCN) with GACBS, this algorithm first extracts key image slices, then uses these slices to generate an initial contour of pulmonary parenchyma of un-segmented slices with an interpolation algorithm, and finally segments lung parenchyma of un-segmented slices. Experimental results show that the segmentation results generated by our method are close to what manual segmentation can produce, with an average volume overlap ratio of 91.48%.

  15. Improving vertebra segmentation through joint vertebra-rib atlases

    NASA Astrophysics Data System (ADS)

    Wang, Yinong; Yao, Jianhua; Roth, Holger R.; Burns, Joseph E.; Summers, Ronald M.

    2016-03-01

    Accurate spine segmentation allows for improved identification and quantitative characterization of abnormalities of the vertebra, such as vertebral fractures. However, in existing automated vertebra segmentation methods on computed tomography (CT) images, leakage into nearby bones such as ribs occurs due to the close proximity of these visibly intense structures in a 3D CT volume. To reduce this error, we propose the use of joint vertebra-rib atlases to improve the segmentation of vertebrae via multi-atlas joint label fusion. Segmentation was performed and evaluated on CTs containing 106 thoracic and lumbar vertebrae from 10 pathological and traumatic spine patients on an individual vertebra level basis. Vertebra atlases produced errors where the segmentation leaked into the ribs. The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92.5 +/- 3.1% to 93.8 +/- 2.1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0.75 +/- 0.60mm and 8.63 +/- 4.44mm to 0.30 +/- 0.27mm and 3.65 +/- 2.87mm, respectively.

  16. Improved assessment of body cell mass by segmental bioimpedance analysis in malnourished subjects and acromegaly.

    PubMed

    Pirlich, M; Schütz, T; Ockenga, J; Biering, H; Gerl, H; Schmidt, B; Ertl, S; Plauth, M; Lochs, H

    2003-04-01

    Estimation of body cell mass (BCM) has been regarded valuable for the assessment of malnutrition. To investigate the value of segmental bioelectrical impedance analysis (BIA) for BCM estimation in malnourished subjects and acromegaly. Nineteen controls and 63 patients with either reduced (liver cirrhosis without and with ascites, Cushing's disease) or increased BCM (acromegaly) were included. Whole-body and segmental BIA (separately measuring arm, trunk, leg) at 50 kHz was compared with BCM measured by total-body potassium. Multiple regression analysis was used to develop specific equations for BCM in each subgroup. Compared to whole-body BIA equations, the inclusion of arm resistance improved the specific equation in cirrhotic patients without ascites and in Cushing's disease resulting in excellent prediction of BCM (R(2) = 0.93 and 0.92, respectively; both P<0.001). In acromegaly, inclusion of resistance and reactance of the trunk best described BCM (R(2) = 0.94, P<0.001). In controls and in cirrhotic patients with ascites, segmental impedance parameters did not improve BCM prediction (best values obtained by whole-body measurements: R(2)=0.88 and 0.60; P<0.001 and <0.003, respectively). Segmental BIA improves the assessment of BCM in malnourished patients and acromegaly, but not in patients with severe fluid overload. Copyright 2003 Elsevier Science Ltd.

  17. Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images.

    PubMed

    Hanaoka, Shouhei; Masutani, Yoshitaka; Nemoto, Mitsutaka; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Hayashi, Naoto; Ohtomo, Kuni; Shimizu, Akinobu

    2017-03-01

    A fully automatic multiatlas-based method for segmentation of the spine and pelvis in a torso CT volume is proposed. A novel landmark-guided diffeomorphic demons algorithm is used to register a given CT image to multiple atlas volumes. This algorithm can utilize both grayscale image information and given landmark coordinate information optimally. The segmentation has four steps. Firstly, 170 bony landmarks are detected in the given volume. Using these landmark positions, an atlas selection procedure is performed to reduce the computational cost of the following registration. Then the chosen atlas volumes are registered to the given CT image. Finally, voxelwise label voting is performed to determine the final segmentation result. The proposed method was evaluated using 50 torso CT datasets as well as the public SpineWeb dataset. As a result, a mean distance error of [Formula: see text] and a mean Dice coefficient of [Formula: see text] were achieved for the whole spine and the pelvic bones, which are competitive with other state-of-the-art methods. From the experimental results, the usefulness of the proposed segmentation method was validated.

