Semiautomatic Segmentation of Glioma on Mobile Devices.

PubMed

Wu, Ya-Ping; Lin, Yu-Song; Wu, Wei-Guo; Yang, Cong; Gu, Jian-Qin; Bai, Yan; Wang, Mei-Yun

2017-01-01

Brain tumor segmentation is the first and the most critical step in clinical applications of radiomics. However, segmenting brain images by radiologists is labor intense and prone to inter- and intraobserver variability. Stable and reproducible brain image segmentation algorithms are thus important for successful tumor detection in radiomics. In this paper, we propose a supervised brain image segmentation method, especially for magnetic resonance (MR) brain images with glioma. This paper uses hard edge multiplicative intrinsic component optimization to preprocess glioma medical image on the server side, and then, the doctors could supervise the segmentation process on mobile devices in their convenient time. Since the preprocessed images have the same brightness for the same tissue voxels, they have small data size (typically 1/10 of the original image size) and simple structure of 4 types of intensity value. This observation thus allows follow-up steps to be processed on mobile devices with low bandwidth and limited computing performance. Experiments conducted on 1935 brain slices from 129 patients show that more than 30% of the sample can reach 90% similarity; over 60% of the samples can reach 85% similarity, and more than 80% of the sample could reach 75% similarity. The comparisons with other segmentation methods also demonstrate both efficiency and stability of the proposed approach.

State of the art survey on MRI brain tumor segmentation.

PubMed

Gordillo, Nelly; Montseny, Eduard; Sobrevilla, Pilar

2013-10-01

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized. Copyright © 2013 Elsevier Inc. All rights reserved.

Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil

PubMed Central

Shi, Feng; Yap, Pew-Thian; Fan, Yong; Cheng, Jie-Zhi; Wald, Lawrence L.; Gerig, Guido; Lin, Weili; Shen, Dinggang

2010-01-01

The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods. PMID:20862268

Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

NASA Astrophysics Data System (ADS)

Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

2012-01-01

The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

PubMed

Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

2012-01-01

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging.

PubMed

Fu, J C; Chen, C C; Chai, J W; Wong, S T C; Li, I C

2010-06-01

We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation. Copyright 2009 Elsevier Ltd. All rights reserved.

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

NASA Astrophysics Data System (ADS)

Galimzianova, Alfiia; Lesjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2015-03-01

Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.

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.

Identifying Degenerative Brain Disease Using Rough Set Classifier Based on Wavelet Packet Method.

PubMed

Cheng, Ching-Hsue; Liu, Wei-Xiang

2018-05-28

Population aging has become a worldwide phenomenon, which causes many serious problems. The medical issues related to degenerative brain disease have gradually become a concern. Magnetic Resonance Imaging is one of the most advanced methods for medical imaging and is especially suitable for brain scans. From the literature, although the automatic segmentation method is less laborious and time-consuming, it is restricted in several specific types of images. In addition, hybrid techniques segmentation improves the shortcomings of the single segmentation method. Therefore, this study proposed a hybrid segmentation combined with rough set classifier and wavelet packet method to identify degenerative brain disease. The proposed method is a three-stage image process method to enhance accuracy of brain disease classification. In the first stage, this study used the proposed hybrid segmentation algorithms to segment the brain ROI (region of interest). In the second stage, wavelet packet was used to conduct the image decomposition and calculate the feature values. In the final stage, the rough set classifier was utilized to identify the degenerative brain disease. In verification and comparison, two experiments were employed to verify the effectiveness of the proposed method and compare with the TV-seg (total variation segmentation) algorithm, Discrete Cosine Transform, and the listing classifiers. Overall, the results indicated that the proposed method outperforms the listing methods.

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

NASA Astrophysics Data System (ADS)

Jiji, G. Wiselin; Dehmeshki, Jamshid

2014-04-01

Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

Research on segmentation based on multi-atlas in brain MR image

NASA Astrophysics Data System (ADS)

Qian, Yuejing

2018-03-01

Accurate segmentation of specific tissues in brain MR image can be effectively achieved with the multi-atlas-based segmentation method, and the accuracy mainly depends on the image registration accuracy and fusion scheme. This paper proposes an automatic segmentation method based on the multi-atlas for brain MR image. Firstly, to improve the registration accuracy in the area to be segmented, we employ a target-oriented image registration method for the refinement. Then In the label fusion, we proposed a new algorithm to detect the abnormal sparse patch and simultaneously abandon the corresponding abnormal sparse coefficients, this method is made based on the remaining sparse coefficients combined with the multipoint label estimator strategy. The performance of the proposed method was compared with those of the nonlocal patch-based label fusion method (Nonlocal-PBM), the sparse patch-based label fusion method (Sparse-PBM) and majority voting method (MV). Based on our experimental results, the proposed method is efficient in the brain MR images segmentation compared with MV, Nonlocal-PBM, and Sparse-PBM methods.

Integration of Sparse Multi-modality Representation and Anatomical Constraint for Isointense Infant Brain MR Image Segmentation

PubMed Central

Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination process. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6–8 months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6 months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter. PMID:24291615

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches

NASA Astrophysics Data System (ADS)

Lindner, Lydia; Pfarrkirchner, Birgit; Gsaxner, Christina; Schmalstieg, Dieter; Egger, Jan

2018-03-01

Accurate segmentation and measurement of brain tumors plays an important role in clinical practice and research, as it is critical for treatment planning and monitoring of tumor growth. However, brain tumor segmentation is one of the most challenging tasks in medical image analysis. Since manual segmentations are subjective, time consuming and neither accurate nor reliable, there exists a need for objective, robust and fast automated segmentation methods that provide competitive performance. Therefore, deep learning based approaches are gaining interest in the field of medical image segmentation. When the training data set is large enough, deep learning approaches can be extremely effective, but in domains like medicine, only limited data is available in the majority of cases. Due to this reason, we propose a method that allows to create a large dataset of brain MRI (Magnetic Resonance Imaging) images containing synthetic brain tumors - glioblastomas more specifically - and the corresponding ground truth, that can be subsequently used to train deep neural networks.

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.

Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

PubMed

Lian, Yanyun; Song, Zhijian

2014-01-01

Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

Skeleton-based region competition for automated gray matter and white matter segmentation of human brain MR images

NASA Astrophysics Data System (ADS)

Chu, Yong; Chen, Ya-Fang; Su, Min-Ying; Nalcioglu, Orhan

2005-04-01

Image segmentation is an essential process for quantitative analysis. Segmentation of brain tissues in magnetic resonance (MR) images is very important for understanding the structural-functional relationship for various pathological conditions, such as dementia vs. normal brain aging. Different brain regions are responsible for certain functions and may have specific implication for diagnosis. Segmentation may facilitate the analysis of different brain regions to aid in early diagnosis. Region competition has been recently proposed as an effective method for image segmentation by minimizing a generalized Bayes/MDL criterion. However, it is sensitive to initial conditions - the "seeds", therefore an optimal choice of "seeds" is necessary for accurate segmentation. In this paper, we present a new skeleton-based region competition algorithm for automated gray and white matter segmentation. Skeletons can be considered as good "seed regions" since they provide the morphological a priori information, thus guarantee a correct initial condition. Intensity gradient information is also added to the global energy function to achieve a precise boundary localization. This algorithm was applied to perform gray and white matter segmentation using simulated MRI images from a realistic digital brain phantom. Nine different brain regions were manually outlined for evaluation of the performance in these separate regions. The results were compared to the gold-standard measure to calculate the true positive and true negative percentages. In general, this method worked well with a 96% accuracy, although the performance varied in different regions. We conclude that the skeleton-based region competition is an effective method for gray and white matter segmentation.

Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

PubMed

Lu, Yisu; Jiang, Jun; Yang, Wei; Feng, Qianjin; Chen, Wufan

2014-01-01

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior

PubMed Central

Lu, Yisu; Jiang, Jun; Chen, Wufan

2014-01-01

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. PMID:25254064

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.

Brain tumor classification and segmentation using sparse coding and dictionary learning.

PubMed

Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

2016-08-01

This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

A region-based segmentation of tumour from brain CT images using nonlinear support vector machine classifier.

PubMed

Nanthagopal, A Padma; Rajamony, R Sukanesh

2012-07-01

The proposed system provides new textural information for segmenting tumours, efficiently and accurately and with less computational time, from benign and malignant tumour images, especially in smaller dimensions of tumour regions of computed tomography (CT) images. Region-based segmentation of tumour from brain CT image data is an important but time-consuming task performed manually by medical experts. The objective of this work is to segment brain tumour from CT images using combined grey and texture features with new edge features and nonlinear support vector machine (SVM) classifier. The selected optimal features are used to model and train the nonlinear SVM classifier to segment the tumour from computed tomography images and the segmentation accuracies are evaluated for each slice of the tumour image. The method is applied on real data of 80 benign, malignant tumour images. The results are compared with the radiologist labelled ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and the overlap similarity measure dice metric. From the analysis and performance measures such as segmentation accuracy and dice metric, it is inferred that better segmentation accuracy and higher dice metric are achieved with the normalized cut segmentation method than with the fuzzy c-means clustering method.

A Learning-Based Wrapper Method to Correct Systematic Errors in Automatic Image Segmentation: Consistently Improved Performance in Hippocampus, Cortex and Brain Segmentation

PubMed Central

Wang, Hongzhi; Das, Sandhitsu R.; Suh, Jung Wook; Altinay, Murat; Pluta, John; Craige, Caryne; Avants, Brian; Yushkevich, Paul A.

2011-01-01

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively. PMID:21237273

Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

2017-03-01

Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

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.

Semi-automatic segmentation of brain tumors using population and individual information.

PubMed

Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

2013-08-01

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

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.

Semiautomated volumetry of the cerebrum, cerebellum-brain stem, and temporal lobe on brain magnetic resonance images.

PubMed

Hayashi, Norio; Sanada, Shigeru; Suzuki, Masayuki; Matsuura, Yukihiro; Kawahara, Kazuhiro; Tsujii, Hideo; Yamamoto, Tomoyuki; Matsui, Osamu

2008-02-01

The aim of this study was to develop an automated method of segmenting the cerebrum, cerebellum-brain stem, and temporal lobe simultaneously on magnetic resonance (MR) images. We obtained T1-weighted MR images from 10 normal subjects and 19 patients with brain atrophy. To perform automated volumetry from MR images, we performed the following three steps: (1) segmentation of the brain region; (2) separation between the cerebrum and the cerebellum-brain stem; and (3) segmentation of the temporal lobe. Evaluation was based on the correctly recognized region (CRR) (i.e., the region recognized by both the automated and manual methods). The mean CRRs of the normal and atrophic brains were 98.2% and 97.9% for the cerebrum, 87.9% and 88.5% for the cerebellum-brain stem, and 76.9% and 85.8% for the temporal lobe, respectively. We introduce an automated volumetric method for the cerebrum, cerebellum-brain stem, and temporal lobe on brain MR images. Our method can be applied to not only the normal brain but also the atrophic brain.

Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

PubMed

Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

2013-10-01

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

Rough Sets and Stomped Normal Distribution for Simultaneous Segmentation and Bias Field Correction in Brain MR Images.

PubMed

Banerjee, Abhirup; Maji, Pradipta

2015-12-01

The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels.

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

PubMed

Chen, Hao; Dou, Qi; Yu, Lequan; Qin, Jing; Heng, Pheng-Ann

2018-04-15

Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain structures is critical. Copyright © 2017 Elsevier Inc. All rights reserved.

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

NASA Astrophysics Data System (ADS)

Alizadeh Savareh, Behrouz; Emami, Hassan; Hajiabadi, Mohamadreza; Ghafoori, Mahyar; Majid Azimi, Seyed

2018-03-01

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests

PubMed Central

Serag, Ahmed; Wilkinson, Alastair G.; Telford, Emma J.; Pataky, Rozalia; Sparrow, Sarah A.; Anblagan, Devasuda; Macnaught, Gillian; Semple, Scott I.; Boardman, James P.

2017-01-01

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course. PMID:28163680

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI.

PubMed

Moeskops, Pim; de Bresser, Jeroen; Kuijf, Hugo J; Mendrik, Adriënne M; Biessels, Geert Jan; Pluim, Josien P W; Išgum, Ivana

2018-01-01

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T 1 -weighted image, a T 2 -weighted fluid attenuated inversion recovery (FLAIR) image and a T 1 -weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge ( n  = 20), quantitatively and qualitatively in relatively healthy older subjects ( n  = 96), and qualitatively in patients from a memory clinic ( n  = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ  = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts.

Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

PubMed

Liyanage, Kishan Andre; Steward, Christopher; Moffat, Bradford Armstrong; Opie, Nicholas Lachlan; Rind, Gil Simon; John, Sam Emmanuel; Ronayne, Stephen; May, Clive Newton; O'Brien, Terence John; Milne, Marjorie Eileen; Oxley, Thomas James

2016-01-01

Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.

Level set method with automatic selective local statistics for brain tumor segmentation in MR images.

PubMed

Thapaliya, Kiran; Pyun, Jae-Young; Park, Chun-Su; Kwon, Goo-Rak

2013-01-01

The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

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

Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

PubMed

Zhao, Liya; Jia, Kebin

2016-01-01

Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

Computer aided detection of tumor and edema in brain FLAIR magnetic resonance image using ANN

NASA Astrophysics Data System (ADS)

Pradhan, Nandita; Sinha, A. K.

2008-03-01

This paper presents an efficient region based segmentation technique for detecting pathological tissues (Tumor & Edema) of brain using fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. This work segments FLAIR brain images for normal and pathological tissues based on statistical features and wavelet transform coefficients using k-means algorithm. The image is divided into small blocks of 4×4 pixels. The k-means algorithm is used to cluster the image based on the feature vectors of blocks forming different classes representing different regions in the whole image. With the knowledge of the feature vectors of different segmented regions, supervised technique is used to train Artificial Neural Network using fuzzy back propagation algorithm (FBPA). Segmentation for detecting healthy tissues and tumors has been reported by several researchers by using conventional MRI sequences like T1, T2 and PD weighted sequences. This work successfully presents segmentation of healthy and pathological tissues (both Tumors and Edema) using FLAIR images. At the end pseudo coloring of segmented and classified regions are done for better human visualization.

Brain tumor segmentation using holistically nested neural networks in MRI images.

PubMed

Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

2017-10-01

Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 h for training with 50,000 iterations, and approximately 30 s for testing of a typical MRI image in the BRATS 2013 dataset with a size of 160 × 216 × 176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

PubMed Central

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

PubMed

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

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.

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 evaluating tumor response during treatment, this method can be used as a clinical image analysis tool for doctors or radiologists.

Delivery of Nano-Tethered Therapies to Brain Metastases of Primary Breast Cancer Using a Cellular Trojan Horse

DTIC Science & Technology

2015-10-01

tomography images. The CT image densities in Hounsfield units (HU) of the brain were translated into corresponding optical properties (absorption...derived the Hounsfield units and optical properties of brain tissues such as white/gray matter. 13-15 The segmentation software generated an optical map...treatment protocol. Head CT image densities (in Hounsfield Units /HU) are segmented and translated into optical properties of the brain tissue

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Research of the multimodal brain-tumor segmentation algorithm

NASA Astrophysics Data System (ADS)

Lu, Yisu; Chen, Wufan

2015-12-01

It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance.

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.

PubMed

Bricq, S; Collet, Ch; Armspach, J P

2008-12-01

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.

Quantitative mouse brain phenotyping based on single and multispectral MR protocols

PubMed Central

Badea, Alexandra; Gewalt, Sally; Avants, Brian B.; Cook, James J.; Johnson, G. Allan

2013-01-01

Sophisticated image analysis methods have been developed for the human brain, but such tools still need to be adapted and optimized for quantitative small animal imaging. We propose a framework for quantitative anatomical phenotyping in mouse models of neurological and psychiatric conditions. The framework encompasses an atlas space, image acquisition protocols, and software tools to register images into this space. We show that a suite of segmentation tools (Avants, Epstein et al., 2008) designed for human neuroimaging can be incorporated into a pipeline for segmenting mouse brain images acquired with multispectral magnetic resonance imaging (MR) protocols. We present a flexible approach for segmenting such hyperimages, optimizing registration, and identifying optimal combinations of image channels for particular structures. Brain imaging with T1, T2* and T2 contrasts yielded accuracy in the range of 83% for hippocampus and caudate putamen (Hc and CPu), but only 54% in white matter tracts, and 44% for the ventricles. The addition of diffusion tensor parameter images improved accuracy for large gray matter structures (by >5%), white matter (10%), and ventricles (15%). The use of Markov random field segmentation further improved overall accuracy in the C57BL/6 strain by 6%; so Dice coefficients for Hc and CPu reached 93%, for white matter 79%, for ventricles 68%, and for substantia nigra 80%. We demonstrate the segmentation pipeline for the widely used C57BL/6 strain, and two test strains (BXD29, APP/TTA). This approach appears promising for characterizing temporal changes in mouse models of human neurological and psychiatric conditions, and may provide anatomical constraints for other preclinical imaging, e.g. fMRI and molecular imaging. This is the first demonstration that multiple MR imaging modalities combined with multivariate segmentation methods lead to significant improvements in anatomical segmentation in the mouse brain. PMID:22836174

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).

Hidden Markov random field model and Broyden-Fletcher-Goldfarb-Shanno algorithm for brain image segmentation

NASA Astrophysics Data System (ADS)

Guerrout, EL-Hachemi; Ait-Aoudia, Samy; Michelucci, Dominique; Mahiou, Ramdane

2018-05-01

Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (BrainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.

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

PubMed

Prastawa, Marcel; Bullitt, Elizabeth; Gerig, Guido

2009-04-01

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

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

NASA Astrophysics Data System (ADS)

Lee, Joohwi; Kim, Sun Hyung; Styner, Martin

2016-03-01

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool.

[Tumor segmentation of brain MRI with adaptive bandwidth mean shift].

PubMed

Hou, Xiaowen; Liu, Qi

2014-10-01

In order to get the adaptive bandwidth of mean shift to make the tumor segmentation of brain magnetic resonance imaging (MRI) to be more accurate, we in this paper present an advanced mean shift method. Firstly, we made use of the space characteristics of brain image to eliminate the impact on segmentation of skull; and then, based on the characteristics of spatial agglomeration of different tissues of brain (includes tumor), we applied edge points to get the optimal initial mean value and the respectively adaptive bandwidth, in order to improve the accuracy of tumor segmentation. The results of experiment showed that, contrast to the fixed bandwidth mean shift method, the method in this paper could segment the tumor more accurately.

LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images.

PubMed

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Gilmore, John H; Lin, Weili; Shen, Dinggang

2015-03-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. Copyright © 2014 Elsevier Inc. All rights reserved.

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

PubMed Central

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. PMID:25541188

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

NASA Technical Reports Server (NTRS)

Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

Statistical model of laminar structure for atlas-based segmentation of the fetal brain from in utero MR images

NASA Astrophysics Data System (ADS)

Habas, Piotr A.; Kim, Kio; Chandramohan, Dharshan; Rousseau, Francois; Glenn, Orit A.; Studholme, Colin

2009-02-01

Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain segmentation are limited in their ability to accurately delineate complex structures of developing tissues from fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient (DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to 0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.

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.

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

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.

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

Integration of Sparse Multi-modality Representation and Geometrical Constraint for Isointense Infant Brain Segmentation

PubMed Central

Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H.; Shen, Dinggang

2014-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6–8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods. PMID:24505729

Integration of sparse multi-modality representation and geometrical constraint for isointense infant brain segmentation.