  18. Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.

    PubMed

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

    2014-05-01

    In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. A sensitivity analysis method for the body segment inertial parameters based on ground reaction and joint moment regressor matrices.

    PubMed

    Futamure, Sumire; Bonnet, Vincent; Dumas, Raphael; Venture, Gentiane

    2017-11-07

    This paper presents a method allowing a simple and efficient sensitivity analysis of dynamics parameters of complex whole-body human model. The proposed method is based on the ground reaction and joint moment regressor matrices, developed initially in robotics system identification theory, and involved in the equations of motion of the human body. The regressor matrices are linear relatively to the segment inertial parameters allowing us to use simple sensitivity analysis methods. The sensitivity analysis method was applied over gait dynamics and kinematics data of nine subjects and with a 15 segments 3D model of the locomotor apparatus. According to the proposed sensitivity indices, 76 segments inertial parameters out the 150 of the mechanical model were considered as not influent for gait. The main findings were that the segment masses were influent and that, at the exception of the trunk, moment of inertia were not influent for the computation of the ground reaction forces and moments and the joint moments. The same method also shows numerically that at least 90% of the lower-limb joint moments during the stance phase can be estimated only from a force-plate and kinematics data without knowing any of the segment inertial parameters. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Analysis of gene expression levels in individual bacterial cells without image segmentation

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

    Kwak, In Hae; Son, Minjun; Hagen, Stephen J., E-mail: sjhagen@ufl.edu

    2012-05-11

    Highlights: Black-Right-Pointing-Pointer We present a method for extracting gene expression data from images of bacterial cells. Black-Right-Pointing-Pointer The method does not employ cell segmentation and does not require high magnification. Black-Right-Pointing-Pointer Fluorescence and phase contrast images of the cells are correlated through the physics of phase contrast. Black-Right-Pointing-Pointer We demonstrate the method by characterizing noisy expression of comX in Streptococcus mutans. -- Abstract: Studies of stochasticity in gene expression typically make use of fluorescent protein reporters, which permit the measurement of expression levels within individual cells by fluorescence microscopy. Analysis of such microscopy images is almost invariably based on amore » segmentation algorithm, where the image of a cell or cluster is analyzed mathematically to delineate individual cell boundaries. However segmentation can be ineffective for studying bacterial cells or clusters, especially at lower magnification, where outlines of individual cells are poorly resolved. Here we demonstrate an alternative method for analyzing such images without segmentation. The method employs a comparison between the pixel brightness in phase contrast vs fluorescence microscopy images. By fitting the correlation between phase contrast and fluorescence intensity to a physical model, we obtain well-defined estimates for the different levels of gene expression that are present in the cell or cluster. The method reveals the boundaries of the individual cells, even if the source images lack the resolution to show these boundaries clearly.« less

  1. Renal cortex segmentation using optimal surface search with novel graph construction.

    PubMed

    Li, Xiuli; Chen, Xinjian; Yao, Jianhua; Zhang, Xing; Tian, Jie

    2011-01-01

    In this paper, we propose a novel approach to solve the renal cortex segmentation problem, which has rarely been studied. In this study, the renal cortex segmentation problem is handled as a multiple-surfaces extraction problem, which is solved using the optimal surface search method. We propose a novel graph construction scheme in the optimal surface search to better accommodate multiple surfaces. Different surface sub-graphs are constructed according to their properties, and inter-surface relationships are also modeled in the graph. The proposed method was tested on 17 clinical CT datasets. The true positive volume fraction (TPVF) and false positive volume fraction (FPVF) are 74.10% and 0.08%, respectively. The experimental results demonstrate the effectiveness of the proposed method.