PubMed

Wang, Li; Shi, Feng; Li, Gang; Lin, Weili; Gilmore, John H; Shen, Dinggang

2013-01-01

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination process. During the first year of life, the signal contrast between white matter (WM) and gray matter (GM) in MR images undergoes inverse changes. In particular, the inversion of WM/GM signal contrast appears around 6-8 months of age, where brain tissues appear isointense and hence exhibit extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a novel segmentation method to address the above-mentioned challenge based on the sparse representation of the complementary tissue distribution information from T1, T2 and diffusion-weighted images. Specifically, we first derive an initial segmentation from a library of aligned multi-modality images with ground-truth segmentations by using sparse representation in a patch-based fashion. The segmentation is further refined by the integration of the geometrical constraint information. The proposed method was evaluated on 22 6-month-old training subjects using leave-one-out cross-validation, as well as 10 additional infant testing subjects, showing superior results in comparison to other state-of-the-art methods.

Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.

PubMed

Deng, Minghui; Yu, Renping; Wang, Li; Shi, Feng; Yap, Pew-Thian; Shen, Dinggang

2016-12-01

Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio. In this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation. The proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52% ± 0.9%, 89.49% ± 1.83%, and 79.97% ± 4.32% for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values < 0.021). For the ADNI dataset, the group difference comparisons indicate that the proposed algorithm outperforms state-of-the-art segmentation methods. The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation. © 2016 American Association of Physicists in Medicine.

Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.

PubMed

Deng, Minghui; Yu, Renping; Wang, Li; Shi, Feng; Yap, Pew-Thian; Shen, Dinggang

2016-12-01

Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio. In this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation. The proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52% ± 0.9%, 89.49% ± 1.83%, and 79.97% ± 4.32% for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values < 0.021). For the ADNI dataset, the group difference comparisons indicate that the proposed algorithm outperforms state-of-the-art segmentation methods. The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.

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).

Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.

PubMed

Zhao, Liang; Wu, Wei; Corso, Jason J

2013-01-01

Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories-the medical image analog to optical flow and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation.

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.

Brain tumor segmentation in MR slices using improved GrowCut algorithm

NASA Astrophysics Data System (ADS)

Ji, Chunhong; Yu, Jinhua; Wang, Yuanyuan; Chen, Liang; Shi, Zhifeng; Mao, Ying

2015-12-01

The detection of brain tumor from MR images is very significant for medical diagnosis and treatment. However, the existing methods are mostly based on manual or semiautomatic segmentation which are awkward when dealing with a large amount of MR slices. In this paper, a new fully automatic method for the segmentation of brain tumors in MR slices is presented. Based on the hypothesis of the symmetric brain structure, the method improves the interactive GrowCut algorithm by further using the bounding box algorithm in the pre-processing step. More importantly, local reflectional symmetry is used to make up the deficiency of the bounding box method. After segmentation, 3D tumor image is reconstructed. We evaluate the accuracy of the proposed method on MR slices with synthetic tumors and actual clinical MR images. Result of the proposed method is compared with the actual position of simulated 3D tumor qualitatively and quantitatively. In addition, our automatic method produces equivalent performance as manual segmentation and the interactive GrowCut with manual interference while providing fully automatic segmentation.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

PubMed

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien; Gu, Xuejun

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain

NASA Astrophysics Data System (ADS)

Phellan, Renzo; Lindner, Thomas; Falcão, Alexandre X.; Forkert, Nils D.

2017-03-01

4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

NASA Astrophysics Data System (ADS)

Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

2015-03-01

Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

Fast and robust brain tumor segmentation using level set method with multiple image information.

PubMed

Lok, Ka Hei; Shi, Lin; Zhu, Xianlun; Wang, Defeng

2017-01-01

Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation. To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images. In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index. Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor. The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.

Discriminative confidence estimation for probabilistic multi-atlas label fusion.

PubMed

Benkarim, Oualid M; Piella, Gemma; González Ballester, Miguel Angel; Sanroma, Gerard

2017-12-01

Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image. Accurately estimating the contribution of each atlas labelmap to the final segmentation is a critical step for the success of multi-atlas segmentation. Common approaches to label fusion either rely on local patch similarity, probabilistic statistical frameworks or a combination of both. In this work, we propose a probabilistic label fusion framework based on atlas label confidences computed at each voxel of the structure of interest. Maximum likelihood atlas confidences are estimated using a supervised approach, explicitly modeling the relationship between local image appearances and segmentation errors produced by each of the atlases. We evaluate different spatial pooling strategies for modeling local segmentation errors. We also present a novel type of label-dependent appearance features based on atlas labelmaps that are used during confidence estimation to increase the accuracy of our label fusion. Our approach is evaluated on the segmentation of seven subcortical brain structures from the MICCAI 2013 SATA Challenge dataset and the hippocampi from the ADNI dataset. Overall, our results indicate that the proposed label fusion framework achieves superior performance to state-of-the-art approaches in the majority of the evaluated brain structures and shows more robustness to registration errors. Copyright © 2017 Elsevier B.V. All rights reserved.

Corpus callosum segmentation using deep neural networks with prior information from multi-atlas images

NASA Astrophysics Data System (ADS)

Park, Gilsoon; Hong, Jinwoo; Lee, Jong-Min

2018-03-01

In human brain, Corpus Callosum (CC) is the largest white matter structure, connecting between right and left hemispheres. Structural features such as shape and size of CC in midsagittal plane are of great significance for analyzing various neurological diseases, for example Alzheimer's disease, autism and epilepsy. For quantitative and qualitative studies of CC in brain MR images, robust segmentation of CC is important. In this paper, we present a novel method for CC segmentation. Our approach is based on deep neural networks and the prior information generated from multi-atlas images. Deep neural networks have recently shown good performance in various image processing field. Convolutional neural networks (CNN) have shown outstanding performance for classification and segmentation in medical image fields. We used convolutional neural networks for CC segmentation. Multi-atlas based segmentation model have been widely used in medical image segmentation because atlas has powerful information about the target structure we want to segment, consisting of MR images and corresponding manual segmentation of the target structure. We combined the prior information, such as location and intensity distribution of target structure (i.e. CC), made from multi-atlas images in CNN training process for more improving training. The CNN with prior information showed better segmentation performance than without.

A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

PubMed Central

Habas, Piotr A.; Kim, Kio; Corbett-Detig, James M.; Rousseau, Francois; Glenn, Orit A.; Barkovich, A. James; Studholme, Colin

2010-01-01

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation. PMID:20600970

Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.

PubMed

Zhou, Yongxin; Bai, Jing

2007-01-01

A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain.

PubMed

Hall, L O; Bensaid, A M; Clarke, L P; Velthuizen, R P; Silbiger, M S; Bezdek, J C

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation

PubMed Central

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

2012-01-01

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

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.

An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM).

PubMed

Feng, Xiang; Deistung, Andreas; Dwyer, Michael G; Hagemeier, Jesper; Polak, Paul; Lebenberg, Jessica; Frouin, Frédérique; Zivadinov, Robert; Reichenbach, Jürgen R; Schweser, Ferdinand

2017-06-01

Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T 1 -weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of >2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method. Copyright © 2017 Elsevier Inc. All rights reserved.

Multifractal texture estimation for detection and segmentation of brain tumors.

PubMed

Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

2013-11-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

PubMed Central

Islam, Atiq; Reza, Syed M. S.

2016-01-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

PubMed

Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

2016-05-01

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

Automatic segmentation of cortical vessels in pre- and post-tumor resection laser range scan images

NASA Astrophysics Data System (ADS)

Ding, Siyi; Miga, Michael I.; Thompson, Reid C.; Garg, Ishita; Dawant, Benoit M.

2009-02-01

Measurement of intra-operative cortical brain movement is necessary to drive mechanical models developed to predict sub-cortical shift. At our institution, this is done with a tracked laser range scanner. This device acquires both 3D range data and 2D photographic images. 3D cortical brain movement can be estimated if 2D photographic images acquired over time can be registered. Previously, we have developed a method, which permits this registration using vessels visible in the images. But, vessel segmentation required the localization of starting and ending points for each vessel segment. Here, we propose a method, which automates the segmentation process further. This method involves several steps: (1) correction of lighting artifacts, (2) vessel enhancement, and (3) vessels' centerline extraction. Result obtained on 5 images obtained in the operating room suggests that our method is robust and is able to segment vessels reliably.

A Multiscale Parallel Computing Architecture for Automated Segmentation of the Brain Connectome

PubMed Central

Knobe, Kathleen; Newton, Ryan R.; Schlimbach, Frank; Blower, Melanie; Reid, R. Clay

2015-01-01

Several groups in neurobiology have embarked into deciphering the brain circuitry using large-scale imaging of a mouse brain and manual tracing of the connections between neurons. Creating a graph of the brain circuitry, also called a connectome, could have a huge impact on the understanding of neurodegenerative diseases such as Alzheimer’s disease. Although considerably smaller than a human brain, a mouse brain already exhibits one billion connections and manually tracing the connectome of a mouse brain can only be achieved partially. This paper proposes to scale up the tracing by using automated image segmentation and a parallel computing approach designed for domain experts. We explain the design decisions behind our parallel approach and we present our results for the segmentation of the vasculature and the cell nuclei, which have been obtained without any manual intervention. PMID:21926011

aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data

PubMed Central

Niedworok, Christian J.; Brown, Alexander P. Y.; Jorge Cardoso, M.; Osten, Pavel; Ourselin, Sebastien; Modat, Marc; Margrie, Troy W.

2016-01-01

The validation of automated image registration and segmentation is crucial for accurate and reliable mapping of brain connectivity and function in three-dimensional (3D) data sets. While validation standards are necessarily high and routinely met in the clinical arena, they have to date been lacking for high-resolution microscopy data sets obtained from the rodent brain. Here we present a tool for optimized automated mouse atlas propagation (aMAP) based on clinical registration software (NiftyReg) for anatomical segmentation of high-resolution 3D fluorescence images of the adult mouse brain. We empirically evaluate aMAP as a method for registration and subsequent segmentation by validating it against the performance of expert human raters. This study therefore establishes a benchmark standard for mapping the molecular function and cellular connectivity of the rodent brain. PMID:27384127

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

PubMed Central

Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases. PMID:28985229

Registration of 3D fetal neurosonography and MRI☆

PubMed Central

Kuklisova-Murgasova, Maria; Cifor, Amalia; Napolitano, Raffaele; Papageorghiou, Aris; Quaghebeur, Gerardine; Rutherford, Mary A.; Hajnal, Joseph V.; Noble, J. Alison; Schnabel, Julia A.

2013-01-01

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image. PMID:23969169

Local contrast-enhanced MR images via high dynamic range processing.

PubMed

Chandra, Shekhar S; Engstrom, Craig; Fripp, Jurgen; Neubert, Ales; Jin, Jin; Walker, Duncan; Salvado, Olivier; Ho, Charles; Crozier, Stuart

2018-09-01

To develop a local contrast-enhancing and feature-preserving high dynamic range (HDR) image processing algorithm for multichannel and multisequence MR images of multiple body regions and tissues, and to evaluate its performance for structure visualization, bias field (correction) mitigation, and automated tissue segmentation. A multiscale-shape and detail-enhancement HDR-MRI algorithm is applied to data sets of multichannel and multisequence MR images of the brain, knee, breast, and hip. In multisequence 3T hip images, agreement between automatic cartilage segmentations and corresponding synthesized HDR-MRI series were computed for mean voxel overlap established from manual segmentations for a series of cases. Qualitative comparisons between the developed HDR-MRI and standard synthesis methods were performed on multichannel 7T brain and knee data, and multisequence 3T breast and knee data. The synthesized HDR-MRI series provided excellent enhancement of fine-scale structure from multiple scales and contrasts, while substantially reducing bias field effects in 7T brain gradient echo, T 1 and T 2 breast images and 7T knee multichannel images. Evaluation of the HDR-MRI approach on 3T hip multisequence images showed superior outcomes for automatic cartilage segmentations with respect to manual segmentation, particularly around regions with hyperintense synovial fluid, across a set of 3D sequences. The successful combination of multichannel/sequence MR images into a single-fused HDR-MR image format provided consolidated visualization of tissues within 1 omnibus image, enhanced definition of thin, complex anatomical structures in the presence of variable or hyperintense signals, and improved tissue (cartilage) segmentation outcomes. © 2018 International Society for Magnetic Resonance in Medicine.

Extracting morphologies from third harmonic generation images of structurally normal human brain tissue.

PubMed

Zhang, Zhiqing; Kuzmin, Nikolay V; Groot, Marie Louise; de Munck, Jan C

2017-06-01

The morphologies contained in 3D third harmonic generation (THG) images of human brain tissue can report on the pathological state of the tissue. However, the complexity of THG brain images makes the usage of modern image processing tools, especially those of image filtering, segmentation and validation, to extract this information challenging. We developed a salient edge-enhancing model of anisotropic diffusion for image filtering, based on higher order statistics. We split the intrinsic 3-phase segmentation problem into two 2-phase segmentation problems, each of which we solved with a dedicated model, active contour weighted by prior extreme. We applied the novel proposed algorithms to THG images of structurally normal ex-vivo human brain tissue, revealing key tissue components-brain cells, microvessels and neuropil, enabling statistical characterization of these components. Comprehensive comparison to manually delineated ground truth validated the proposed algorithms. Quantitative comparison to second harmonic generation/auto-fluorescence images, acquired simultaneously from the same tissue area, confirmed the correctness of the main THG features detected. The software and test datasets are available from the authors. z.zhang@vu.nl. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

PubMed

Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

2018-02-27

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

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

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.

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

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.

Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach

PubMed Central

Wang, Yue; Adalý, Tülay; Kung, Sun-Yuan; Szabo, Zsolt

2007-01-01

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches. PMID:18172510

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.

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire

2017-12-01

Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.

PubMed

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-04-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

Asymmetric bias in user guided segmentations of brain structures

NASA Astrophysics Data System (ADS)

Styner, Martin; Smith, Rachel G.; Graves, Michael M.; Mosconi, Matthew W.; Peterson, Sarah; White, Scott; Blocher, Joe; El-Sayed, Mohammed; Hazlett, Heather C.

2007-03-01

Brain morphometric studies often incorporate comparative asymmetry analyses of left and right hemispheric brain structures. In this work we show evidence that common methods of user guided structural segmentation exhibit strong left-right asymmetric biases and thus fundamentally influence any left-right asymmetry analyses. We studied several structural segmentation methods with varying degree of user interaction from pure manual outlining to nearly fully automatic procedures. The methods were applied to MR images and their corresponding left-right mirrored images from an adult and a pediatric study. Several expert raters performed the segmentations of all structures. The asymmetric segmentation bias is assessed by comparing the left-right volumetric asymmetry in the original and mirrored datasets, as well as by testing each sides volumetric differences to a zero mean standard t-tests. The structural segmentations of caudate, putamen, globus pallidus, amygdala and hippocampus showed a highly significant asymmetric bias using methods with considerable manual outlining or landmark placement. Only the lateral ventricle segmentation revealed no asymmetric bias due to the high degree of automation and a high intensity contrast on its boundary. Our segmentation methods have been adapted in that they are applied to only one of the hemispheres in an image and its left-right mirrored image. Our work suggests that existing studies of hemispheric asymmetry without similar precautions should be interpreted in a new, skeptical light. Evidence of an asymmetric segmentation bias is novel and unknown to the imaging community. This result seems less surprising to the visual perception community and its likely cause is differences in perception of oppositely curved 3D structures.

Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation.

PubMed

Mehmood, Irfan; Ejaz, Naveed; Sajjad, Muhammad; Baik, Sung Wook

2013-10-01

The objective of the present study is to explore prioritization methods in diagnostic imaging modalities to automatically determine the contents of medical images. In this paper, we propose an efficient prioritization of brain MRI. First, the visual perception of the radiologists is adapted to identify salient regions. Then this saliency information is used as an automatic label for accurate segmentation of brain lesion to determine the scientific value of that image. The qualitative and quantitative results prove that the rankings generated by the proposed method are closer to the rankings created by radiologists. Copyright © 2013 Elsevier Ltd. All rights reserved.

Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling.

PubMed

Tong, Tong; Wolz, Robin; Coupé, Pierrick; Hajnal, Joseph V; Rueckert, Daniel

2013-08-01

We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods. Copyright © 2013 Elsevier Inc. All rights reserved.

Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

NASA Astrophysics Data System (ADS)

Hajiesmaeili, Maryam; Dehmeshki, Jamshid; Bagheri Nakhjavanlo, Bashir; Ellis, Tim

2014-04-01

Shrinkage of the hippocampus is a primary biomarker for Alzheimer's disease and can be measured through accurate segmentation of brain MR images. The paper will describe the problem of initialisation of a 3D level set algorithm for hippocampus segmentation that must cope with the some challenging characteristics, such as small size, wide range of intensities, narrow width, and shape variation. In addition, MR images require bias correction, to account for additional inhomogeneity associated with the scanner technology. Due to these inhomogeneities, using a single initialisation seed region inside the hippocampus is prone to failure. Alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus. The Dice metric is used to validate our segmentation results with respect to ground truth for a dataset of 25 MR images. Experimental results indicate significant improvement in segmentation performance using the multiple initialisations techniques, yielding more accurate segmentation results for the hippocampus.

MR/PET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

PubMed Central

Fei, Baowei; Yang, Xiaofeng; Nye, Jonathon A.; Aarsvold, John N.; Raghunath, Nivedita; Cervo, Morgan; Stark, Rebecca; Meltzer, Carolyn C.; Votaw, John R.

2012-01-01

Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET. PMID:23039679

Volume estimation of brain abnormalities in MRI data

NASA Astrophysics Data System (ADS)

Suprijadi, Pratama, S. H.; Haryanto, F.

2014-02-01

The abnormality of brain tissue always becomes a crucial issue in medical field. This medical condition can be recognized through segmentation of certain region from medical images obtained from MRI dataset. Image processing is one of computational methods which very helpful to analyze the MRI data. In this study, combination of segmentation and rendering image were used to isolate tumor and stroke. Two methods of thresholding were employed to segment the abnormality occurrence, followed by filtering to reduce non-abnormality area. Each MRI image is labeled and then used for volume estimations of tumor and stroke-attacked area. The algorithms are shown to be successful in isolating tumor and stroke in MRI images, based on thresholding parameter and stated detection accuracy.

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.

Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

NASA Astrophysics Data System (ADS)

Gaffney, Kevin P.; Aghaei, Faranak; Battiste, James; Zheng, Bin

2017-03-01

Detection of residual brain tumor is important to evaluate efficacy of brain cancer surgery, determine optimal strategy of further radiation therapy if needed, and assess ultimate prognosis of the patients. Brain MR is a commonly used imaging modality for this task. In order to distinguish between residual tumor and surgery induced scar tissues, two sets of MRI scans are conducted pre- and post-gadolinium contrast injection. The residual tumors are only enhanced in the post-contrast injection images. However, subjective reading and quantifying this type of brain MR images faces difficulty in detecting real residual tumor regions and measuring total volume of the residual tumor. In order to help solve this clinical difficulty, we developed and tested a new interactive computer-aided detection scheme, which consists of three consecutive image processing steps namely, 1) segmentation of the intracranial region, 2) image registration and subtraction, 3) tumor segmentation and refinement. The scheme also includes a specially designed and implemented graphical user interface (GUI) platform. When using this scheme, two sets of pre- and post-contrast injection images are first automatically processed to detect and quantify residual tumor volume. Then, a user can visually examine segmentation results and conveniently guide the scheme to correct any detection or segmentation errors if needed. The scheme has been repeatedly tested using five cases. Due to the observed high performance and robustness of the testing results, the scheme is currently ready for conducting clinical studies and helping clinicians investigate the association between this quantitative image marker and outcome of patients.

Atlas-Guided Segmentation of Vervet Monkey Brain MRI

PubMed Central

Fedorov, Andriy; Li, Xiaoxing; Pohl, Kilian M; Bouix, Sylvain; Styner, Martin; Addicott, Merideth; Wyatt, Chris; Daunais, James B; Wells, William M; Kikinis, Ron

2011-01-01

The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model. PMID:22253661

White matter lesion extension to automatic brain tissue segmentation on MRI.