  2. Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

    NASA Astrophysics Data System (ADS)

    Zhang, Honghai; Abiose, Ademola K.; Campbell, Dwayne N.; Sonka, Milan; Martins, James B.; Wahle, Andreas

    2010-03-01

    Quantitative analysis of the left ventricular shape and motion patterns associated with left ventricular mechanical dyssynchrony (LVMD) is essential for diagnosis and treatment planning in congestive heart failure. Real-time 3D echocardiography (RT3DE) used for LVMD analysis is frequently limited by heavy speckle noise or partially incomplete data, thus a segmentation method utilizing learned global shape knowledge is beneficial. In this study, the endocardial surface of the left ventricle (LV) is segmented using a hybrid approach combining active shape model (ASM) with optimal graph search. The latter is used to achieve landmark refinement in the ASM framework. Optimal graph search translates the 3D segmentation into the detection of a minimum-cost closed set in a graph and can produce a globally optimal result. Various information-gradient, intensity distributions, and regional-property terms-are used to define the costs for the graph search. The developed method was tested on 44 RT3DE datasets acquired from 26 LVMD patients. The segmentation accuracy was assessed by surface positioning error and volume overlap measured for the whole LV as well as 16 standard LV regions. The segmentation produced very good results that were not achievable using ASM or graph search alone.

  3. 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.

  4. Using Paleoseismic Trenching and LiDAR Analysis to Evaluate Rupture Propagation Through Segment Boundaries of the Central Wasatch Fault Zone, Utah

    NASA Astrophysics Data System (ADS)

    Bennett, S. E. K.; DuRoss, C. B.; Reitman, N. G.; Devore, J. R.; Hiscock, A.; Gold, R. D.; Briggs, R. W.; Personius, S. F.

    2014-12-01

    Paleoseismic data near fault segment boundaries constrain the extent of past surface ruptures and the persistence of rupture termination at segment boundaries. Paleoseismic evidence for large (M≥7.0) earthquakes on the central Holocene-active fault segments of the 350-km-long Wasatch fault zone (WFZ) generally supports single-segment ruptures but also permits multi-segment rupture scenarios. The extent and frequency of ruptures that span segment boundaries remains poorly known, adding uncertainty to seismic hazard models for this populated region of Utah. To address these uncertainties we conducted four paleoseismic investigations near the Salt Lake City-Provo and Provo-Nephi segment boundaries of the WFZ. We examined an exposure of the WFZ at Maple Canyon (Woodland Hills, UT) and excavated the Flat Canyon trench (Salem, UT), 7 and 11 km, respectively, from the southern tip of the Provo segment. We document evidence for at least five earthquakes at Maple Canyon and four to seven earthquakes that post-date mid-Holocene fan deposits at Flat Canyon. These earthquake chronologies will be compared to seven earthquakes observed in previous trenches on the northern Nephi segment to assess rupture correlation across the Provo-Nephi segment boundary. To assess rupture correlation across the Salt Lake City-Provo segment boundary we excavated the Alpine trench (Alpine, UT), 1 km from the northern tip of the Provo segment, and the Corner Canyon trench (Draper, UT) 1 km from the southern tip of the Salt Lake City segment. We document evidence for six earthquakes at both sites. Ongoing geochronologic analysis (14C, optically stimulated luminescence) will constrain earthquake chronologies and help identify through-going ruptures across these segment boundaries. Analysis of new high-resolution (0.5m) airborne LiDAR along the entire WFZ will quantify latest Quaternary displacements and slip rates and document spatial and temporal slip patterns near fault segment boundaries.

  5. Comparison of volume estimation methods for pancreatic islet cells

    NASA Astrophysics Data System (ADS)

    Dvořák, JiřÃ.­; Å vihlík, Jan; Habart, David; Kybic, Jan

    2016-03-01

    In this contribution we study different methods of automatic volume estimation for pancreatic islets which can be used in the quality control step prior to the islet transplantation. The total islet volume is an important criterion in the quality control. Also, the individual islet volume distribution is interesting -- it has been indicated that smaller islets can be more effective. A 2D image of a microscopy slice containing the islets is acquired. The input of the volume estimation methods are segmented images of individual islets. The segmentation step is not discussed here. We consider simple methods of volume estimation assuming that the islets have spherical or ellipsoidal shape. We also consider a local stereological method, namely the nucleator. The nucleator does not rely on any shape assumptions and provides unbiased estimates if isotropic sections through the islets are observed. We present a simulation study comparing the performance of the volume estimation methods in different scenarios and an experimental study comparing the methods on a real dataset.

  6. Markov models of genome segmentation

    NASA Astrophysics Data System (ADS)

    Thakur, Vivek; Azad, Rajeev K.; Ramaswamy, Ram

    2007-01-01

    We introduce Markov models for segmentation of symbolic sequences, extending a segmentation procedure based on the Jensen-Shannon divergence that has been introduced earlier. Higher-order Markov models are more sensitive to the details of local patterns and in application to genome analysis, this makes it possible to segment a sequence at positions that are biologically meaningful. We show the advantage of higher-order Markov-model-based segmentation procedures in detecting compositional inhomogeneity in chimeric DNA sequences constructed from genomes of diverse species, and in application to the E. coli K12 genome, boundaries of genomic islands, cryptic prophages, and horizontally acquired regions are accurately identified.

  7. Market segmentation using perceived constraints

    Treesearch

    Jinhee Jun; Gerard Kyle; Andrew Mowen

    2008-01-01

    We examined the practical utility of segmenting potential visitors to Cleveland Metroparks using their constraint profiles. Our analysis identified three segments based on their scores on the dimensions of constraints: Other priorities--visitors who scored the highest on 'other priorities' dimension; Highly Constrained--visitors who scored relatively high on...

  8. Medical Image Segmentation by Combining Graph Cut and Oriented Active Appearance Models

    PubMed Central

    Chen, Xinjian; Udupa, Jayaram K.; Bağcı, Ulaş; Zhuge, Ying; Yao, Jianhua

    2017-01-01

    In this paper, we propose a novel 3D segmentation method based on the effective combination of the active appearance model (AAM), live wire (LW), and graph cut (GC). The proposed method consists of three main parts: model building, initialization, and segmentation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the initialization part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW method, resulting in Oriented AAM (OAAM). A multi-object strategy is utilized to help in object initialization. We employ a pseudo-3D initialization strategy, and segment the organs slice by slice via multi-object OAAM method. For the segmentation part, a 3D shape constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT dataset and also tested on the MICCAI 2007 grand challenge for liver segmentation training dataset. The results show the following: (a) An overall segmentation accuracy of true positive volume fraction (TPVF) > 94.3%, false positive volume fraction (FPVF) < 0.2% can be achieved. (b) The initialization performance can be improved by combining AAM and LW. (c) The multi-object strategy greatly facilitates the initialization. (d) Compared to the traditional 3D AAM method, the pseudo 3D OAAM method achieves comparable performance while running 12 times faster. (e) The performance of proposed method is comparable to the state of the art liver segmentation algorithm. The executable version of 3D shape constrained GC with user interface can be downloaded from website http://xinjianchen.wordpress.com/research/. PMID:22311862

  9. Simplex volume analysis for finding endmembers in hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Li, Hsiao-Chi; Song, Meiping; Chang, Chein-I.

    2015-05-01

    Using maximal simplex volume as an optimal criterion for finding endmembers is a common approach and has been widely studied in the literature. Interestingly, very little work has been reported on how simplex volume is calculated. It turns out that the issue of calculating simplex volume is much more complicated and involved than what we may think. This paper investigates this issue from two different aspects, geometric structure and eigen-analysis. The geometric structure is derived from its simplex structure whose volume can be calculated by multiplying its base with its height. On the other hand, eigen-analysis takes advantage of the Cayley-Menger determinant to calculate the simplex volume. The major issue of this approach is that when the matrix is ill-rank where determinant is desired. To deal with this problem two methods are generally considered. One is to perform data dimensionality reduction to make the matrix to be of full rank. The drawback of this method is that the original volume has been shrunk and the found volume of a dimensionality-reduced simplex is not the real original simplex volume. Another is to use singular value decomposition (SVD) to find singular values for calculating simplex volume. The dilemma of this method is its instability in numerical calculations. This paper explores all of these three methods in simplex volume calculation. Experimental results show that geometric structure-based method yields the most reliable simplex volume.

  10. Rapid Contour-based Segmentation for 18F-FDG PET Imaging of Lung Tumors by Using ITK-SNAP: Comparison to Expert-based Segmentation.