PubMed

de Boer, Renske; Vrooman, Henri A; van der Lijn, Fedde; Vernooij, Meike W; Ikram, M Arfan; van der Lugt, Aad; Breteler, Monique M B; Niessen, Wiro J

2009-05-01

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images.

PubMed

Hamoud Al-Tamimi, Mohammed Sabbih; Sulong, Ghazali; Shuaib, Ibrahim Lutfi

2015-07-01

Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor. Copyright © 2015 Elsevier Inc. All rights reserved.

Brain tumor classification of microscopy images using deep residual learning

NASA Astrophysics Data System (ADS)

Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

2016-12-01

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

Brain MR image segmentation based on an improved active contour model

PubMed Central

Meng, Xiangrui; Gu, Wenya; Zhang, Jianwei

2017-01-01

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%. PMID:28854235

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.

A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain.

PubMed

Márquez Neila, Pablo; Baumela, Luis; González-Soriano, Juncal; Rodríguez, Jose-Rodrigo; DeFelipe, Javier; Merchán-Pérez, Ángel

2016-04-01

Recent electron microscopy (EM) imaging techniques permit the automatic acquisition of a large number of serial sections from brain samples. Manual segmentation of these images is tedious, time-consuming and requires a high degree of user expertise. Therefore, there is considerable interest in developing automatic segmentation methods. However, currently available methods are computationally demanding in terms of computer time and memory usage, and to work properly many of them require image stacks to be isotropic, that is, voxels must have the same size in the X, Y and Z axes. We present a method that works with anisotropic voxels and that is computationally efficient allowing the segmentation of large image stacks. Our approach involves anisotropy-aware regularization via conditional random field inference and surface smoothing techniques to improve the segmentation and visualization. We have focused on the segmentation of mitochondria and synaptic junctions in EM stacks from the cerebral cortex, and have compared the results to those obtained by other methods. Our method is faster than other methods with similar segmentation results. Our image regularization procedure introduces high-level knowledge about the structure of labels. We have also reduced memory requirements with the introduction of energy optimization in overlapping partitions, which permits the regularization of very large image stacks. Finally, the surface smoothing step improves the appearance of three-dimensional renderings of the segmented volumes.

Automatic Brain Portion Segmentation From Magnetic Resonance Images of Head Scans Using Gray Scale Transformation and Morphological Operations.

PubMed

Somasundaram, Karuppanagounder; Ezhilarasan, Kamalanathan

2015-01-01

To develop an automatic skull stripping method for magnetic resonance imaging (MRI) of human head scans. The proposed method is based on gray scale transformation and morphological operations. The proposed method has been tested with 20 volumes of normal T1-weighted images taken from Internet Brain Segmentation Repository. Experimental results show that the proposed method gives better results than the popular skull stripping methods Brain Extraction Tool and Brain Surface Extractor. The average value of Jaccard and Dice coefficients are 0.93 and 0.962 respectively. In this article, we have proposed a novel skull stripping method using intensity transformation and morphological operations. This is a low computational complexity method but gives competitive or better results than that of the popular skull stripping methods Brain Surface Extractor and Brain Extraction Tool.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

PubMed Central

Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

2013-01-01

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers. PMID:24098863

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

Three validation metrics for automated probabilistic image segmentation of brain tumours

PubMed Central

Zou, Kelly H.; Wells, William M.; Kikinis, Ron; Warfield, Simon K.

2005-01-01

SUMMARY The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts’ manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered. PMID:15083482

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

[Research on K-means clustering segmentation method for MRI brain image based on selecting multi-peaks in gray histogram].

PubMed

Chen, Zhaoxue; Yu, Haizhong; Chen, Hao

2013-12-01

To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.

Development of quantitative analysis method for stereotactic brain image: assessment of reduced accumulation in extent and severity using anatomical segmentation.

PubMed

Mizumura, Sunao; Kumita, Shin-ichiro; Cho, Keiichi; Ishihara, Makiko; Nakajo, Hidenobu; Toba, Masahiro; Kumazaki, Tatsuo

2003-06-01

Through visual assessment by three-dimensional (3D) brain image analysis methods using stereotactic brain coordinates system, such as three-dimensional stereotactic surface projections and statistical parametric mapping, it is difficult to quantitatively assess anatomical information and the range of extent of an abnormal region. In this study, we devised a method to quantitatively assess local abnormal findings by segmenting a brain map according to anatomical structure. Through quantitative local abnormality assessment using this method, we studied the characteristics of distribution of reduced blood flow in cases with dementia of the Alzheimer type (DAT). Using twenty-five cases with DAT (mean age, 68.9 years old), all of whom were diagnosed as probable Alzheimer's disease based on NINCDS-ADRDA, we collected I-123 iodoamphetamine SPECT data. A 3D brain map using the 3D-SSP program was compared with the data of 20 cases in the control group, who age-matched the subject cases. To study local abnormalities on the 3D images, we divided the whole brain into 24 segments based on anatomical classification. We assessed the extent of an abnormal region in each segment (rate of the coordinates with a Z-value that exceeds the threshold value, in all coordinates within a segment), and severity (average Z-value of the coordinates with a Z-value that exceeds the threshold value). This method clarified orientation and expansion of reduced accumulation, through classifying stereotactic brain coordinates according to the anatomical structure. This method was considered useful for quantitatively grasping distribution abnormalities in the brain and changes in abnormality distribution.

Multi-fractal texture features for brain tumor and edema segmentation

NASA Astrophysics Data System (ADS)

Reza, S.; Iftekharuddin, K. M.

2014-03-01

In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

Wavelet-space correlation imaging for high-speed MRI without motion monitoring or data segmentation.

PubMed

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2015-12-01

This study aims to (i) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and (ii) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called "wavelet-space correlation imaging", is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI, and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. © 2014 Wiley Periodicals, Inc.

Automated tissue segmentation of MR brain images in the presence of white matter lesions.

PubMed

Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

2017-01-01

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.

Brain tumor segmentation with Vander Lugt correlator based active contour.

PubMed

Essadike, Abdelaziz; Ouabida, Elhoussaine; Bouzid, Abdenbi

2018-07-01

The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation. In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages. The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image. Results reported on BRATS database reveal that our proposed system improves over the recently published state-of-the-art methods in brain tumor detection and segmentation. Copyright © 2018 Elsevier B.V. All rights reserved.

Wavelet-space Correlation Imaging for High-speed MRI without Motion Monitoring or Data Segmentation

PubMed Central

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2014-01-01

Purpose This study aims to 1) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and 2) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Methods Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called “wavelet-space correlation imaging”, is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Results Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Conclusion Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. PMID:25470230

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.

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

Deep learning for medical image segmentation - using the IBM TrueNorth neurosynaptic system

NASA Astrophysics Data System (ADS)

Moran, Steven; Gaonkar, Bilwaj; Whitehead, William; Wolk, Aidan; Macyszyn, Luke; Iyer, Subramanian S.

2018-03-01

Deep convolutional neural networks have found success in semantic image segmentation tasks in computer vision and medical imaging. These algorithms are executed on conventional von Neumann processor architectures or GPUs. This is suboptimal. Neuromorphic processors that replicate the structure of the brain are better-suited to train and execute deep learning models for image segmentation by relying on massively-parallel processing. However, given that they closely emulate the human brain, on-chip hardware and digital memory limitations also constrain them. Adapting deep learning models to execute image segmentation tasks on such chips, requires specialized training and validation. In this work, we demonstrate for the first-time, spinal image segmentation performed using a deep learning network implemented on neuromorphic hardware of the IBM TrueNorth Neurosynaptic System and validate the performance of our network by comparing it to human-generated segmentations of spinal vertebrae and disks. To achieve this on neuromorphic hardware, the training model constrains the coefficients of individual neurons to {-1,0,1} using the Energy Efficient Deep Neuromorphic (EEDN)1 networks training algorithm. Given the 1 million neurons and 256 million synapses, the scale and size of the neural network implemented by the IBM TrueNorth allows us to execute the requisite mapping between segmented images and non-uniform intensity MR images >20 times faster than on a GPU-accelerated network and using <0.1 W. This speed and efficiency implies that a trained neuromorphic chip can be deployed in intra-operative environments where real-time medical image segmentation is necessary.

Combining Segmented Grey and White Matter Images Improves Voxel-based Morphometry for the Case of Dilated Lateral Ventricles.

PubMed

Goto, Masami; Abe, Osamu; Aoki, Shigeki; Kamagata, Koji; Hori, Masaaki; Miyati, Tosiaki; Gomi, Tsutomu; Takeda, Tohoru

2018-01-18

To evaluate the error in segmented tissue images and to show the usefulness of the brain image in voxel-based morphometry (VBM) using Statistical Parametric Mapping (SPM) 12 software and 3D T 1 -weighted magnetic resonance images (3D-T 1 WIs) processed to simulate idiopathic normal pressure hydrocephalus (iNPH). VBM analysis was performed on sagittal 3D-T 1 WIs obtained in 22 healthy volunteers using a 1.5T MR scanner. Regions of interest for the lateral ventricles of all subjects were carefully outlined on the original 3D-T 1 WIs, and two types of simulated 3D-T 1 WI were also prepared (non-dilated 3D-T 1 WI as normal control and dilated 3D-T 1 WI to simulate iNPH). All simulated 3D-T 1 WIs were segmented into gray matter, white matter, and cerebrospinal fluid images, and normalized to standard space. A brain image was made by adding the gray and white matter images. After smoothing with a 6-mm isotropic Gaussian kernel, group comparisons (dilated vs non-dilated) were made for gray and white matter, cerebrospinal fluid, and brain images using a paired t-test. In evaluation of tissue volume, estimation error was larger using gray or white matter images than using the brain image, and estimation errors in gray and white matter volume change were found for the brain surface. To our knowledge, this is the first VBM study to show the possibility that VBM of gray and white matter volume on the brain surface may be more affected by individual differences in the level of dilation of the lateral ventricles than by individual differences in gray and white matter volumes. We recommend that VBM evaluation in patients with iNPH should be performed using the brain image rather than the gray and white matter images.

[RSF model optimization and its application to brain tumor segmentation in MRI].

PubMed

Cheng, Zhaoning; Song, Zhijian

2013-04-01

Magnetic resonance imaging (MRI) is usually obscure and non-uniform in gray, and the tumors inside are poorly circumscribed, hence the automatic tumor segmentation in MRI is very difficult. Region-scalable fitting (RSF) energy model is a new segmentation approach for some uneven grayscale images. However, the level set formulation (LSF) of RSF model is not suitable for the environment with different grey level distribution inside and outside the intial contour, and the complex intensity environment of MRI always makes it hard to get ideal segmentation results. Therefore, we improved the model by a new LSF and combined it with the mean shift method, which can be helpful for tumor segmentation and has better convergence and target direction. The proposed method has been utilized in a series of studies for real MRI images, and the results showed that it could realize fast, accurate and robust segmentations for brain tumors in MRI, which has great clinical significance.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.

PubMed

Latha, Manohar; Kavitha, Ganesan

2018-02-03

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Self-correcting multi-atlas segmentation

NASA Astrophysics Data System (ADS)

Gao, Yi; Wilford, Andrew; Guo, Liang

2016-03-01

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

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.

Sensitivity analysis of brain morphometry based on MRI-derived surface models

NASA Astrophysics Data System (ADS)

Klein, Gregory J.; Teng, Xia; Schoenemann, P. T.; Budinger, Thomas F.

1998-07-01

Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for characterizing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due to shrinkage of the cerebrum after extraction from the skull, deformation of slices during sectioning, and numerous other factors. In vivo imaging studies of brain anatomy avoid these problems and allow repetitive studies following progression of brain structure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh models of the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing technique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosions and openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region- following procedure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiled together to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation of cortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex from the skull, the technique is automatic and requires only modest computation time on modern workstations. Though the use of image data avoids many of the pitfalls of ex vivo and sectioning techniques, our MRI-based technique is still vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies include segmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise and surface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cortical surface area and volume.

An accurate segmentation method for volumetry of brain tumor in 3D MRI

NASA Astrophysics Data System (ADS)

Wang, Jiahui; Li, Qiang; Hirai, Toshinori; Katsuragawa, Shigehiko; Li, Feng; Doi, Kunio

2008-03-01

Accurate volumetry of brain tumors in magnetic resonance imaging (MRI) is important for evaluating the interval changes in tumor volumes during and after treatment, and also for planning of radiation therapy. In this study, an automated volumetry method for brain tumors in MRI was developed by use of a new three-dimensional (3-D) image segmentation technique. First, the central location of a tumor was identified by a radiologist, and then a volume of interest (VOI) was determined automatically. To substantially simplify tumor segmentation, we transformed the 3-D image of the tumor into a two-dimensional (2-D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the tumor scanned the 3-D image spirally from the "north pole" to the "south pole". The voxels scanned by the radial line provided a transformed 2-D image. We employed dynamic programming to delineate an "optimal" outline of the tumor in the transformed 2-D image. We then transformed the optimal outline back into 3-D image space to determine the volume of the tumor. The volumetry method was trained and evaluated by use of 16 cases with 35 brain tumors. The agreement between tumor volumes provided by computer and a radiologist was employed as a performance metric. Our method provided relatively accurate results with a mean agreement value of 88%.

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

NASA Astrophysics Data System (ADS)

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

2012-02-01

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

Applications of magnetic resonance image segmentation in neurology

NASA Astrophysics Data System (ADS)

Heinonen, Tomi; Lahtinen, Antti J.; Dastidar, Prasun; Ryymin, Pertti; Laarne, Paeivi; Malmivuo, Jaakko; Laasonen, Erkki; Frey, Harry; Eskola, Hannu

1999-05-01

After the introduction of digital imagin devices in medicine computerized tissue recognition and classification have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical modes, 3D visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment.

Automated segmentation of ventricles from serial brain MRI for the quantification of volumetric changes associated with communicating hydrocephalus in patients with brain tumor

NASA Astrophysics Data System (ADS)

Pura, John A.; Hamilton, Allison M.; Vargish, Geoffrey A.; Butman, John A.; Linguraru, Marius George

2011-03-01

Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1- weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

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

PubMed

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

2015-09-01

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

Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.

PubMed

Li, Yuhong; Jia, Fucang; Qin, Jing

2016-10-01

Accurately segmenting and quantifying brain gliomas from magnetic resonance (MR) images remains a challenging task because of the large spatial and structural variability among brain tumors. To develop a fully automatic and accurate brain tumor segmentation algorithm, we present a probabilistic model of multimodal MR brain tumor segmentation. This model combines sparse representation and the Markov random field (MRF) to solve the spatial and structural variability problem. We formulate the tumor segmentation problem as a multi-classification task by labeling each voxel as the maximum posterior probability. We estimate the maximum a posteriori (MAP) probability by introducing the sparse representation into a likelihood probability and a MRF into the prior probability. Considering the MAP as an NP-hard problem, we convert the maximum posterior probability estimation into a minimum energy optimization problem and employ graph cuts to find the solution to the MAP estimation. Our method is evaluated using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013) and obtained Dice coefficient metric values of 0.85, 0.75, and 0.69 on the high-grade Challenge data set, 0.73, 0.56, and 0.54 on the high-grade Challenge LeaderBoard data set, and 0.84, 0.54, and 0.57 on the low-grade Challenge data set for the complete, core, and enhancing regions. The experimental results show that the proposed algorithm is valid and ranks 2nd compared with the state-of-the-art tumor segmentation algorithms in the MICCAI BRATS 2013 challenge. Copyright © 2016 Elsevier B.V. All rights reserved.

Brain Activity and Human Unilateral Chewing

PubMed Central

Quintero, A.; Ichesco, E.; Myers, C.; Schutt, R.; Gerstner, G.E.

2012-01-01

Brain mechanisms underlying mastication have been studied in non-human mammals but less so in humans. We used functional magnetic resonance imaging (fMRI) to evaluate brain activity in humans during gum chewing. Chewing was associated with activations in the cerebellum, motor cortex and caudate, cingulate, and brainstem. We also divided the 25-second chew-blocks into 5 segments of equal 5-second durations and evaluated activations within and between each of the 5 segments. This analysis revealed activation clusters unique to the initial segment, which may indicate brain regions involved with initiating chewing. Several clusters were uniquely activated during the last segment as well, which may represent brain regions involved with anticipatory or motor events associated with the end of the chew-block. In conclusion, this study provided evidence for specific brain areas associated with chewing in humans and demonstrated that brain activation patterns may dynamically change over the course of chewing sequences. PMID:23103631

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

PubMed

Cui, Shaoguo; Mao, Lei; Jiang, Jingfeng; Liu, Chang; Xiong, Shuyu

2018-01-01

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.

A survey of MRI-based medical image analysis for brain tumor studies

NASA Astrophysics Data System (ADS)

Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P.; Reyes, Mauricio

2013-07-01

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study.

PubMed

Chae, Soo Young; Suh, Sangil; Ryoo, Inseon; Park, Arim; Noh, Kyoung Jin; Shim, Hackjoon; Seol, Hae Young

2017-05-01

We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c ) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

PubMed Central

Chen, Yasheng; Juttukonda, Meher; Su, Yi; Benzinger, Tammie; Rubin, Brian G.; Lee, Yueh Z.; Lin, Weili; Shen, Dinggang; Lalush, David

2015-01-01

Purpose To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. Materials and Methods In this institutional review board–approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. Results The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). Conclusion PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction. © RSNA, 2014 PMID:25521778

Regional growth and atlasing of the developing human brain

PubMed Central

Makropoulos, Antonios; Aljabar, Paul; Wright, Robert; Hüning, Britta; Merchant, Nazakat; Arichi, Tomoki; Tusor, Nora; Hajnal, Joseph V.; Edwards, A. David; Counsell, Serena J.; Rueckert, Daniel

2016-01-01

Detailed morphometric analysis of the neonatal brain is required to characterise brain development and define neuroimaging biomarkers related to impaired brain growth. Accurate automatic segmentation of neonatal brain MRI is a prerequisite to analyse large datasets. We have previously presented an accurate and robust automatic segmentation technique for parcellating the neonatal brain into multiple cortical and subcortical regions. In this study, we further extend our segmentation method to detect cortical sulci and provide a detailed delineation of the cortical ribbon. These detailed segmentations are used to build a 4-dimensional spatio-temporal structural atlas of the brain for 82 cortical and subcortical structures throughout this developmental period. We employ the algorithm to segment an extensive database of 420 MR images of the developing brain, from 27 to 45 weeks post-menstrual age at imaging. Regional volumetric and cortical surface measurements are derived and used to investigate brain growth and development during this critical period and to assess the impact of immaturity at birth. Whole brain volume, the absolute volume of all structures studied, cortical curvature and cortical surface area increased with increasing age at scan. Relative volumes of cortical grey matter, cerebellum and cerebrospinal fluid increased with age at scan, while relative volumes of white matter, ventricles, brainstem and basal ganglia and thalami decreased. Preterm infants at term had smaller whole brain volumes, reduced regional white matter and cortical and subcortical grey matter volumes, and reduced cortical surface area compared with term born controls, while ventricular volume was greater in the preterm group. Increasing prematurity at birth was associated with a reduction in total and regional white matter, cortical and subcortical grey matter volume, an increase in ventricular volume, and reduced cortical surface area. PMID:26499811

Regional growth and atlasing of the developing human brain.