    PubMed

    Besson, Florent L; Henry, Théophraste; Meyer, Céline; Chevance, Virgile; Roblot, Victoire; Blanchet, Elise; Arnould, Victor; Grimon, Gilles; Chekroun, Malika; Mabille, Laurence; Parent, Florence; Seferian, Andrei; Bulifon, Sophie; Montani, David; Humbert, Marc; Chaumet-Riffaud, Philippe; Lebon, Vincent; Durand, Emmanuel

    2018-04-03

    Purpose To assess the performance of the ITK-SNAP software for fluorodeoxyglucose (FDG) positron emission tomography (PET) segmentation of complex-shaped lung tumors compared with an optimized, expert-based manual reference standard. Materials and Methods Seventy-six FDG PET images of thoracic lesions were retrospectively segmented by using ITK-SNAP software. Each tumor was manually segmented by six raters to generate an optimized reference standard by using the simultaneous truth and performance level estimate algorithm. Four raters segmented 76 FDG PET images of lung tumors twice by using ITK-SNAP active contour algorithm. Accuracy of ITK-SNAP procedure was assessed by using Dice coefficient and Hausdorff metric. Interrater and intrarater reliability were estimated by using intraclass correlation coefficients of output volumes. Finally, the ITK-SNAP procedure was compared with currently recommended PET tumor delineation methods on the basis of thresholding at 41% volume of interest (VOI; VOI 41 ) and 50% VOI (VOI 50 ) of the tumor's maximal metabolism intensity. Results Accuracy estimates for the ITK-SNAP procedure indicated a Dice coefficient of 0.83 (95% confidence interval: 0.77, 0.89) and a Hausdorff distance of 12.6 mm (95% confidence interval: 9.82, 15.32). Interrater reliability was an intraclass correlation coefficient of 0.94 (95% confidence interval: 0.91, 0.96). The intrarater reliabilities were intraclass correlation coefficients above 0.97. Finally, VOI 41 and VOI 50 accuracy metrics were as follows: Dice coefficient, 0.48 (95% confidence interval: 0.44, 0.51) and 0.34 (95% confidence interval: 0.30, 0.38), respectively, and Hausdorff distance, 25.6 mm (95% confidence interval: 21.7, 31.4) and 31.3 mm (95% confidence interval: 26.8, 38.4), respectively. Conclusion ITK-SNAP is accurate and reliable for active-contour-based segmentation of heterogeneous thoracic PET tumors. ITK-SNAP surpassed the recommended PET methods compared with ground truth

  11. A novel cardiac MR chamber volume model for mechanical dyssynchrony assessment

    NASA Astrophysics Data System (ADS)

    Song, Ting; Fung, Maggie; Stainsby, Jeffrey A.; Hood, Maureen N.; Ho, Vincent B.

    2009-02-01

    A novel cardiac chamber volume model is proposed for the assessment of left ventricular mechanical dyssynchrony. The tool is potentially useful for assessment of regional cardiac function and identification of mechanical dyssynchrony on MRI. Dyssynchrony results typically from a contraction delay between one or more individual left ventricular segments, which in turn leads to inefficient ventricular function and ultimately heart failure. Cardiac resynchronization therapy has emerged as an electrical treatment of choice for heart failure patients with dyssynchrony. Prior MRI techniques have relied on assessments of actual cardiac wall changes either using standard cine MR images or specialized pulse sequences. In this abstract, we detail a semi-automated method that evaluates dyssynchrony based on segmental volumetric analysis of the left ventricular (LV) chamber as illustrated on standard cine MR images. Twelve sectors each were chosen for the basal and mid-ventricular slices and 8 sectors were chosen for apical slices for a total of 32 sectors. For each slice (i.e. basal, mid and apical), a systolic dyssynchrony index (SDI) was measured. SDI, a parameter used for 3D echocardiographic analysis of dyssynchrony, was defined as the corrected standard deviation of the time at which minimal volume is reached in each sector. The SDI measurement of a healthy volunteer was 3.54%. In a patient with acute myocardial infarction, the SDI measurements 10.98%, 16.57% and 1.41% for basal, mid-ventricular and apical LV slices, respectively. Based on published 3D echocardiogram reference threshold values, the patient's SDI corresponds to moderate basal dysfunction, severe mid-ventricular dysfunction, and normal apical LV function, which were confirmed on echocardiography. The LV chamber segmental volume analysis model and SDI is feasible using standard cine MR data and may provide more reliable assessment of patients with dyssynchrony especially if the LV myocardium is thin or if

  12. Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

    PubMed Central

    Mansoor, Awais; Foster, Brent; Xu, Ziyue; Papadakis, Georgios Z.; Folio, Les R.; Udupa, Jayaram K.; Mollura, Daniel J.

    2015-01-01

    The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy–guided, and (e) machine learning–based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. ©RSNA, 2015 PMID:26172351

  13. Semiautomated Middle Ear Volume Measurement as a Predictor of Postsurgical Outcomes for Congenital Aural Atresia.

    PubMed

    Kabadi, S J; Ruhl, D S; Mukherjee, S; Kesser, B W

    2018-02-01

    Middle ear space is one of the most important components of the Jahrsdoerfer grading system (J-score), which is used to determine surgical candidacy for congenital aural atresia. The purpose of this study was to introduce a semiautomated method for measuring middle ear volume and determine whether middle ear volume, either alone or in combination with the J-score, can be used to predict early postoperative audiometric outcomes. A retrospective analysis was conducted of 18 patients who underwent an operation for unilateral congenital aural atresia at our institution. Using the Livewire Segmentation tool in the Carestream Vue PACS, we segmented middle ear volumes using a semiautomated method for all atretic and contralateral normal ears on preoperative high-resolution CT imaging. Postsurgical audiometric outcome data were then analyzed in the context of these middle ear volumes. Atretic middle ear volumes were significantly smaller than those in contralateral normal ears ( P < .001). Patients with atretic middle ear volumes of >305 mm 3 had significantly better postoperative pure tone average and speech reception thresholds than those with atretic ears below this threshold volume ( P = .01 and P = .006, respectively). Atretic middle ear volume incorporated into the J-score offered the best association with normal postoperative hearing (speech reception threshold ≤ 30 dB; OR = 37.8, P = .01). Middle ear volume, calculated in a semiautomated fashion, is predictive of postsurgical audiometric outcomes, both independently and in combination with the conventional J-score. © 2018 by American Journal of Neuroradiology.

  14. Computer-aided liver volumetry: performance of a fully-automated, prototype post-processing solution for whole-organ and lobar segmentation based on MDCT imaging.

    PubMed

    Fananapazir, Ghaneh; Bashir, Mustafa R; Marin, Daniele; Boll, Daniel T

    2015-06-01

    To evaluate the performance of a prototype, fully-automated post-processing solution for whole-liver and lobar segmentation based on MDCT datasets. A polymer liver phantom was used to assess accuracy of post-processing applications comparing phantom volumes determined via Archimedes' principle with MDCT segmented datasets. For the IRB-approved, HIPAA-compliant study, 25 patients were enrolled. Volumetry performance compared the manual approach with the automated prototype, assessing intraobserver variability, and interclass correlation for whole-organ and lobar segmentation using ANOVA comparison. Fidelity of segmentation was evaluated qualitatively. Phantom volume was 1581.0 ± 44.7 mL, manually segmented datasets estimated 1628.0 ± 47.8 mL, representing a mean overestimation of 3.0%, automatically segmented datasets estimated 1601.9 ± 0 mL, representing a mean overestimation of 1.3%. Whole-liver and segmental volumetry demonstrated no significant intraobserver variability for neither manual nor automated measurements. For whole-liver volumetry, automated measurement repetitions resulted in identical values; reproducible whole-organ volumetry was also achieved with manual segmentation, p(ANOVA) 0.98. For lobar volumetry, automated segmentation improved reproducibility over manual approach, without significant measurement differences for either methodology, p(ANOVA) 0.95-0.99. Whole-organ and lobar segmentation results from manual and automated segmentation showed no significant differences, p(ANOVA) 0.96-1.00. Assessment of segmentation fidelity found that segments I-IV/VI showed greater segmentation inaccuracies compared to the remaining right hepatic lobe segments. Automated whole-liver segmentation showed non-inferiority of fully-automated whole-liver segmentation compared to manual approaches with improved reproducibility and post-processing duration; automated dual-seed lobar segmentation showed slight tendencies for underestimating the right hepatic lobe

  15. 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

  16. An interactive toolbox for atlas-based segmentation and coding of volumetric images

    NASA Astrophysics Data System (ADS)

    Menegaz, G.; Luti, S.; Duay, V.; Thiran, J.-Ph.