PubMed

Makropoulos, Antonios; Aljabar, Paul; Wright, Robert; Hüning, Britta; Merchant, Nazakat; Arichi, Tomoki; Tusor, Nora; Hajnal, Joseph V; Edwards, A David; Counsell, Serena J; Rueckert, Daniel

2016-01-15

Detailed morphometric analysis of the neonatal brain is required to characterise brain development and define neuroimaging biomarkers related to impaired brain growth. Accurate automatic segmentation of neonatal brain MRI is a prerequisite to analyse large datasets. We have previously presented an accurate and robust automatic segmentation technique for parcellating the neonatal brain into multiple cortical and subcortical regions. In this study, we further extend our segmentation method to detect cortical sulci and provide a detailed delineation of the cortical ribbon. These detailed segmentations are used to build a 4-dimensional spatio-temporal structural atlas of the brain for 82 cortical and subcortical structures throughout this developmental period. We employ the algorithm to segment an extensive database of 420 MR images of the developing brain, from 27 to 45weeks post-menstrual age at imaging. Regional volumetric and cortical surface measurements are derived and used to investigate brain growth and development during this critical period and to assess the impact of immaturity at birth. Whole brain volume, the absolute volume of all structures studied, cortical curvature and cortical surface area increased with increasing age at scan. Relative volumes of cortical grey matter, cerebellum and cerebrospinal fluid increased with age at scan, while relative volumes of white matter, ventricles, brainstem and basal ganglia and thalami decreased. Preterm infants at term had smaller whole brain volumes, reduced regional white matter and cortical and subcortical grey matter volumes, and reduced cortical surface area compared with term born controls, while ventricular volume was greater in the preterm group. Increasing prematurity at birth was associated with a reduction in total and regional white matter, cortical and subcortical grey matter volume, an increase in ventricular volume, and reduced cortical surface area. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets.

PubMed

Jiang, Jun; Wu, Yao; Huang, Meiyan; Yang, Wei; Chen, Wufan; Feng, Qianjin

2013-01-01

Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

PubMed

Menze, Bjoern H; Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B; Ayache, Nicholas; Buendia, Patricia; Collins, D Louis; Cordier, Nicolas; Corso, Jason J; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M; Jena, Raj; John, Nigel M; Konukoglu, Ender; Lashkari, Danial; Mariz, José Antonió; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J; Raviv, Tammy Riklin; Reza, Syed M S; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A; Sousa, Nuno; Subbanna, Nagesh K; Szekely, Gabor; Taylor, Thomas J; Thomas, Owen M; Tustison, Nicholas J; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

2015-10-01

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

PubMed Central

Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B.; Ayache, Nicholas; Buendia, Patricia; Collins, D. Louis; Cordier, Nicolas; Corso, Jason J.; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R.; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M.; Jena, Raj; John, Nigel M.; Konukoglu, Ender; Lashkari, Danial; Mariz, José António; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J.; Raviv, Tammy Riklin; Reza, Syed M. S.; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A.; Sousa, Nuno; Subbanna, Nagesh K.; Szekely, Gabor; Taylor, Thomas J.; Thomas, Owen M.; Tustison, Nicholas J.; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

2016-01-01

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource. PMID:25494501

Automated segmentation of the actively stained mouse brain using multi-spectral MR microscopy.

PubMed

Sharief, Anjum A; Badea, Alexandra; Dale, Anders M; Johnson, G Allan

2008-01-01

Magnetic resonance microscopy (MRM) has created new approaches for high-throughput morphological phenotyping of mouse models of diseases. Transgenic and knockout mice serve as a test bed for validating hypotheses that link genotype to the phenotype of diseases, as well as developing and tracking treatments. We describe here a Markov random fields based segmentation of the actively stained mouse brain, as a prerequisite for morphological phenotyping. Active staining achieves higher signal to noise ratio (SNR) thereby enabling higher resolution imaging per unit time than obtained in previous formalin-fixed mouse brain studies. The segmentation algorithm was trained on isotropic 43-mum T1- and T2-weighted MRM images. The mouse brain was segmented into 33 structures, including the hippocampus, amygdala, hypothalamus, thalamus, as well as fiber tracts and ventricles. Probabilistic information used in the segmentation consisted of (a) intensity distributions in the T1- and T2-weighted data, (b) location, and (c) contextual priors for incorporating spatial information. Validation using standard morphometric indices showed excellent consistency between automatically and manually segmented data. The algorithm has been tested on the widely used C57BL/6J strain, as well as on a selection of six recombinant inbred BXD strains, chosen especially for their largely variant hippocampus.

Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation.

PubMed

Alex, Varghese; Vaidhya, Kiran; Thirunavukkarasu, Subramaniam; Kesavadas, Chandrasekharan; Krishnamurthi, Ganapathy

2017-10-01

The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. Stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with patches drawn from a limited number of patients ([Formula: see text], 40, 65). The results show negligible loss in performance even when SDAE was fine-tuned using 20 labeled patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach in which a network pretrained with high grade glioma data was fine-tuned using LGG image patches. The networks were also shown to generalize well and provide good segmentation on unseen BraTS 2013 and BraTS 2015 test data. The manuscript also includes the use of a single layer DAE, referred to as novelty detector (ND). ND was trained to accurately reconstruct nonlesion patches. The reconstruction error maps of test data were used to localize lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the nonlesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database.

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Machine learning in a graph framework for subcortical segmentation

NASA Astrophysics Data System (ADS)

Guo, Zhihui; Kashyap, Satyananda; Sonka, Milan; Oguz, Ipek

2017-02-01

Automated and reliable segmentation of subcortical structures from human brain magnetic resonance images is of great importance for volumetric and shape analyses in quantitative neuroimaging studies. However, poor boundary contrast and variable shape of these structures make the automated segmentation a tough task. We propose a 3D graph-based machine learning method, called LOGISMOS-RF, to segment the caudate and the putamen from brain MRI scans in a robust and accurate way. An atlas-based tissue classification and bias-field correction method is applied to the images to generate an initial segmentation for each structure. Then a 3D graph framework is utilized to construct a geometric graph for each initial segmentation. A locally trained random forest classifier is used to assign a cost to each graph node. The max-flow algorithm is applied to solve the segmentation problem. Evaluation was performed on a dataset of T1-weighted MRI's of 62 subjects, with 42 images used for training and 20 images for testing. For comparison, FreeSurfer, FSL and BRAINSCut approaches were also evaluated using the same dataset. Dice overlap coefficients and surface-to-surfaces distances between the automated segmentation and expert manual segmentations indicate the results of our method are statistically significantly more accurate than the three other methods, for both the caudate (Dice: 0.89 +/- 0.03) and the putamen (0.89 +/- 0.03).

Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

PubMed

Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

2012-01-01

Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.

A prior feature SVM – MRF based method for mouse brain segmentation

PubMed Central

Wu, Teresa; Bae, Min Hyeok; Zhang, Min; Pan, Rong; Badea, Alexandra

2012-01-01

We introduce an automated method, called prior feature Support Vector Machine- Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer’s Disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders. PMID:21988893

A prior feature SVM-MRF based method for mouse brain segmentation.

PubMed

Wu, Teresa; Bae, Min Hyeok; Zhang, Min; Pan, Rong; Badea, Alexandra

2012-02-01

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders. Copyright © 2011 Elsevier Inc. All rights reserved.

BEaST: brain extraction based on nonlocal segmentation technique.

PubMed

Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir; Manjón, José V; Leung, Kelvin K; Guizard, Nicolas; Wassef, Shafik N; Østergaard, Lasse Riis; Collins, D Louis

2012-02-01

Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834±0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Copyright © 2011 Elsevier Inc. All rights reserved.

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

PubMed

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

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).

Medical diagnosis imaging systems: image and signal processing applications aided by fuzzy logic

NASA Astrophysics Data System (ADS)

Hata, Yutaka

2010-04-01

First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are successfully determined.

Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Ross, Stephen R.; Wang, Yunzhi; Wu, Dee H.; Cornwell, Benjamin O.; Ray, Bappaditya; Zheng, Bin

2017-03-01

Aneurysmal subarachnoid hemorrhage (aSAH) is a form of hemorrhagic stroke that affects middle-aged individuals and associated with significant morbidity and/or mortality especially those presenting with higher clinical and radiologic grades at the time of admission. Previous studies suggested that blood extravasated after aneurysmal rupture was a potentially clinical prognosis factor. But all such studies used qualitative scales to predict prognosis. The purpose of this study is to develop and test a new interactive computer-aided detection (CAD) tool to detect, segment and quantify brain hemorrhage and ventricular cerebrospinal fluid on non-contrasted brain CT images. First, CAD segments brain skull using a multilayer region growing algorithm with adaptively adjusted thresholds. Second, CAD assigns pixels inside the segmented brain region into one of three classes namely, normal brain tissue, blood and fluid. Third, to avoid "black-box" approach and increase accuracy in quantification of these two image markers using CT images with large noise variation in different cases, a graphic User Interface (GUI) was implemented and allows users to visually examine segmentation results. If a user likes to correct any errors (i.e., deleting clinically irrelevant blood or fluid regions, or fill in the holes inside the relevant blood or fluid regions), he/she can manually define the region and select a corresponding correction function. CAD will automatically perform correction and update the computed data. The new CAD tool is now being used in clinical and research settings to estimate various quantitatively radiological parameters/markers to determine radiological severity of aSAH at presentation and correlate the estimations with various homeostatic/metabolic derangements and predict clinical outcome.

Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images.

PubMed

Harati, Vida; Khayati, Rasoul; Farzan, Abdolreza

2011-07-01

Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary. A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures. Copyright © 2011 Elsevier Ltd. All rights reserved.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

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.

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

PubMed

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

2015-12-01

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

3-D in vivo brain tumor geometry study by scaling analysis

NASA Astrophysics Data System (ADS)

Torres Hoyos, F.; Martín-Landrove, M.

2012-02-01

A new method, based on scaling analysis, is used to calculate fractal dimension and local roughness exponents to characterize in vivo 3-D tumor growth in the brain. Image acquisition was made according to the standard protocol used for brain radiotherapy and radiosurgery, i.e., axial, coronal and sagittal magnetic resonance T1-weighted images, and comprising the brain volume for image registration. Image segmentation was performed by the application of the k-means procedure upon contrasted images. We analyzed glioblastomas, astrocytomas, metastases and benign brain tumors. The results show significant variations of the parameters depending on the tumor stage and histological origin.

3D surface rendered MR images of the brain and its vasculature.

PubMed

Cline, H E; Lorensen, W E; Souza, S P; Jolesz, F A; Kikinis, R; Gerig, G; Kennedy, T E

1991-01-01

Both time-of-flight and phase contrast magnetic resonance angiography images are combined with stationary tissue images to provide data depicting two contrast relationships yielding intrinsic discrimination of brain matter and flowing blood. A computer analysis is based on nearest neighbor segmentation and the connection between anatomical structures to partition the images into different tissue categories: from which, high resolution brain parenchymal and vascular surfaces are constructed and rendered in juxtaposition, aiding in surgical planning.

Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel

2014-03-01

Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors

PubMed Central

Sanjuán, Ana; Price, Cathy J.; Mancini, Laura; Josse, Goulven; Grogan, Alice; Yamamoto, Adam K.; Geva, Sharon; Leff, Alex P.; Yousry, Tarek A.; Seghier, Mohamed L.

2013-01-01

Brain tumors can have different shapes or locations, making their identification very challenging. In functional MRI, it is not unusual that patients have only one anatomical image due to time and financial constraints. Here, we provide a modified automatic lesion identification (ALI) procedure which enables brain tumor identification from single MR images. Our method rests on (A) a modified segmentation-normalization procedure with an explicit “extra prior” for the tumor and (B) an outlier detection procedure for abnormal voxel (i.e., tumor) classification. To minimize tissue misclassification, the segmentation-normalization procedure requires prior information of the tumor location and extent. We therefore propose that ALI is run iteratively so that the output of Step B is used as a patient-specific prior in Step A. We test this procedure on real T1-weighted images from 18 patients, and the results were validated in comparison to two independent observers' manual tracings. The automated procedure identified the tumors successfully with an excellent agreement with the manual segmentation (area under the ROC curve = 0.97 ± 0.03). The proposed procedure increases the flexibility and robustness of the ALI tool and will be particularly useful for lesion-behavior mapping studies, or when lesion identification and/or spatial normalization are problematic. PMID:24381535

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods.

PubMed

Serag, Ahmed; Blesa, Manuel; Moore, Emma J; Pataky, Rozalia; Sparrow, Sarah A; Wilkinson, A G; Macnaught, Gillian; Semple, Scott I; Boardman, James P

2016-03-24

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases 'uniformly' distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course.

Multimodal Image Registration through Simultaneous Segmentation.

PubMed

Aganj, Iman; Fischl, Bruce

2017-11-01

Multimodal image registration facilitates the combination of complementary information from images acquired with different modalities. Most existing methods require computation of the joint histogram of the images, while some perform joint segmentation and registration in alternate iterations. In this work, we introduce a new non-information-theoretical method for pairwise multimodal image registration, in which the error of segmentation - using both images - is considered as the registration cost function. We empirically evaluate our method via rigid registration of multi-contrast brain magnetic resonance images, and demonstrate an often higher registration accuracy in the results produced by the proposed technique, compared to those by several existing methods.

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.

Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks.

PubMed

Demirhan, Ayşe; Toru, Mustafa; Guler, Inan

2015-07-01

Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2017-02-01

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Techniques on semiautomatic segmentation using the Adobe Photoshop

NASA Astrophysics Data System (ADS)

Park, Jin Seo; Chung, Min Suk; Hwang, Sung Bae

2005-04-01

The purpose of this research is to enable anybody to semiautomatically segment the anatomical structures in the MRIs, CTs, and other medical images on the personal computer. The segmented images are used for making three-dimensional images, which are helpful in medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was MR scanned to make 557 MRIs, which were transferred to a personal computer. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL; successively, manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a likewise manner, 11 anatomical structures in the 8,500 anatomcial images were segmented. Also, 12 brain and 10 heart anatomical structures in anatomical images were segmented. Proper segmentation was verified by making and examining the coronal, sagittal, and three-dimensional images from the segmented images. During semiautomatic segmentation on Adobe Photoshop, suitable algorithm could be used, the extent of automatization could be regulated, convenient user interface could be used, and software bugs rarely occurred. The techniques of semiautomatic segmentation using Adobe Photoshop are expected to be widely used for segmentation of the anatomical structures in various medical images.

Consistent cortical reconstruction and multi-atlas brain segmentation.

PubMed

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

2016-09-01

Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. 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. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely available in open source. Copyright © 2016 Elsevier Inc. All rights reserved.

Development of representative magnetic resonance imaging-based atlases of the canine brain and evaluation of three methods for atlas-based segmentation.

PubMed

Milne, Marjorie E; Steward, Christopher; Firestone, Simon M; Long, Sam N; O'Brien, Terrence J; Moffat, Bradford A

2016-04-01

To develop representative MRI atlases of the canine brain and to evaluate 3 methods of atlas-based segmentation (ABS). 62 dogs without clinical signs of epilepsy and without MRI evidence of structural brain disease. The MRI scans from 44 dogs were used to develop 4 templates on the basis of brain shape (brachycephalic, mesaticephalic, dolichocephalic, and combined mesaticephalic and dolichocephalic). Atlas labels were generated by segmenting the brain, ventricular system, hippocampal formation, and caudate nuclei. The MRI scans from the remaining 18 dogs were used to evaluate 3 methods of ABS (manual brain extraction and application of a brain shape-specific template [A], automatic brain extraction and application of a brain shape-specific template [B], and manual brain extraction and application of a combined template [C]). The performance of each ABS method was compared by calculation of the Dice and Jaccard coefficients, with manual segmentation used as the gold standard. Method A had the highest mean Jaccard coefficient and was the most accurate ABS method assessed. Measures of overlap for ABS methods that used manual brain extraction (A and C) ranged from 0.75 to 0.95 and compared favorably with repeated measures of overlap for manual extraction, which ranged from 0.88 to 0.97. Atlas-based segmentation was an accurate and repeatable method for segmentation of canine brain structures. It could be performed more rapidly than manual segmentation, which should allow the application of computer-assisted volumetry to large data sets and clinical cases and facilitate neuroimaging research and disease diagnosis.

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

PubMed

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

2013-08-15

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

MRI brain tumor segmentation based on improved fuzzy c-means method

NASA Astrophysics Data System (ADS)

Deng, Wankai; Xiao, Wei; Pan, Chao; Liu, Jianguo

2009-10-01

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

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

PubMed

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

2016-07-01

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

Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain.

PubMed

Ali, Anjum A; Dale, Anders M; Badea, Alexandra; Johnson, G Allan

2005-08-15

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.

Segmentation of solid subregion of high grade gliomas in MRI images based on active contour model (ACM)

NASA Astrophysics Data System (ADS)

Seow, P.; Win, M. T.; Wong, J. H. D.; Abdullah, N. A.; Ramli, N.

2016-03-01

Gliomas are tumours arising from the interstitial tissue of the brain which are heterogeneous, infiltrative and possess ill-defined borders. Tumour subregions (e.g. solid enhancing part, edema and necrosis) are often used for tumour characterisation. Tumour demarcation into substructures facilitates glioma staging and provides essential information. Manual segmentation had several drawbacks that include laborious, time consuming, subjected to intra and inter-rater variability and hindered by diversity in the appearance of tumour tissues. In this work, active contour model (ACM) was used to segment the solid enhancing subregion of the tumour. 2D brain image acquisition data using 3T MRI fast spoiled gradient echo sequence in post gadolinium of four histologically proven high-grade glioma patients were obtained. Preprocessing of the images which includes subtraction and skull stripping were performed and then followed by ACM segmentation. The results of the automatic segmentation method were compared against the manual delineation of the tumour by a trainee radiologist. Both results were further validated by an experienced neuroradiologist and a brief quantitative evaluations (pixel area and difference ratio) were performed. Preliminary results of the clinical data showed the potential of ACM model in the application of fast and large scale tumour segmentation in medical imaging.

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

PubMed

Charron, Odelin; Lallement, Alex; Jarnet, Delphine; Noblet, Vincent; Clavier, Jean-Baptiste; Meyer, Philippe

2018-04-01

Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

PubMed

Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

2014-09-01

Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. Copyright © 2014 Elsevier Inc. All rights reserved.

Magnetic Resonance–Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region

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

Zheng, Weili; Kim, Joshua P.; Kadbi, Mo

2015-11-01

Purpose: To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Methods and Materials: Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessedmore » by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. Results: On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone–air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. Conclusions: A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated into our synCT pipeline for brain, and results agreed well with clinical CTs, thereby supporting MR-only radiation therapy treatment planning in the brain.« less

Magnetic Resonance-Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region.

PubMed

Zheng, Weili; Kim, Joshua P; Kadbi, Mo; Movsas, Benjamin; Chetty, Indrin J; Glide-Hurst, Carri K

2015-11-01

To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone-air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated into our synCT pipeline for brain, and results agreed well with clinical CTs, thereby supporting MR-only radiation therapy treatment planning in the brain. Copyright © 2015 Elsevier Inc. All rights reserved.

A hybrid approach of using symmetry technique for brain tumor segmentation.

PubMed

Saddique, Mubbashar; Kazmi, Jawad Haider; Qureshi, Kalim

2014-01-01

Tumor and related abnormalities are a major cause of disability and death worldwide. Magnetic resonance imaging (MRI) is a superior modality due to its noninvasiveness and high quality images of both the soft tissues and bones. In this paper we present two hybrid segmentation techniques and their results are compared with well-recognized techniques in this area. The first technique is based on symmetry and we call it a hybrid algorithm using symmetry and active contour (HASA). In HASA, we take refection image, calculate the difference image, and then apply the active contour on the difference image to segment the tumor. To avoid unimportant segmented regions, we improve the results by proposing an enhancement in the form of the second technique, EHASA. In EHASA, we also take reflection of the original image, calculate the difference image, and then change this image into a binary image. This binary image is mapped onto the original image followed by the application of active contouring to segment the tumor region.

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network

PubMed Central

Mao, Lei; Liu, Chang; Xiong, Shuyu

2018-01-01

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice. PMID:29755716

Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Lindner, Dirk; Arlt, Felix; Ituna-Yudonago, Jean Fulbert; Chalopin, Claire

2018-03-01

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS. A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented. Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods. The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism.

PubMed

Wang, Li; Li, Gang; Adeli, Ehsan; Liu, Mingxia; Wu, Zhengwang; Meng, Yu; Lin, Weili; Shen, Dinggang

2018-06-01

Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness. © 2018 Wiley Periodicals, Inc.

A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

PubMed Central

Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

2013-01-01

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index1

PubMed Central

Zou, Kelly H.; Warfield, Simon K.; Bharatha, Aditya; Tempany, Clare M.C.; Kaus, Michael R.; Haker, Steven J.; Wells, William M.; Jolesz, Ferenc A.; Kikinis, Ron

2005-01-01

Rationale and Objectives To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy. Materials and Methods The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA). Results Example 1: The mean DSCs of 0.883 (range, 0.876–0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819–0.852) with 0.5T intraoperative MRI (P < .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519–0.893), astrocytomas (0.487–0.972), and other mixed gliomas (0.490–0.899). Conclusion The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks. PMID:14974593

Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

PubMed Central

Ma, Da; Cardoso, Manuel J.; Modat, Marc; Powell, Nick; Wells, Jack; Holmes, Holly; Wiseman, Frances; Tybulewicz, Victor; Fisher, Elizabeth; Lythgoe, Mark F.; Ourselin, Sébastien

2014-01-01

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework. PMID:24475148

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 accurate metabolic activity of the grey matter can be calculated using the novel segmentation technique that we applied to MRI. By combining these quantitative data with those generated from F-FDG-PET images we were able to calculate the accurate metabolic activity of the grey matter. These types of measurements will be of great value in accurate analysis of the data from patients with neuropsychiatric disorders.

Toward real-time tumor margin identification in image-guided robotic brain tumor resection

NASA Astrophysics Data System (ADS)

Hu, Danying; Jiang, Yang; Belykh, Evgenii; Gong, Yuanzheng; Preul, Mark C.; Hannaford, Blake; Seibel, Eric J.

2017-03-01

For patients with malignant brain tumors (glioblastomas), a safe maximal resection of tumor is critical for an increased survival rate. However, complete resection of the cancer is hard to achieve due to the invasive nature of these tumors, where the margins of the tumors become blurred from frank tumor to more normal brain tissue, but in which single cells or clusters of malignant cells may have invaded. Recent developments in fluorescence imaging techniques have shown great potential for improved surgical outcomes by providing surgeons intraoperative contrast-enhanced visual information of tumor in neurosurgery. The current near-infrared (NIR) fluorophores, such as indocyanine green (ICG), cyanine5.5 (Cy5.5), 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX), are showing clinical potential to be useful in targeting and guiding resections of such tumors. Real-time tumor margin identification in NIR imaging could be helpful to both surgeons and patients by reducing the operation time and space required by other imaging modalities such as intraoperative MRI, and has the potential to integrate with robotically assisted surgery. In this paper, a segmentation method based on the Chan-Vese model was developed for identifying the tumor boundaries in an ex-vivo mouse brain from relatively noisy fluorescence images acquired by a multimodal scanning fiber endoscope (mmSFE). Tumor contours were achieved iteratively by minimizing an energy function formed by a level set function and the segmentation model. Quantitative segmentation metrics based on tumor-to-background (T/B) ratio were evaluated. Results demonstrated feasibility in detecting the brain tumor margins at quasi-real-time and has the potential to yield improved precision brain tumor resection techniques or even robotic interventions in the future.

Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3D segmentation algorithms

PubMed Central

2011-01-01

Background Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods. Results We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation. Conclusions We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss/. PMID:21668958

Clinical evaluation of single-shot and readout-segmented diffusion-weighted imaging in stroke patients at 3 T.

PubMed

Morelli, John; Porter, David; Ai, Fei; Gerdes, Clint; Saettele, Megan; Feiweier, Thorsten; Padua, Abraham; Dix, James; Marra, Michael; Rangaswamy, Rajesh; Runge, Val

2013-04-01

Diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) is most commonly performed utilizing a single-shot echo-planar imaging technique (ss-EPI). Susceptibility artifact and image blur are severe when this sequence is utilized at 3 T. To evaluate a readout-segmented approach to DWI MR in comparison with single-shot echo planar imaging for brain MRI. Eleven healthy volunteers and 14 patients with acute and early subacute infarctions underwent DWI MR examinations at 1.5 and 3T with ss-EPI and readout-segmented echo-planar (rs-EPI) DWI at equal nominal spatial resolutions. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations were made, and two blinded readers ranked the scans in terms of high signal intensity bulk susceptibility artifact, spatial distortions, image blur, overall preference, and motion artifact. SNR and CNR were greatest with rs-EPI (8.1 ± 0.2 SNR vs. 6.0 ± 0.2; P <10(-4) at 3T). Spatial distortions were greater with single-shot (0.23 ± 0.03 at 3T; P <0.001) than with rs-EPI (0.12 ± 0.02 at 3T). Combined with blur and artifact reduction, this resulted in a qualitative preference for the readout-segmented scans overall. Substantial image quality improvements are possible with readout-segmented vs. single-shot EPI - the current clinical standard for DWI - regardless of field strength (1.5 or 3 T). This results in improved image quality secondary to greater real spatial resolution and reduced artifacts from susceptibility in MR imaging of the brain.

Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing.

PubMed

Hsieh, Thomas M; Liu, Yi-Min; Liao, Chun-Chih; Xiao, Furen; Chiang, I-Jen; Wong, Jau-Min

2011-08-26

In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system. The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.

A comparative analysis of the dependences of the hemodynamic parameters on changes in ROI's position in perfusion CT scans

NASA Astrophysics Data System (ADS)

Choi, Yong-Seok; Cho, Jae-Hwan; Namgung, Jang-Sun; Kim, Hyo-Jin; Yoon, Dae-Young; Lee, Han-Joo

2013-05-01

This study performed a comparative analysis of cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and mean time-to-peak (TTP) obtained by changing the region of interest's (ROI) anatomical positions, during CT brain perfusion. We acquired axial source images of perfusion CT from 20 patients undergoing CT perfusion exams due to brain trauma. Subsequently, the CBV, CBF, MTT, and TTP values were calculated through data-processing of the perfusion CT images. The color scales for the CBV, CBF, MTT, and TTP maps were obtained using the image data. Anterior cerebral artery (ACA) was taken as the standard ROI for the calculations of the perfusion values. Differences in the hemodynamic average values were compared in a quantitative analysis by placing ROI and the dividing axial images into proximal, middle, and distal segments anatomically. By performing the qualitative analysis using a blind test, we observed changes in the sensory characteristics by using the color scales of the CBV, CBF, and MTT maps in the proximal, middle, and distal segments. According to the qualitative analysis, no differences were found in CBV, CBF, MTT, and TTP values of the proximal, middle, and distal segments and no changes were detected in the color scales of the the CBV, CBF, MTT, and TTP maps in the proximal, middle, and distal segments. We anticipate that the results of the study will useful in assessing brain trauma patients using by perfusion imaging.

MRI segmentation using dialectical optimization.

PubMed

dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E

2009-01-01

Biology, Psychology and Social Sciences are intrinsically connected to the very roots of the development of algorithms and methods in Computational Intelligence, as it is easily seen in approaches like genetic algorithms, evolutionary programming and particle swarm optimization. In this work we propose a new optimization method based on dialectics using fuzzy membership functions to model the influence of interactions between integrating poles in the status of each pole. Poles are the basic units composing dialectical systems. In order to validate our proposal we designed a segmentation method based on the optimization of k-means using dialectics for the segmentation of MR images. As a case study we used 181 MR synthetic multispectral images composed by proton density, T(1)- and T(2)-weighted synthetic brain images of 181 slices with 1 mm, resolution of 1 mm(3), for a normal brain and a noiseless MR tomographic system without field inhomogeneities, amounting a total of 543 images, generated by the simulator BrainWeb [2]. Our principal target here is comparing our proposal to k-means, fuzzy c-means, and Kohonen's self-organized maps, concerning the quantization error, we proved that our method can improved results obtained using k-means.

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation.

PubMed

al-Rifaie, Mohammad Majid; Aber, Ahmed; Hemanth, Duraiswamy Jude

2015-12-01

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

MO-F-CAMPUS-J-04: Tissue Segmentation-Based MR Electron Density Mapping Method for MR-Only Radiation Treatment Planning of Brain

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

Yu, H; Lee, Y; Ruschin, M

2015-06-15

Purpose: Automatically derive electron density of tissues using MR images and generate a pseudo-CT for MR-only treatment planning of brain tumours. Methods: 20 stereotactic radiosurgery (SRS) patients’ T1-weighted MR images and CT images were retrospectively acquired. First, a semi-automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution.more » Next, soft tissues - muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo-CT by linearly fitting their relative intensity histograms. Co-registered CT was used as a ground truth. The bone segmentations of pseudo-CT were compared with those of co-registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo-CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated. Results: The average of distances between voxels on external skull from pseudo-CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively. Conclusion: The semi-automated MR electron density mapping technique was developed using T1-weighted MR images. The generated pseudo-CT is comparable to that of CT in terms of anatomical position of tissues and similarity of electron density assignment. This method can allow MR-only treatment planning.« less

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI

NASA Astrophysics Data System (ADS)

Pei, Linmin; Reza, Syed M. S.; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M.

2017-03-01

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI.

PubMed

Pei, Linmin; Reza, Syed M S; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M

2017-02-11

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

PubMed Central

Wang, Jiahui; Vachet, Clement; Rumple, Ashley; Gouttard, Sylvain; Ouziel, Clémentine; Perrot, Emilie; Du, Guangwei; Huang, Xuemei; Gerig, Guido; Styner, Martin

2014-01-01

Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual “atlases” that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique. We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation. The multi-atlas-based AutoSeg has been evaluated on subcortical structure segmentation with a testing dataset of 20 adult brain MRI scans and 15 atlas MRI scans. The AutoSeg achieved mean Dice coefficients of 81.73% for the subcortical structures. PMID:24567717

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning.

PubMed

Rundo, Leonardo; Stefano, Alessandro; Militello, Carmelo; Russo, Giorgio; Sabini, Maria Gabriella; D'Arrigo, Corrado; Marletta, Francesco; Ippolito, Massimo; Mauri, Giancarlo; Vitabile, Salvatore; Gilardi, Maria Carla

2017-06-01

Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [ 11 C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife ® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTV MRI . A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians. The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.87 ± 14.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTV MRI enhanced the CTV more accurately than BTV in 25% of cases. The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTV MRI and GTV should be considered for a comprehensive treatment planning. Copyright © 2017 Elsevier B.V. All rights reserved.

Impaired diffusion tensor imaging findings in the corpus callosum and cingulum may underlie impaired learning and memory abilities in systemic lupus erythematosus.

PubMed

Shapira-Lichter, I; Weinstein, M; Lustgarten, N; Ash, E; Litinsky, I; Aloush, V; Anouk, M; Caspi, D; Hendler, T; Paran, D

2016-10-01

Memory impairment is prevalent in systemic lupus erythematosus (SLE); however, its pathogenesis is unknown. In a previous functional magnetic resonance imaging (fMRI) study we demonstrated altered brain activity dynamics and less brain deactivation in patients with SLE as compared with healthy controls, when performing a learning and memory task. Our findings localized this impairment to the default mode network (DMN), and particularly to its anterior medial prefrontal cortex node. In addition, altered networking of the hippocampal subsystem of the DMN was seen in patients with SLE when performing this task, as well as atrophy of the left hippocampus. The present study aimed to search for a structural substrate for the altered recruitment pattern observed in fMRI studies using diffusion tensor imaging (DTI). Using DTI, we characterized brain diffusivity in 10 patients with SLE and nine healthy controls. Two tracts associated with the DMN were reconstructed: the corpus callosum (CC) and the cingulum bundle. The CC was segmented according to the Witelson segmentation scheme and the cingulum was segmented into superior and descending bundles. A significant increase in mean diffusivity (MD) was seen in patients with SLE without neuropsychiatric SLE (NPSLE) as compared with healthy controls in all five segments of the CC (segment 1: p = 0.043; segment 2: p = 0.005; segment 3: p = 0.003; segment 4: p = 0.012; segment 5: p = 0.023) as well as in the descending portion of the left cingulum bundle (p = 0.026). Increased MD values in the CC and the left cingulum may indicate impaired organization/reduced integrity of these tracts, which may underlie the abnormal pattern of brain activity recruitment of the DMN observed during a verbal learning and memory task. Taking into account the central role of the left hippocampus in verbal memory, the abnormal integrity of the left cingulum may contribute to the reduced performance of patients with SLE on verbal memory tasks. © The Author(s) 2016.

SU-F-J-220: Micro-CT Based Quantification of Mouse Brain Vasculature: The Effects of Acquisition Technique and Contrast Material

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

Tipton, C; Lamba, M; Qi, Z

Purpose: Cognitive impairment from radiation therapy to the brain may be linked to the loss of total blood volume in the brain. To account for brain injury, it is crucial to develop an understanding of blood volume loss as a result of radiation therapy. This study investigates µCT based quantification of mouse brain vasculature, focusing on the effect of acquisition technique and contrast material. Methods: Four mice were scanned on a µCT scanner (Siemens Inveon). The reconstructed voxel size was 18µm3 and all protocols were Hounsfield Unit (HU) calibrated. The mice were injected with 40mg of gold nanoparticles (MediLumine) ormore » 100µl of Exitron 12000 (Miltenyi Biotec). Two acquisition techniques were also performed. A single kVp technique scanned the mouse once using an x-ray beam of 80kVp and segmentation was completed based on a threshold of HU values. The dual kVp technique scanned the mouse twice using 50kVp and 80kVp, this segmentation was based on the ratio of the HU value of the two kVps. After image reconstruction and segmentation, the brain blood volume was determined as a percentage of the total brain volume. Results: For the single kVp acquisition at 80kVp, the brain blood volume had an average of 3.5% for gold and 4.0% for Exitron 12000. Also at 80kVp, the contrast-noise ratio was significantly better for images acquired with the gold nanoparticles (2.0) than for those acquired with the Exitron 12000 (1.4). The dual kVp acquisition shows improved separation of skull from vasculature, but increased image noise. Conclusion: In summary, the effects of acquisition technique and contrast material for quantification of mouse brain vasculature showed that gold nanoparticles produced more consistent segmentation of brain vasculature than Exitron 12000. Also, dual kVp acquisition may improve the accuracy of brain vasculature quantification, although the effect of noise amplification warrants further study.« less

Multiprotocol MR image segmentation in multiple sclerosis: experience with over 1000 studies

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Nyul, Laszlo G.; Ge, Yulin; Grossman, Robert I.

2000-06-01

Multiple Sclerosis (MS) is an acquired disease of the central nervous system. Subjective cognitive and ambulatory test scores on a scale called EDSS are currently utilized to assess the disease severity. Various MRI protocols are being investigated to study the disease based on how it manifests itself in the images. In an attempt to eventually replace EDSS by an objective measure to assess the natural course of the disease and its response to therapy, we have developed image segmentation methods based on fuzzy connectedness to quantify various objects in multiprotocol MRI. These include the macroscopic objects such as lesions, the gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and brain parenchyma as well as the microscopic aspects of the diseased WM. Over 1000 studies have been processed to date. By far the strongest correlations with the clinical measures were demonstrated by the Magnetization Transfer Ratio (MTR) histogram parameters obtained for the various segmented tissue regions emphasizing the importance of considering the microscopic/diffused nature of the disease in the individual tissue regions. Brain parenchymal volume also demonstrated a strong correlation with the clinical measures indicating that brain atrophy is an important indicator of the disease. Fuzzy connectedness is a viable segmentation method for studying MS.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

PubMed

Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

PubMed Central

Yang, Zhang; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

Spatio-Temporal Video Segmentation with Shape Growth or Shrinkage Constraint

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Charpiat, Guillaume; Brucker, Ludovic; Menze, Bjoern H.

2014-01-01

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

Segmentation of Image Ensembles via Latent Atlases

PubMed Central

Van Leemput, Koen; Menze, Bjoern H.; Wells, William M.; Golland, Polina

2010-01-01

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented. PMID:20580305

Automatic morphometry in Alzheimer's disease and mild cognitive impairment☆☆☆

PubMed Central

Heckemann, Rolf A.; Keihaninejad, Shiva; Aljabar, Paul; Gray, Katherine R.; Nielsen, Casper; Rueckert, Daniel; Hajnal, Joseph V.; Hammers, Alexander

2011-01-01

This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline images (1.5 T and 3 T) have been processed with the multi-atlas based MAPER procedure, resulting in labels for 83 regions covering the whole brain in 816 subjects. Selected segmentations were subjected to visual assessment. The segmentations are self-consistent, as evidenced by strong agreement between segmentations of paired images acquired at different field strengths (Jaccard coefficient: 0.802 ± 0.0146). Morphometric comparisons between diagnostic groups (normal; stable mild cognitive impairment; mild cognitive impairment with progression to Alzheimer's disease; Alzheimer's disease) showed highly significant group differences for individual regions, the majority of which were located in the temporal lobe. Additionally, significant effects were seen in the parietal lobe. Increased left/right asymmetry was found in posterior cortical regions. An automatically derived white-matter hypointensities index was found to be a suitable means of quantifying white-matter disease. This repository of segmentations is a potentially valuable resource to researchers working with ADNI data. PMID:21397703

Brain vascular image segmentation based on fuzzy local information C-means clustering

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie

2017-02-01

Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.

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.

Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

PubMed

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

Automated diagnosis of Alzheimer's disease with multi-atlas based whole brain segmentations

NASA Astrophysics Data System (ADS)

Luo, Yuan; Tang, Xiaoying

2017-03-01

Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.

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 derived gold standards (mean Dice's Kappa > 0.79 and Euclidean distance <1.3 mm between centroids). Using this method, we demonstrate that the average volume of the hippocampus is significantly different (p < 0.0001) in early-in-life (621.8 mm(3)) and term-equivalent age (958.8 mm(3)). Using these differences, we generalize the hippocampal growth rate to 38.3 ± 11.7 mm(3)/week and 40.5 ± 12.9 mm(3)/week for the left and right hippocampi respectively. Not surprisingly, younger gestational age at birth is associated with smaller volumes of the hippocampi (p = 0.001). MAGeT-Brain is capable of segmenting hippocampi accurately in preterm neonates, even at early-in-life. Hippocampal asymmetry with a larger right side is demonstrated on early-in-life images, suggesting that this phenomenon has its onset in the 3rd trimester of gestation. Hippocampal volume assessed at the time of early-in-life and term-equivalent age is linearly associated with GA at birth, whereby smaller volumes are associated with earlier birth.