    2007-03-01

    Medical imaging poses the great challenge of having compression algorithms that are lossless for diagnostic and legal reasons and yet provide high compression rates for reduced storage and transmission time. The images usually consist of a region of interest representing the part of the body under investigation surrounded by a "background", which is often noisy and not of diagnostic interest. In this paper, we propose a ROI-based 3D coding system integrating both the segmentation and the compression tools. The ROI is extracted by an atlas based 3D segmentation method combining active contours with information theoretic principles, and the resulting segmentation map is exploited for ROI based coding. The system is equipped with a GUI allowing the medical doctors to supervise the segmentation process and eventually reshape the detected contours at any point. The process is initiated by the user through the selection of either one pre-de.ned reference image or one image of the volume to be used as the 2D "atlas". The object contour is successively propagated from one frame to the next where it is used as the initial border estimation. In this way, the entire volume is segmented based on a unique 2D atlas. The resulting 3D segmentation map is exploited for adaptive coding of the different image regions. Two coding systems were considered: the JPEG3D standard and the 3D-SPITH. The evaluation of the performance with respect to both segmentation and coding proved the high potential of the proposed system in providing an integrated, low-cost and computationally effective solution for CAD and PAC systems.

  17. A novel content-based active contour model for brain tumor segmentation.

    PubMed

    Sachdeva, Jainy; Kumar, Vinod; Gupta, Indra; Khandelwal, Niranjan; Ahuja, Chirag Kamal

    2012-06-01

    Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. Comparative analysis of operational forecasts versus actual weather conditions in airline flight planning, volume 4

    NASA Technical Reports Server (NTRS)

    Keitz, J. F.

    1982-01-01

    The impact of more timely and accurate weather data on airline flight planning with the emphasis on fuel savings is studied. This volume of the report discusses the results of Task 4 of the four major tasks included in the study. Task 4 uses flight plan segment wind and temperature differences as indicators of dates and geographic areas for which significant forecast errors may have occurred. An in-depth analysis is then conducted for the days identified. The analysis show that significant errors occur in the operational forecast on 15 of the 33 arbitrarily selected days included in the study. Wind speeds in an area of maximum winds are underestimated by at least 20 to 25 kts. on 14 of these days. The analysis also show that there is a tendency to repeat the same forecast errors from prog to prog. Also, some perceived forecast errors from the flight plan comparisons could not be verified by visual inspection of the corresponding National Meteorological Center forecast and analyses charts, and it is likely that they are the result of weather data interpolation techniques or some other data processing procedure in the airlines' flight planning systems.

  19. A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina

    PubMed Central

    Kafieh, Raheleh; Rabbani, Hossein; Kermani, Saeed

    2013-01-01

    Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging. PMID:24083137

  20. Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

    NASA Astrophysics Data System (ADS)

    Wang, Fang; Li, Zong-shou; Li, Jin-wei

    2014-12-01

    Feature extraction plays a great important role in image processing and pattern recognition. As a power tool, multifractal theory is recently employed for this job. However, traditional multifractal methods are proposed to analyze the objects with stationary measure and cannot for non-stationary measure. The works of this paper is twofold. First, the definition of stationary image and 2D image feature detection methods are proposed. Second, a novel feature extraction scheme for non-stationary image is proposed by local multifractal detrended fluctuation analysis (Local MF-DFA), which is based on 2D MF-DFA. A set of new multifractal descriptors, called local generalized Hurst exponent (Lhq) is defined to characterize the local scaling properties of textures. To test the proposed method, both the novel texture descriptor and other two multifractal indicators, namely, local Hölder coefficients based on capacity measure and multifractal dimension Dq based on multifractal differential box-counting (MDBC) method, are compared in segmentation experiments. The first experiment indicates that the segmentation results obtained by the proposed Lhq are better than the MDBC-based Dq slightly and superior to the local Hölder coefficients significantly. The results in the second experiment demonstrate that the Lhq can distinguish the texture images more effectively and provide more robust segmentations than the MDBC-based Dq significantly.