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 in comparison to manually derived gold standards (mean Dice's Kappa > 0.79 and Euclidean distance <1.3 mm between centroids). Using this method, we demonstrate that the average volume of the hippocampus is significantly different (p < 0.0001) in early-in-life (621.8 mm3) and term-equivalent age (958.8 mm3). Using these differences, we generalize the hippocampal growth rate to 38.3 ± 11.7 mm3/week and 40.5 ± 12.9 mm3/week for the left and right hippocampi respectively. Not surprisingly, younger gestational age at birth is associated with smaller volumes of the hippocampi (p = 0.001). Conclusions MAGeT-Brain is capable of segmenting hippocampi accurately in preterm neonates, even at early-in-life. Hippocampal asymmetry with a larger right side is demonstrated on early-in-life images, suggesting that this phenomenon has its onset in the 3rd trimester of gestation. Hippocampal volume assessed at the time of early-in-life and term-equivalent age is linearly associated with GA at birth, whereby smaller volumes are associated with earlier birth. PMID:26740912

Label fusion based brain MR image segmentation via a latent selective model

NASA Astrophysics Data System (ADS)

Liu, Gang; Guo, Xiantang; Zhu, Kai; Liao, Hengxu

2018-04-01

Multi-atlas segmentation is an effective approach and increasingly popular for automatically labeling objects of interest in medical images. Recently, segmentation methods based on generative models and patch-based techniques have become the two principal branches of label fusion. However, these generative models and patch-based techniques are only loosely related, and the requirement for higher accuracy, faster segmentation, and robustness is always a great challenge. In this paper, we propose novel algorithm that combines the two branches using global weighted fusion strategy based on a patch latent selective model to perform segmentation of specific anatomical structures for human brain magnetic resonance (MR) images. In establishing this probabilistic model of label fusion between the target patch and patch dictionary, we explored the Kronecker delta function in the label prior, which is more suitable than other models, and designed a latent selective model as a membership prior to determine from which training patch the intensity and label of the target patch are generated at each spatial location. Because the image background is an equally important factor for segmentation, it is analyzed in label fusion procedure and we regard it as an isolated label to keep the same privilege between the background and the regions of interest. During label fusion with the global weighted fusion scheme, we use Bayesian inference and expectation maximization algorithm to estimate the labels of the target scan to produce the segmentation map. Experimental results indicate that the proposed algorithm is more accurate and robust than the other segmentation methods.

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.

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.

Automated methods for hippocampus segmentation: the evolution and a review of the state of the art.

PubMed

Dill, Vanderson; Franco, Alexandre Rosa; Pinho, Márcio Sarroglia

2015-04-01

The segmentation of the hippocampus in Magnetic Resonance Imaging (MRI) has been an important procedure to diagnose and monitor several clinical situations. The precise delineation of the borders of this brain structure makes it possible to obtain a measure of the volume and estimate its shape, which can be used to diagnose some diseases, such as Alzheimer's disease, schizophrenia and epilepsy. As the manual segmentation procedure in three-dimensional images is highly time consuming and the reproducibility is low, automated methods introduce substantial gains. On the other hand, the implementation of those methods is a challenge because of the low contrast of this structure in relation to the neighboring areas of the brain. Within this context, this research presents a review of the evolution of automatized methods for the segmentation of the hippocampus in MRI. Many proposed methods for segmentation of the hippocampus have been published in leading journals in the medical image processing area. This paper describes these methods presenting the techniques used and quantitatively comparing the methods based on Dice Similarity Coefficient. Finally, we present an evaluation of those methods considering the degree of user intervention, computational cost, segmentation accuracy and feasibility of application in a clinical routine.

Anatomo-clinical overlapping maps (AnaCOM): a new method to create anatomo-functional maps from neuropsychological tests and structural MRI scan of subjects with brain lesions

NASA Astrophysics Data System (ADS)

Kinkingnehun, Serge R. J.; du Boisgueheneuc, Foucaud; Golmard, Jean-Louis; Zhang, Sandy X.; Levy, Richard; Dubois, Bruno

2004-04-01

We have developed a new technique to analyze correlations between brain anatomy and its neurological functions. The technique is based on the anatomic MRI of patients with brain lesions who are administered neuropsychological tests. Brain lesions of the MRI scans are first manually segmented. The MRI volumes are then normalized to a reference map, using the segmented area as a mask. After normalization, the brain lesions of the MRI are segmented again in order to redefine the border of the lesions in the context of the normalized brain. Once the MRI is segmented, the patient's score on the neuropsychological test is assigned to each voxel in the lesioned area, while the rest of the voxels of the image are set to 0. Subsequently, the individual patient's MRI images are superimposed, and each voxel is reassigned the average score of the patients who have a lesion at that voxel. A threshold is applied to remove regions having less than three overlaps. This process leads to an anatomo-functional map that links brain areas to functional loss. Other maps can be created to aid in analyzing the functional maps, such as one that indicates the 95% confidence interval of the averaged scores for each area. This anatomo-clinical overlapping map (AnaCOM) method was used to obtain functional maps from patients with lesions in the superior frontal gyrus. By finding particular subregions more responsible for a particular deficit, this method can generate new hypotheses to be tested by conventional group methods.

Transfer learning improves supervised image segmentation across imaging protocols.

PubMed

van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

2015-05-01

The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization.

PubMed

Karayiannis, N B; Pai, P I

1999-02-01

This paper evaluates a segmentation technique for magnetic resonance (MR) images of the brain based on fuzzy algorithms for learning vector quantization (FALVQ). These algorithms perform vector quantization by updating all prototypes of a competitive network through an unsupervised learning process. Segmentation of MR images is formulated as an unsupervised vector quantization process, where the local values of different relaxation parameters form the feature vectors which are represented by a relatively small set of prototypes. The experiments evaluate a variety of FALVQ algorithms in terms of their ability to identify different tissues and discriminate between normal tissues and abnormalities.

Statistical shape (ASM) and appearance (AAM) models for the segmentation of the cerebellum in fetal ultrasound

NASA Astrophysics Data System (ADS)

Reyes López, Misael; Arámbula Cosío, Fernando

2017-11-01

The cerebellum is an important structure to determine the gestational age of the fetus, moreover most of the abnormalities it presents are related to growth disorders. In this work, we present the results of the segmentation of the fetal cerebellum applying statistical shape and appearance models. Both models were tested on ultrasound images of the fetal brain taken from 23 pregnant women, between 18 and 24 gestational weeks. The accuracy results obtained on 11 ultrasound images show a mean Hausdorff distance of 6.08 mm between the manual segmentation and the segmentation using active shape model, and a mean Hausdorff distance of 7.54 mm between the manual segmentation and the segmentation using active appearance model. The reported results demonstrate that the active shape model is more robust in the segmentation of the fetal cerebellum in ultrasound images.

Automatic brain tumor detection in MRI: methodology and statistical validation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

2005-04-01

Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

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

PubMed

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

2014-04-01

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

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 segmentation with established methods is performed. PMID:29672584

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.

Multifractal modeling, segmentation, prediction, and statistical validation of posterior fossa tumors

NASA Astrophysics Data System (ADS)

Islam, Atiq; Iftekharuddin, Khan M.; Ogg, Robert J.; Laningham, Fred H.; Sivakumar, Bhuvaneswari

2008-03-01

In this paper, we characterize the tumor texture in pediatric brain magnetic resonance images (MRIs) and exploit these features for automatic segmentation of posterior fossa (PF) tumors. We focus on PF tumor because of the prevalence of such tumor in pediatric patients. Due to varying appearance in MRI, we propose to model the tumor texture with a multi-fractal process, such as a multi-fractional Brownian motion (mBm). In mBm, the time-varying Holder exponent provides flexibility in modeling irregular tumor texture. We develop a detailed mathematical framework for mBm in two-dimension and propose a novel algorithm to estimate the multi-fractal structure of tissue texture in brain MRI based on wavelet coefficients. This wavelet based multi-fractal feature along with MR image intensity and a regular fractal feature obtained using our existing piecewise-triangular-prism-surface-area (PTPSA) method, are fused in segmenting PF tumor and non-tumor regions in brain T1, T2, and FLAIR MR images respectively. We also demonstrate a non-patient-specific automated tumor prediction scheme based on these image features. We experimentally show the tumor discriminating power of our novel multi-fractal texture along with intensity and fractal features in automated tumor segmentation and statistical prediction. To evaluate the performance of our tumor prediction scheme, we obtain ROCs and demonstrate how sharply the curves reach the specificity of 1.0 sacrificing minimal sensitivity. Experimental results show the effectiveness of our proposed techniques in automatic detection of PF tumors in pediatric MRIs.

Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine

NASA Astrophysics Data System (ADS)

Selva Bhuvaneswari, K.; Geetha, P.

2017-05-01

Magnetic resonance imaging segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumour detection techniques are presented in the literature. The entire segmentation process of our proposed work comprises three phases: threshold generation with dynamic modified region growing phase, texture feature generation phase and region merging phase. by dynamically changing two thresholds in the modified region growing approach, the first phase of the given input image can be performed as dynamic modified region growing process, in which the optimisation algorithm, firefly algorithm help to optimise the two thresholds in modified region growing. After obtaining the region growth segmented image using modified region growing, the edges can be detected with edge detection algorithm. In the second phase, the texture feature can be extracted using entropy-based operation from the input image. In region merging phase, the results obtained from the texture feature-generation phase are combined with the results of dynamic modified region growing phase and similar regions are merged using a distance comparison between regions. After identifying the abnormal tissues, the classification can be done by hybrid kernel-based SVM (Support Vector Machine). The performance analysis of the proposed method will be carried by K-cross fold validation method. The proposed method will be implemented in MATLAB with various images.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2012-01-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Neural network-based brain tissue segmentation in MR images using extracted features from intraframe coding in H.264

NASA Astrophysics Data System (ADS)

Jafari, Mehdi; Kasaei, Shohreh

2011-12-01

Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

PubMed

Wu, Wei; Chen, Albert Y C; Zhao, Liang; Corso, Jason J

2014-03-01

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using "structural knowledge" such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369-376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

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.

Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.

PubMed

Hu, Yu-Chi; Grossberg, Michael; Mageras, Gikas

2016-04-01

Volumetric medical images of a single subject can be acquired using different imaging modalities, such as computed tomography, magnetic resonance imaging (MRI), and positron emission tomography. In this work, we present a semiautomatic segmentation algorithm that can leverage the synergies between different image modalities while integrating interactive human guidance. The algorithm provides a statistical segmentation framework partly automating the segmentation task while still maintaining critical human oversight. The statistical models presented are trained interactively using simple brush strokes to indicate tumor and nontumor tissues and using intermediate results within a patient's image study. To accomplish the segmentation, we construct the energy function in the conditional random field (CRF) framework. For each slice, the energy function is set using the estimated probabilities from both user brush stroke data and prior approved segmented slices within a patient study. The progressive segmentation is obtained using a graph-cut-based minimization. Although no similar semiautomated algorithm is currently available, we evaluated our method with an MRI data set from Medical Image Computing and Computer Assisted Intervention Society multimodal brain segmentation challenge (BRATS 2012 and 2013) against a similar fully automatic method based on CRF and a semiautomatic method based on grow-cut, and our method shows superior performance.

MS lesion segmentation using a multi-channel patch-based approach with spatial consistency

NASA Astrophysics Data System (ADS)

Mechrez, Roey; Goldberger, Jacob; Greenspan, Hayit

2015-03-01

This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAIR). An MS lesion patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally a novel iterative patch-based label refinement process based on the initial segmentation map is performed to ensure spatial consistency of the detected lesions. A leave-one-out evaluation is done for each testing image in the MS lesion segmentation challenge of MICCAI 2008. Results are shown to compete with the state-of-the-art methods on the MICCAI 2008 challenge.

Hierarchical brain tissue segmentation and its application in multiple sclerosis and Alzheimer's disease

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Udupa, Jayaram K.; Moonis, Gul; Schwartz, Eric; Balcer, Laura

2005-04-01

Based on Fuzzy Connectedness (FC) object delineation principles and algorithms, a hierarchical brain tissue segmentation technique has been developed for MR images. After MR image background intensity inhomogeneity correction and intensity standardization, three FC objects for cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) are generated via FC object delineation, and an intracranial (IC) mask is created via morphological operations. Then, the IC mask is decomposed into parenchymal (BP) and CSF masks, while the BP mask is separated into WM and GM masks. WM mask is further divided into pure and dirty white matter masks (PWM and DWM). In Multiple Sclerosis studies, a severe white matter lesion (LS) mask is defined from DWM mask. Based on the segmented brain tissue images, a histogram-based method has been developed to find disease-specific, image-based quantitative markers for characterizing the macromolecular manifestation of the two diseases. These same procedures have been applied to 65 MS (46 patients and 19 normal subjects) and 25 AD (15 patients and 10 normal subjects) data sets, each of which consists of FSE PD- and T2-weighted MR images. Histograms representing standardized PD and T2 intensity distributions and their numerical parameters provide an effective means for characterizing the two diseases. The procedures are systematic, nearly automated, robust, and the results are reproducible.

Unsupervised MRI segmentation of brain tissues using a local linear model and level set.

PubMed

Rivest-Hénault, David; Cheriet, Mohamed

2011-02-01

Real-world magnetic resonance imaging of the brain is affected by intensity nonuniformity (INU) phenomena which makes it difficult to fully automate the segmentation process. This difficult task is accomplished in this work by using a new method with two original features: (1) each brain tissue class is locally modeled using a local linear region representative, which allows us to account for the INU in an implicit way and to more accurately position the region's boundaries; and (2) the region models are embedded in the level set framework, so that the spatial coherence of the segmentation can be controlled in a natural way. Our new method has been tested on the ground-truthed Internet Brain Segmentation Repository (IBSR) database and gave promising results, with Tanimoto indexes ranging from 0.61 to 0.79 for the classification of the white matter and from 0.72 to 0.84 for the gray matter. To our knowledge, this is the first time a region-based level set model has been used to perform the segmentation of real-world MRI brain scans with convincing results. Copyright © 2011 Elsevier Inc. All rights reserved.

Quantitative Analysis of Geometry and Lateral Symmetry of Proximal Middle Cerebral Artery.

PubMed

Peter, Roman; Emmer, Bart J; van Es, Adriaan C G M; van Walsum, Theo

2017-10-01

The purpose of our work is to quantitatively assess clinically relevant geometric properties of proximal middle cerebral arteries (pMCA), to investigate the degree of their lateral symmetry, and to evaluate whether the pMCA can be modeled by using state-of-the-art deformable image registration of the ipsi- and contralateral hemispheres. Individual pMCA segments were identified, quantified, and statistically evaluated on a set of 55 publicly available magnetic resonance angiography time-of-flight images. Rigid and deformable image registrations were used for geometric alignment of the ipsi- and contralateral hemispheres. Lateral symmetry of relevant geometric properties was evaluated before and after the image registration. No significant lateral differences regarding tortuosity and diameters of contralateral M1 segments of pMCA were identified. Regarding the length of M1 segment, 44% of all subjects could be considered laterally symmetrical. Dominant M2 segment was identified in 30% of men and 9% of women in both brain hemispheres. Deformable image registration performed significantly better (P < .01) than rigid registration with regard to distances between the ipsi- and the contralateral centerlines of M1 segments (1.5 ± 1.1 mm versus 2.8 ± 1.2 mm respectively) and between the M1 and the anterior cerebral artery (ACA) branching points (1.6 ± 1.4 mm after deformable registration). Although natural lateral variation of the length of M1 may not allow for sufficient modeling of the complete pMCA, deformable image registration of the contralateral brain hemisphere to the ipsilateral hemisphere is feasible for localization of ACA-M1 branching point and for modeling 71 ± 23% of M1 segment. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

PubMed

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

2016-12-01

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

PubMed Central

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

Preprocessing film-copied MRI for studying morphological brain changes.

PubMed

Pham, Tuan D; Eisenblätter, Uwe; Baune, Bernhard T; Berger, Klaus

2009-06-15

The magnetic resonance imaging (MRI) of the brain is one of the important data items for studying memory and morbidity in elderly as these images can provide useful information through the quantitative measures of various regions of interest of the brain. As an effort to fully automate the biomedical analysis of the brain that can be combined with the genetic data of the same human population and where the records of the original MRI data are missing, this paper presents two effective methods for addressing this imaging problem. The first method handles the restoration of the film-copied MRI. The second method involves the segmentation of the image data. Experimental results and comparisons with other methods suggest the usefulness of the proposed image analysis methodology.

Automatic falx cerebri and tentorium cerebelli segmentation from magnetic resonance images

NASA Astrophysics Data System (ADS)

Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L.; Butman, John A.; Prince, Jerry L.

2017-03-01

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Automatic falx cerebri and tentorium cerebelli segmentation from Magnetic Resonance Images.

PubMed

Glaister, Jeffrey; Carass, Aaron; Pham, Dzung L; Butman, John A; Prince, Jerry L

2017-02-01

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Graphical user interface to optimize image contrast parameters used in object segmentation - biomed 2009.

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2009-01-01

Image segmentation is the process of isolating distinct objects within an image. Computer algorithms have been developed to aid in the process of object segmentation, but a completely autonomous segmentation algorithm has yet to be developed [1]. This is because computers do not have the capability to understand images and recognize complex objects within the image. However, computer segmentation methods [2], requiring user input, have been developed to quickly segment objects in serial sectioned images, such as magnetic resonance images (MRI) and confocal laser scanning microscope (CLSM) images. In these cases, the segmentation process becomes a powerful tool in visualizing the 3D nature of an object. The user input is an important part of improving the performance of many segmentation methods. A double threshold segmentation method has been investigated [3] to separate objects in gray scaled images, where the gray level of the object is among the gray levels of the background. In order to best determine the threshold values for this segmentation method the image must be manipulated for optimal contrast. The same is true of other segmentation and edge detection methods as well. Typically, the better the image contrast, the better the segmentation results. This paper describes a graphical user interface (GUI) that allows the user to easily change image contrast parameters that will optimize the performance of subsequent object segmentation. This approach makes use of the fact that the human brain is extremely effective in object recognition and understanding. The GUI provides the user with the ability to define the gray scale range of the object of interest. These lower and upper bounds of this range are used in a histogram stretching process to improve image contrast. Also, the user can interactively modify the gamma correction factor that provides a non-linear distribution of gray scale values, while observing the corresponding changes to the image. This interactive approach gives the user the power to make optimal choices in the contrast enhancement parameters.

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 settings. PMID:26106562

Oscillatory network with self-organized dynamical connections for synchronization-based image segmentation.

PubMed

Kuzmina, Margarita; Manykin, Eduard; Surina, Irina

2004-01-01

An oscillatory network of columnar architecture located in 3D spatial lattice was recently designed by the authors as oscillatory model of the brain visual cortex. Single network oscillator is a relaxational neural oscillator with internal dynamics tunable by visual image characteristics - local brightness and elementary bar orientation. It is able to demonstrate either activity state (stable undamped oscillations) or "silence" (quickly damped oscillations). Self-organized nonlocal dynamical connections of oscillators depend on oscillator activity levels and orientations of cortical receptive fields. Network performance consists in transfer into a state of clusterized synchronization. At current stage grey-level image segmentation tasks are carried out by 2D oscillatory network, obtained as a limit version of the source model. Due to supplemented network coupling strength control the 2D reduced network provides synchronization-based image segmentation. New results on segmentation of brightness and texture images presented in the paper demonstrate accurate network performance and informative visualization of segmentation results, inherent in the model.

Validated Automatic Brain Extraction of Head CT Images

PubMed Central

Muschelli, John; Ullman, Natalie L.; Mould, W. Andrew; Vespa, Paul; Hanley, Daniel F.; Crainiceanu, Ciprian M.

2015-01-01

Background X-ray Computed Tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction – the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0 – 100 Hounsfield units (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ = 1mm3) and re-thresholded to 0 – 100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomatedICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and and an intraclass correlation (ICC) was estimated using a one-way ANOVA. Results Smoothing images improves brain extraction results using BET for all measures except specificity (all p < 0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p < 0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p < 0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p < 0.001) and lower specificity (0.9981 vs. 0.9982; median difference: −0.0001, p < 0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains. Conclusion BET performs well at brain extraction on thresholded, 1mm3 smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided. PMID:25862260

Effects of penetrating traumatic brain injury on event segmentation and memory.

PubMed

Zacks, Jeffrey M; Kurby, Christopher A; Landazabal, Claudia S; Krueger, Frank; Grafman, Jordan

2016-01-01

Penetrating traumatic brain injury (pTBI) is associated with deficits in cognitive tasks including comprehension and memory, and also with impairments in tasks of daily living. In naturalistic settings, one important component of cognitive task performance is event segmentation, the ability to parse the ongoing stream of behavior into meaningful units. Event segmentation ability is associated with memory performance and with action control, but is not well assessed by standard neuropsychological assessments or laboratory tasks. Here, we measured event segmentation and memory in a sample of 123 male military veterans aged 59-81 who had suffered a traumatic brain injury as young men, and 34 demographically similar controls. Participants watched movies of everyday activities and segmented them to identify fine-grained or coarse-grained events, and then completed tests of recognition memory for pictures from the movies and of memory for the temporal order of actions in the movies. Lesion location and volume were assessed with computed tomography (CT) imaging. Patients with traumatic brain injury were impaired on event segmentation. Those with larger lesions had larger impairments for fine segmentation and also impairments for both memory measures. Further, the degree of memory impairment was statistically mediated by the degree of event segmentation impairment. There was some evidence that lesions to the ventromedial prefrontal cortex (vmPFC) selectively impaired coarse segmentation; however, lesions outside of a priori regions of interest also were associated with impaired segmentation. One possibility is that the effect of vmPFC damage reflects the role of prefrontal event knowledge representations in ongoing comprehension. These results suggest that assessment of naturalistic event comprehension can be a valuable component of cognitive assessment in cases of traumatic brain injury, and that interventions aimed at event segmentation could be clinically helpful. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automatically tracking neurons in a moving and deforming brain

PubMed Central

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

2017-01-01

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

Automatically tracking neurons in a moving and deforming brain.

PubMed

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

2017-05-01

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

Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images

PubMed Central

Du, Jia; Younes, Laurent; Qiu, Anqi

2011-01-01

This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are simultaneously carried from one subject to another through a flow of diffeomorphisms. To the best of our knowledge, this is the first time that the diffeomorphic metric from one brain to another is derived in a shape space of intensity images and point sets (such as curves and surfaces) in a unified manner. We describe the Euler–Lagrange equation associated with this algorithm with respect to momentum, a linear transformation of the velocity vector field of the diffeomorphic flow. The numerical implementation for solving this variational problem, which involves large-scale kernel convolution in an irregular grid, is made feasible by introducing a class of computationally friendly kernels. We apply this algorithm to align magnetic resonance brain data. Our whole brain mapping results show that our algorithm outperforms the image-based LDDMM algorithm in terms of the mapping accuracy of gyral/sulcal curves, sulcal regions, and cortical and subcortical segmentation. Moreover, our algorithm provides better whole brain alignment than combined volumetric and surface registration (Postelnicu et al., 2009) and hierarchical attribute matching mechanism for elastic registration (HAMMER) (Shen and Davatzikos, 2002) in terms of cortical and subcortical volume segmentation. PMID:21281722

Automatic segmentation of MR brain images of preterm infants using supervised classification.

PubMed

Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

2015-09-01

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data from the partially annotated images resulted in slightly lower Dice coefficients, the performance in all experiments was close to that of a second human expert (0.93 for WM, 0.79 for GM and 0.86 for CSF for the images acquired at 30weeks, and 0.94 for WM, 0.76 for GM and 0.87 for CSF for the images acquired at 40weeks). These results show that the presented method is robust to age and acquisition protocol and that it performs accurate segmentation of WM, GM, and CSF when the training data is extracted from complete annotations as well as when the training data is extracted from partial annotations only. This extends the applicability of the method by reducing the time and effort necessary to create training data in a population with different characteristics. Copyright © 2015 Elsevier Inc. All rights reserved.

Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

PubMed

Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

2017-11-01

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

A new method based on Dempster-Shafer theory and fuzzy c-means for brain MRI segmentation

NASA Astrophysics Data System (ADS)

Liu, Jie; Lu, Xi; Li, Yunpeng; Chen, Xiaowu; Deng, Yong

2015-10-01

In this paper, a new method is proposed to decrease sensitiveness to motion noise and uncertainty in magnetic resonance imaging (MRI) segmentation especially when only one brain image is available. The method is approached with considering spatial neighborhood information by fusing the information of pixels with their neighbors with Dempster-Shafer (DS) theory. The basic probability assignment (BPA) of each single hypothesis is obtained from the membership function of applying fuzzy c-means (FCM) clustering to the gray levels of the MRI. Then multiple hypotheses are generated according to the single hypothesis. Then we update the objective pixel’s BPA by fusing the BPA of the objective pixel and those of its neighbors to get the final result. Some examples in MRI segmentation are demonstrated at the end of the paper, in which our method is compared with some previous methods. The results show that the proposed method is more effective than other methods in motion-blurred MRI segmentation.

Diffusion Tensor Imaging Based Thalamic Segmentation in Deep Brain Stimulation for Chronic Pain Conditions

PubMed Central

Kim, Won; Chivukula, Srinivas; Hauptman, Jason; Pouratian, Nader

2016-01-01

Background/Aims Thalamic deep brain stimulation (DBS) for the treatment of medically refractory pain has largely been abandoned on account of its inconsistent and oftentimes poor efficacy. Our aim here was to use diffusion tensor imaging (DTI)-based segmentation to assess the internal thalamic nuclei of patients who have undergone thalamic DBS for intractable pain and retrospectively correlate lead position with clinical outcome. Methods DTI-based segmentation was performed on 5 patients who underwent sensory thalamus DBS for chronic pain. Postoperative computed tomography (CT) images obtained for electrode placement were fused with preoperative MRIs that had undergone DTI-based thalamic segmentation. Sensory thalamus maps of 4 patients were analyzed for lead positioning and interpatient variability. Results Four patients who experienced significant pain relief following DBS demonstrated contact positions within the DTI-determined sensory thalamus or in its vicinity, whereas one who did not respond to stimulation did not. Only four voxels (2%) within the sensory thalamus were mutually shared among patients; 108 voxels (58%) were uniquely represented. Conclusions DTI-based segmentation of the thalamus can be used to confirm thalamic lead placement relative to the sensory thalamus, and may serve as a useful tool to guide thalamic DBS electrode implantation in the future. PMID:27537848

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.

PubMed

Zhan, Tianming; Chen, Yi; Hong, Xunning; Lu, Zhenyu; Chen, Yunjie

2017-01-01

In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

NASA Astrophysics Data System (ADS)

Wang, Brian T.; Poirier, Stefan; Guo, Ting; Parrent, Andrew G.; Peters, Terry M.; Khan, Ali R.

2016-03-01

Purpose Deep brain stimulation (DBS) is a common treatment for Parkinson's disease (PD) and involves the use of brain atlases or intrinsic landmarks to estimate the location of target deep brain structures, such as the subthalamic nucleus (STN) and the globus pallidus pars interna (GPi). However, these structures can be difficult to localize with conventional clinical magnetic resonance imaging (MRI), and thus targeting can be prone to error. Ultra-high-field imaging at 7T has the ability to clearly resolve these structures and thus atlases built with these data have the potential to improve targeting accuracy. Methods T1 and T2-weighted images of 12 healthy control subjects were acquired using a 7T MR scanner. These images were then used with groupwise registration to generate an unbiased average template with T1w and T2w contrast. Deep brain structures were manually labelled in each subject by two raters and rater reliability was assessed. We compared the use of this unbiased atlas with two other methods of atlas-based segmentation (single-template and multi-template) for subthalamic nucleus (STN) segmentation on 7T MRI data. We also applied this atlas to clinical DBS data acquired at 1.5T to evaluate its efficacy for DBS target localization as compared to using a standard atlas. Results The unbiased templates provide superb detail of subcortical structures. Through one-way ANOVA tests, the unbiased template is significantly (p <0.05) more accurate than a single-template in atlas-based segmentation and DBS target localization tasks. Conclusion The generated unbiased averaged templates provide better visualization of deep brain nuclei and an increase in accuracy over single-template and lower field strength atlases.

Cone-beam CT image contrast and attenuation-map linearity improvement (CALI) for brain stereotactic radiosurgery procedures

NASA Astrophysics Data System (ADS)

Hashemi, Sayed Masoud; Lee, Young; Eriksson, Markus; Nordström, Hâkan; Mainprize, James; Grouza, Vladimir; Huynh, Christopher; Sahgal, Arjun; Song, William Y.; Ruschin, Mark

2017-03-01

A Contrast and Attenuation-map (CT-number) Linearity Improvement (CALI) framework is proposed for cone-beam CT (CBCT) images used for brain stereotactic radiosurgery (SRS). The proposed framework is used together with our high spatial resolution iterative reconstruction algorithm and is tailored for the Leksell Gamma Knife ICON (Elekta, Stockholm, Sweden). The incorporated CBCT system in ICON facilitates frameless SRS planning and treatment delivery. The ICON employs a half-cone geometry to accommodate the existing treatment couch. This geometry increases the amount of artifacts and together with other physical imperfections causes image inhomogeneity and contrast reduction. Our proposed framework includes a preprocessing step, involving a shading and beam-hardening artifact correction, and a post-processing step to correct the dome/capping artifact caused by the spatial variations in x-ray energy generated by bowtie-filter. Our shading correction algorithm relies solely on the acquired projection images (i.e. no prior information required) and utilizes filtered-back-projection (FBP) reconstructed images to generate a segmented bone and soft-tissue map. Ideal projections are estimated from the segmented images and a smoothed version of the difference between the ideal and measured projections is used in correction. The proposed beam-hardening and dome artifact corrections are segmentation free. The CALI was tested on CatPhan, as well as patient images acquired on the ICON system. The resulting clinical brain images show substantial improvements in soft contrast visibility, revealing structures such as ventricles and lesions which were otherwise un-detectable in FBP-reconstructed images. The linearity of the reconstructed attenuation-map was also improved, resulting in more accurate CT#.

Identifying Lesions on Structural Brain Images-Validation of the Method and Application to Neuropsychological Patients

ERIC Educational Resources Information Center

Stamatakis, E.A.; Tyler, L.K.

2005-01-01

The study of neuropsychological disorders has been greatly facilitated by the localization of brain lesions on MRI scans. Current popular approaches for the assessment of MRI brain scans mostly depend on the successful segmentation of the brain into grey and white matter. These methods cannot be used effectively with large lesions because lesions…

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

PubMed

Jones, Timothy L; Byrnes, Tiernan J; Yang, Guang; Howe, Franklyn A; Bell, B Anthony; Barrick, Thomas R

2015-03-01

There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

Reproducibility of Brain Morphometry from Short-Term Repeat Clinical MRI Examinations: A Retrospective Study

PubMed Central

Liu, Hon-Man; Chen, Shan-Kai; Chen, Ya-Fang; Lee, Chung-Wei; Yeh, Lee-Ren

2016-01-01

Purpose To assess the inter session reproducibility of automatic segmented MRI-derived measures by FreeSurfer in a group of subjects with normal-appearing MR images. Materials and Methods After retrospectively reviewing a brain MRI database from our institute consisting of 14,758 adults, those subjects who had repeat scans and had no history of neurodegenerative disorders were selected for morphometry analysis using FreeSurfer. A total of 34 subjects were grouped by MRI scanner model. After automatic segmentation using FreeSurfer, label-wise comparison (involving area, thickness, and volume) was performed on all segmented results. An intraclass correlation coefficient was used to estimate the agreement between sessions. Wilcoxon signed rank test was used to assess the population mean rank differences across sessions. Mean-difference analysis was used to evaluate the difference intervals across scanners. Absolute percent difference was used to estimate the reproducibility errors across the MRI models. Kruskal-Wallis test was used to determine the across-scanner effect. Results The agreement in segmentation results for area, volume, and thickness measurements of all segmented anatomical labels was generally higher in Signa Excite and Verio models when compared with Sonata and TrioTim models. There were significant rank differences found across sessions in some labels of different measures. Smaller difference intervals in global volume measurements were noted on images acquired by Signa Excite and Verio models. For some brain regions, significant MRI model effects were observed on certain segmentation results. Conclusions Short-term scan-rescan reliability of automatic brain MRI morphometry is feasible in the clinical setting. However, since repeatability of software performance is contingent on the reproducibility of the scanner performance, the scanner performance must be calibrated before conducting such studies or before using such software for retrospective reviewing. PMID:26812647

A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI.

PubMed

Wels, Michael; Carneiro, Gustavo; Aplas, Alexander; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2008-01-01

In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.

Age-Specific Dynamics of Corpus Callosum Development in Children and its Peculiarities in Infantile Cerebral Palsy.

PubMed

Krasnoshchekova, E I; Zykin, P A; Tkachenko, L A; Aleksandrov, T A; Sereda, V M; Yalfimov, A N

2016-10-01

The age dynamics of corpus callosum development was studied on magnetic resonance images of the brain in children aged 2-11 years without neurological abnormalities and with infantile cerebral palsy. The areas of the total corpus callosum and its segments are compared in the midsagittal images. Analysis is carried out with the use of an original formula: proportion of areas of the anterior (genu, CC2; and anterior part, CC3) and posterior (isthmus, CC6 and splenium, CC7) segments: kCC=(CC2+CC3)×CC6/CC7. The results characterize age-specific dynamics of the corpus callosum development and can be used for differentiation, with high confidence, of the brain of children without neurological abnormalities from the brain patients with infantile cerebral palsy.

Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

PubMed Central

Bjornsson, Christopher S; Lin, Gang; Al-Kofahi, Yousef; Narayanaswamy, Arunachalam; Smith, Karen L; Shain, William; Roysam, Badrinath

2009-01-01

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic ‘divide and conquer’ methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (~100 μm) slices of rat brain tissue were labeled using 3 – 5 fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81–92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system. PMID:18294697

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

NASA Astrophysics Data System (ADS)

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

2000-06-01

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

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

NASA Astrophysics Data System (ADS)

Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2016-03-01

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry.

PubMed

Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2016-03-22

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83-0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments.

Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

PubMed Central

Meier, Raphael; Knecht, Urspeter; Loosli, Tina; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

2016-01-01

Information about the size of a tumor and its temporal evolution is needed for diagnosis as well as treatment of brain tumor patients. The aim of the study was to investigate the potential of a fully-automatic segmentation method, called BraTumIA, for longitudinal brain tumor volumetry by comparing the automatically estimated volumes with ground truth data acquired via manual segmentation. Longitudinal Magnetic Resonance (MR) Imaging data of 14 patients with newly diagnosed glioblastoma encompassing 64 MR acquisitions, ranging from preoperative up to 12 month follow-up images, was analysed. Manual segmentation was performed by two human raters. Strong correlations (R = 0.83–0.96, p < 0.001) were observed between volumetric estimates of BraTumIA and of each of the human raters for the contrast-enhancing (CET) and non-enhancing T2-hyperintense tumor compartments (NCE-T2). A quantitative analysis of the inter-rater disagreement showed that the disagreement between BraTumIA and each of the human raters was comparable to the disagreement between the human raters. In summary, BraTumIA generated volumetric trend curves of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments comparable to estimates of human raters. These findings suggest the potential of automated longitudinal tumor segmentation to substitute manual volumetric follow-up of contrast-enhancing and non-enhancing T2-hyperintense tumor compartments. PMID:27001047

Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

NASA Astrophysics Data System (ADS)

Plassard, Andrew J.; McHugo, Maureen; Heckers, Stephan; Landman, Bennett A.

2017-02-01

Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus. This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75). Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R2 greater than 0.95, p < 0.05 for all structures). This work signifies the first result in an ongoing work to develop a robust tool for measurement of the hippocampus and other temporal lobe structures.

The role of image registration in brain mapping

PubMed Central

Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography

PubMed Central

Gray Roncal, William; Prasad, Judy A.; Fernandes, Hugo L.; Gürsoy, Doga; De Andrade, Vincent; Fezzaa, Kamel; Xiao, Xianghui; Vogelstein, Joshua T.; Jacobsen, Chris; Körding, Konrad P.

2017-01-01

Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts. PMID:29085899

3D Slicer as a tool for interactive brain tumor segmentation.

PubMed

Kikinis, Ron; Pieper, Steve

2011-01-01

User interaction is required for reliable segmentation of brain tumors in clinical practice and in clinical research. By incorporating current research tools, 3D Slicer provides a set of interactive, easy to use tools that can be efficiently used for this purpose. One of the modules of 3D Slicer is an interactive editor tool, which contains a variety of interactive segmentation effects. Use of these effects for fast and reproducible segmentation of a single glioblastoma from magnetic resonance imaging data is demonstrated. The innovation in this work lies not in the algorithm, but in the accessibility of the algorithm because of its integration into a software platform that is practical for research in a clinical setting.

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

PubMed

Rachmadi, Muhammad Febrian; Valdés-Hernández, Maria Del C; Agan, Maria Leonora Fatimah; Di Perri, Carol; Komura, Taku

2018-06-01

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI. We also introduce a way to incorporate spatial information in convolution level of CNN for WMH segmentation named global spatial information (GSI). Analysis of covariance corroborated known associations between WMH progression, as assessed by all methods evaluated, and demographic and clinical data. Deep learning algorithms outperform conventional machine learning algorithms by excluding MRI artefacts and pathologies that appear similar to WMH. Our proposed approach of incorporating GSI also successfully helped CNN to achieve better automatic WMH segmentation regardless of network's settings tested. The mean Dice Similarity Coefficient (DSC) values for LST-LGA, SVM, RF, DBM, CNN and CNN-GSI were 0.2963, 0.1194, 0.1633, 0.3264, 0.5359 and 5389 respectively. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Robust Skull-Stripping Segmentation Based on Irrational Mask for Magnetic Resonance Brain Images.

PubMed

Moldovanu, Simona; Moraru, Luminița; Biswas, Anjan

2015-12-01

This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.

Toward knowledge-enhanced viewing using encyclopedias and model-based segmentation

NASA Astrophysics Data System (ADS)

Kneser, Reinhard; Lehmann, Helko; Geller, Dieter; Qian, Yue-Chen; Weese, Jürgen

2009-02-01

To make accurate decisions based on imaging data, radiologists must associate the viewed imaging data with the corresponding anatomical structures. Furthermore, given a disease hypothesis possible image findings which verify the hypothesis must be considered and where and how they are expressed in the viewed images. If rare anatomical variants, rare pathologies, unfamiliar protocols, or ambiguous findings are present, external knowledge sources such as medical encyclopedias are consulted. These sources are accessed using keywords typically describing anatomical structures, image findings, pathologies. In this paper we present our vision of how a patient's imaging data can be automatically enhanced with anatomical knowledge as well as knowledge about image findings. On one hand, we propose the automatic annotation of the images with labels from a standard anatomical ontology. These labels are used as keywords for a medical encyclopedia such as STATdx to access anatomical descriptions, information about pathologies and image findings. On the other hand we envision encyclopedias to contain links to region- and finding-specific image processing algorithms. Then a finding is evaluated on an image by applying the respective algorithm in the associated anatomical region. Towards realization of our vision, we present our method and results of automatic annotation of anatomical structures in 3D MRI brain images. Thereby we develop a complex surface mesh model incorporating major structures of the brain and a model-based segmentation method. We demonstrate the validity by analyzing the results of several training and segmentation experiments with clinical data focusing particularly on the visual pathway.

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.

Tumor segmentation of multi-echo MR T2-weighted images with morphological operators

NASA Astrophysics Data System (ADS)

Torres, W.; Martín-Landrove, M.; Paluszny, M.; Figueroa, G.; Padilla, G.

2009-02-01

In the present work an automatic brain tumor segmentation procedure based on mathematical morphology is proposed. The approach considers sequences of eight multi-echo MR T2-weighted images. The relaxation time T2 characterizes the relaxation of water protons in the brain tissue: white matter, gray matter, cerebrospinal fluid (CSF) or pathological tissue. Image data is initially regularized by the application of a log-convex filter in order to adjust its geometrical properties to those of noiseless data, which exhibits monotonously decreasing convex behavior. Finally the regularized data is analyzed by means of an 8-dimensional morphological eccentricity filter. In a first stage, the filter was used for the spatial homogenization of the tissues in the image, replacing each pixel by the most representative pixel within its structuring element, i.e. the one which exhibits the minimum total distance to all members in the structuring element. On the filtered images, the relaxation time T2 is estimated by means of least square regression algorithm and the histogram of T2 is determined. The T2 histogram was partitioned using the watershed morphological operator; relaxation time classes were established and used for tissue classification and segmentation of the image. The method was validated on 15 sets of MRI data with excellent results.

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.

The Brain/MINDS 3D digital marmoset brain atlas

PubMed Central

Woodward, Alexander; Hashikawa, Tsutomu; Maeda, Masahide; Kaneko, Takaaki; Hikishima, Keigo; Iriki, Atsushi; Okano, Hideyuki; Yamaguchi, Yoko

2018-01-01

We present a new 3D digital brain atlas of the non-human primate, common marmoset monkey (Callithrix jacchus), with MRI and coregistered Nissl histology data. To the best of our knowledge this is the first comprehensive digital 3D brain atlas of the common marmoset having normalized multi-modal data, cortical and sub-cortical segmentation, and in a common file format (NIfTI). The atlas can be registered to new data, is useful for connectomics, functional studies, simulation and as a reference. The atlas was based on previously published work but we provide several critical improvements to make this release valuable for researchers. Nissl histology images were processed to remove illumination and shape artifacts and then normalized to the MRI data. Brain region segmentation is provided for both hemispheres. The data is in the NIfTI format making it easy to integrate into neuroscience pipelines, whereas the previous atlas was in an inaccessible file format. We also provide cortical, mid-cortical and white matter boundary segmentations useful for visualization and analysis. PMID:29437168

Inhomogeneity compensation for MR brain image segmentation using a multi-stage FCM-based approach.

PubMed

Szilágyi, László; Szilágyi, Sándor M; Dávid, László; Benyó, Zoltán

2008-01-01

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of the slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

The Cambridge MRI database for animal models of Huntington disease.

PubMed

Sawiak, Stephen J; Morton, A Jennifer

2016-01-01

We describe the Cambridge animal brain magnetic resonance imaging repository comprising 400 datasets to date from mouse models of Huntington disease. The data include raw images as well as segmented grey and white matter images with maps of cortical thickness. All images and phenotypic data for each subject are freely-available without restriction from (http://www.dspace.cam.ac.uk/handle/1810/243361/). Software and anatomical population templates optimised for animal brain analysis with MRI are also available from this site. Copyright © 2015. Published by Elsevier Inc.

An Example-Based Brain MRI Simulation Framework.

PubMed

He, Qing; Roy, Snehashis; Jog, Amod; Pham, Dzung L

2015-02-21

The simulation of magnetic resonance (MR) images plays an important role in the validation of image analysis algorithms such as image segmentation, due to lack of sufficient ground truth in real MR images. Previous work on MRI simulation has focused on explicitly modeling the MR image formation process. However, because of the overwhelming complexity of MR acquisition these simulations must involve simplifications and approximations that can result in visually unrealistic simulated images. In this work, we describe an example-based simulation framework, which uses an "atlas" consisting of an MR image and its anatomical models derived from the hard segmentation. The relationships between the MR image intensities and its anatomical models are learned using a patch-based regression that implicitly models the physics of the MR image formation. Given the anatomical models of a new brain, a new MR image can be simulated using the learned regression. This approach has been extended to also simulate intensity inhomogeneity artifacts based on the statistical model of training data. Results show that the example based MRI simulation method is capable of simulating different image contrasts and is robust to different choices of atlas. The simulated images resemble real MR images more than simulations produced by a physics-based model.

Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

PubMed

Link, Daphna; Braginsky, Michael B; Joskowicz, Leo; Ben Sira, Liat; Harel, Shaul; Many, Ariel; Tarrasch, Ricardo; Malinger, Gustavo; Artzi, Moran; Kapoor, Cassandra; Miller, Elka; Ben Bashat, Dafna

2018-01-01

Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). The developed method showed high correlation with manual segmentation (r2 = 0.9183, p < 0.001) as well as mean volume and volume overlap differences of 4.77 and 18.13%, respectively. New reference data on 199 normal fetuses were created, and all 9 IUGR fetuses were at or below the third percentile of the normal growth chart. The proposed method is fast, accurate, reproducible, user independent, applicable with retrospective data, and is suggested for use in routine clinical practice. © 2017 S. Karger AG, Basel.

Multi-object segmentation using coupled nonparametric shape and relative pose priors

NASA Astrophysics Data System (ADS)

Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

2009-02-01

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

Computation of a high-resolution MRI 3D stereotaxic atlas of the sheep brain.

PubMed

Ella, Arsène; Delgadillo, José A; Chemineau, Philippe; Keller, Matthieu

2017-02-15

The sheep model was first used in the fields of animal reproduction and veterinary sciences and then was utilized in fundamental and preclinical studies. For more than a decade, magnetic resonance (MR) studies performed on this model have been increasingly reported, especially in the field of neuroscience. To contribute to MR translational neuroscience research, a brain template and an atlas are necessary. We have recently generated the first complete T1-weighted (T1W) and T2W MR population average images (or templates) of in vivo sheep brains. In this study, we 1) defined a 3D stereotaxic coordinate system for previously established in vivo population average templates; 2) used deformation fields obtained during optimized nonlinear registrations to compute nonlinear tissues or prior probability maps (nlTPMs) of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) tissues; 3) delineated 25 external and 28 internal sheep brain structures by segmenting both templates and nlTPMs; and 4) annotated and labeled these structures using an existing histological atlas. We built a quality high-resolution 3D atlas of average in vivo sheep brains linked to a reference stereotaxic space. The atlas and nlTPMs, associated with previously computed T1W and T2W in vivo sheep brain templates and nlTPMs, provide a complete set of imaging space that are able to be imported into other imaging software programs and could be used as standardized tools for neuroimaging studies or other neuroscience methods, such as image registration, image segmentation, identification of brain structures, implementation of recording devices, or neuronavigation. J. Comp. Neurol. 525:676-692, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Automatic Segmentation of Drosophila Neural Compartments Using GAL4 Expression Data Reveals Novel Visual Pathways.

PubMed

Panser, Karin; Tirian, Laszlo; Schulze, Florian; Villalba, Santiago; Jefferis, Gregory S X E; Bühler, Katja; Straw, Andrew D

2016-08-08

Identifying distinct anatomical structures within the brain and developing genetic tools to target them are fundamental steps for understanding brain function. We hypothesize that enhancer expression patterns can be used to automatically identify functional units such as neuropils and fiber tracts. We used two recent, genome-scale Drosophila GAL4 libraries and associated confocal image datasets to segment large brain regions into smaller subvolumes. Our results (available at https://strawlab.org/braincode) support this hypothesis because regions with well-known anatomy, namely the antennal lobes and central complex, were automatically segmented into familiar compartments. The basis for the structural assignment is clustering of voxels based on patterns of enhancer expression. These initial clusters are agglomerated to make hierarchical predictions of structure. We applied the algorithm to central brain regions receiving input from the optic lobes. Based on the automated segmentation and manual validation, we can identify and provide promising driver lines for 11 previously identified and 14 novel types of visual projection neurons and their associated optic glomeruli. The same strategy can be used in other brain regions and likely other species, including vertebrates. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.

PubMed

Sweeney, Elizabeth M; Shinohara, Russell T; Shiee, Navid; Mateen, Farrah J; Chudgar, Avni A; Cuzzocreo, Jennifer L; Calabresi, Peter A; Pham, Dzung L; Reich, Daniel S; Crainiceanu, Ciprian M

2013-01-01

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images.

White matter segmentation by estimating tissue optical attenuation from volumetric OCT massive histology of whole rodent brains

NASA Astrophysics Data System (ADS)

Lefebvre, Joël.; Castonguay, Alexandre; Lesage, Frédéric

2017-02-01

A whole rodent brain was imaged using an automated massive histology setup and an Optical Coherence Tomography (OCT) microscope. Thousands of OCT volumetric tiles were acquired, each covering a size of about 2.5x2.5x0.8 mm3 with a sampling resolution of 4.9x4.9x6.5 microns. This paper shows the techniques for reconstruction, attenuation compensation and segmentation of the sliced brains. The tile positions within the mosaic were evaluated using a displacement model of the motorized stage and pairwise coregistration. Volume blending was then performed by solving the 3D Laplace equation, and consecutive slices were assembled using the cross-correlation of their 2D image gradient. This reconstruction algorithm resulted in a 3D map of optical reflectivity for the whole brain at micrometric resolution. OCT tissue slices were then used to estimate the local attenuation coefficient based on a single scattering photon model. The attenuation map obtained exhibits a high contrast for all white matter fibres, regardless of their orientation. The tissue optical attenuation from the intrinsic OCT reflectivity contributes to better white matter tissue segmentation. The combined 3D maps of reflectivity and attenuation is a step toward the study of white matter at a microscopic scale for the whole brain in small animals.

A JOINT FRAMEWORK FOR 4D SEGMENTATION AND ESTIMATION OF SMOOTH TEMPORAL APPEARANCE CHANGES.

PubMed

Gao, Yang; Prastawa, Marcel; Styner, Martin; Piven, Joseph; Gerig, Guido

2014-04-01

Medical imaging studies increasingly use longitudinal images of individual subjects in order to follow-up changes due to development, degeneration, disease progression or efficacy of therapeutic intervention. Repeated image data of individuals are highly correlated, and the strong causality of information over time lead to the development of procedures for joint segmentation of the series of scans, called 4D segmentation. A main aim was improved consistency of quantitative analysis, most often solved via patient-specific atlases. Challenging open problems are contrast changes and occurance of subclasses within tissue as observed in multimodal MRI of infant development, neurodegeneration and disease. This paper proposes a new 4D segmentation framework that enforces continuous dynamic changes of tissue contrast patterns over time as observed in such data. Moreover, our model includes the capability to segment different contrast patterns within a specific tissue class, for example as seen in myelinated and unmyelinated white matter regions in early brain development. Proof of concept is shown with validation on synthetic image data and with 4D segmentation of longitudinal, multimodal pediatric MRI taken at 6, 12 and 24 months of age, but the methodology is generic w.r.t. different application domains using serial imaging.

Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults

PubMed Central

Liang, Peipeng; Shi, Lin; Chen, Nan; Luo, Yishan; Wang, Xing; Liu, Kai; Mok, Vincent CT; Chu, Winnie CW; Wang, Defeng; Li, Kuncheng

2015-01-01

Despite the known morphological differences (e.g., brain shape and size) in the brains of populations of different origins (e.g., age and race), the Chinese brain atlas is less studied. In the current study, we developed a statistical brain atlas based on a multi-center high quality magnetic resonance imaging (MRI) dataset of 2020 Chinese adults (18–76 years old). We constructed 12 Chinese brain atlas from the age 20 year to the age 75 at a 5 years interval. New Chinese brain standard space, coordinates, and brain area labels were further defined. The new Chinese brain atlas was validated in brain registration and segmentation. It was found that, as contrast to the MNI152 template, the proposed Chinese atlas showed higher accuracy in hippocampus segmentation and relatively smaller shape deformations during registration. These results indicate that a population-specific time varying brain atlas may be more appropriate for studies involving Chinese populations. PMID:26678304

Performing label-fusion-based segmentation using multiple automatically generated templates.

PubMed

Chakravarty, M Mallar; Steadman, Patrick; van Eede, Matthijs C; Calcott, Rebecca D; Gu, Victoria; Shaw, Philip; Raznahan, Armin; Collins, D Louis; Lerch, Jason P

2013-10-01

Classically, model-based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi-atlas-based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel-by-voxel label-voting procedure. In this article, we demonstrate how the multi-atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high-resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model-based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi-atlas label-fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Copyright © 2012 Wiley Periodicals, Inc.

A new method for automated high-dimensional lesion segmentation evaluated in vascular injury and applied to the human occipital lobe.

PubMed

Mah, Yee-Haur; Jager, Rolf; Kennard, Christopher; Husain, Masud; Nachev, Parashkev

2014-07-01

Making robust inferences about the functional neuroanatomy of the brain is critically dependent on experimental techniques that examine the consequences of focal loss of brain function. Unfortunately, the use of the most comprehensive such technique-lesion-function mapping-is complicated by the need for time-consuming and subjective manual delineation of the lesions, greatly limiting the practicability of the approach. Here we exploit a recently-described general measure of statistical anomaly, zeta, to devise a fully-automated, high-dimensional algorithm for identifying the parameters of lesions within a brain image given a reference set of normal brain images. We proceed to evaluate such an algorithm in the context of diffusion-weighted imaging of the commonest type of lesion used in neuroanatomical research: ischaemic damage. Summary performance metrics exceed those previously published for diffusion-weighted imaging and approach the current gold standard-manual segmentation-sufficiently closely for fully-automated lesion-mapping studies to become a possibility. We apply the new method to 435 unselected images of patients with ischaemic stroke to derive a probabilistic map of the pattern of damage in lesions involving the occipital lobe, demonstrating the variation of anatomical resolvability of occipital areas so as to guide future lesion-function studies of the region. Copyright © 2012 Elsevier Ltd. All rights reserved.

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age.

PubMed

Wilke, Marko

2018-02-01

This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1-75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.

Cerebella segmentation on MR images of pediatric patients with medulloblastoma

NASA Astrophysics Data System (ADS)

Shan, Zu Y.; Ji, Qing; Glass, John; Gajjar, Amar; Reddick, Wilburn E.

2005-04-01

In this study, an automated method has been developed to identify the cerebellum from T1-weighted MR brain images of patients with medulloblastoma. A new objective function that is similar to Gibbs free energy in classic physics was defined; and the brain structure delineation was viewed as a process of minimizing Gibbs free energy. We used a rigid-body registration and an active contour (snake) method to minimize the Gibbs free energy in this study. The method was applied to 20 patient data sets to generate cerebellum images and volumetric results. The generated cerebellum images were compared with two manually drawn results. Strong correlations were found between the automatically and manually generated volumetric results, the correlation coefficients with each of manual results were 0.971 and 0.974, respectively. The average Jaccard similarities with each of two manual results were 0.89 and 0.88, respectively. The average Kappa indexes with each of two manual results were 0.94 and 0.93, respectively. These results showed this method was both robust and accurate for cerebellum segmentation. The method may be applied to various research and clinical investigation in which cerebellum segmentation and quantitative MR measurement of cerebellum are needed.

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

PubMed

Kamnitsas, Konstantinos; Ledig, Christian; Newcombe, Virginia F J; Simpson, Joanna P; Kane, Andrew D; Menon, David K; Rueckert, Daniel; Glocker, Ben

2017-02-01

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Crowdsourcing the creation of image segmentation algorithms for connectomics.

PubMed

Arganda-Carreras, Ignacio; Turaga, Srinivas C; Berger, Daniel R; Cireşan, Dan; Giusti, Alessandro; Gambardella, Luca M; Schmidhuber, Jürgen; Laptev, Dmitry; Dwivedi, Sarvesh; Buhmann, Joachim M; Liu, Ting; Seyedhosseini, Mojtaba; Tasdizen, Tolga; Kamentsky, Lee; Burget, Radim; Uher, Vaclav; Tan, Xiao; Sun, Changming; Pham, Tuan D; Bas, Erhan; Uzunbas, Mustafa G; Cardona, Albert; Schindelin, Johannes; Seung, H Sebastian

2015-01-01

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

The Potential of Using Brain Images for Authentication

PubMed Central

Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition. PMID:25126604

The potential of using brain images for authentication.

PubMed

Chen, Fanglin; Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

Brain blood vessel segmentation using line-shaped profiles

NASA Astrophysics Data System (ADS)

Babin, Danilo; Pižurica, Aleksandra; De Vylder, Jonas; Vansteenkiste, Ewout; Philips, Wilfried

2013-11-01

Segmentation of cerebral blood vessels is of great importance in diagnostic and clinical applications, especially for embolization of cerebral aneurysms and arteriovenous malformations (AVMs). In order to perform embolization of the AVM, the structural and geometric information of blood vessels from 3D images is of utmost importance. For this reason, the in-depth segmentation of cerebral blood vessels is usually done as a fusion of different segmentation techniques, often requiring extensive user interaction. In this paper we introduce the idea of line-shaped profiling with an application to brain blood vessel and AVM segmentation, efficient both in terms of resolving details and in terms of computation time. Our method takes into account both local proximate and wider neighbourhood of the processed pixel, which makes it efficient for segmenting large blood vessel tree structures, as well as fine structures of the AVMs. Another advantage of our method is that it requires selection of only one parameter to perform segmentation, yielding very little user interaction.

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images

PubMed Central

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections. PMID:29875639

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images.

PubMed

Varando, Gherardo; Benavides-Piccione, Ruth; Muñoz, Alberto; Kastanauskaite, Asta; Bielza, Concha; Larrañaga, Pedro; DeFelipe, Javier

2018-01-01

The development of 3D visualization and reconstruction methods to analyse microscopic structures at different levels of resolutions is of great importance to define brain microorganization and connectivity. MultiMap is a new tool that allows the visualization, 3D segmentation and quantification of fluorescent structures selectively in the neuropil from large stacks of confocal microscopy images. The major contribution of this tool is the posibility to easily navigate and create regions of interest of any shape and size within a large brain area that will be automatically 3D segmented and quantified to determine the density of puncta in the neuropil. As a proof of concept, we focused on the analysis of glutamatergic and GABAergic presynaptic axon terminals in the mouse hippocampal region to demonstrate its use as a tool to provide putative excitatory and inhibitory synaptic maps. The segmentation and quantification method has been validated over expert labeled images of the mouse hippocampus and over two benchmark datasets, obtaining comparable results to the expert detections.

A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.

PubMed

Kaur, Taranjit; Saini, Barjinder Singh; Gupta, Savita

2018-03-01

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.

Brain tissue segmentation in 4D CT using voxel classification

NASA Astrophysics Data System (ADS)

van den Boom, R.; Oei, M. T. H.; Lafebre, S.; Oostveen, L. J.; Meijer, F. J. A.; Steens, S. C. A.; Prokop, M.; van Ginneken, B.; Manniesing, R.

2012-02-01

A method is proposed to segment anatomical regions of the brain from 4D computer tomography (CT) patient data. The method consists of a three step voxel classification scheme, each step focusing on structures that are increasingly difficult to segment. The first step classifies air and bone, the second step classifies vessels and the third step classifies white matter, gray matter and cerebrospinal fluid. As features the time averaged intensity value and the temporal intensity change value were used. In each step, a k-Nearest-Neighbor classifier was used to classify the voxels. Training data was obtained by placing regions of interest in reconstructed 3D image data. The method has been applied to ten 4D CT cerebral patient data. A leave-one-out experiment showed consistent and accurate segmentation results.

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

NASA Astrophysics Data System (ADS)

Moualhi, Wafa; Ezzeddine, Zagrouba

2015-02-01

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

Spatially adapted augmentation of age-specific atlas-based segmentation using patch-based priors

NASA Astrophysics Data System (ADS)

Liu, Mengyuan; Seshamani, Sharmishtaa; Harrylock, Lisa; Kitsch, Averi; Miller, Steven; Chau, Van; Poskitt, Kenneth; Rousseau, Francois; Studholme, Colin

2014-03-01

One of the most common approaches to MRI brain tissue segmentation is to employ an atlas prior to initialize an Expectation- Maximization (EM) image labeling scheme using a statistical model of MRI intensities. This prior is commonly derived from a set of manually segmented training data from the population of interest. However, in cases where subject anatomy varies significantly from the prior anatomical average model (for example in the case where extreme developmental abnormalities or brain injuries occur), the prior tissue map does not provide adequate information about the observed MRI intensities to ensure the EM algorithm converges to an anatomically accurate labeling of the MRI. In this paper, we present a novel approach for automatic segmentation of such cases. This approach augments the atlas-based EM segmentation by exploring methods to build a hybrid tissue segmentation scheme that seeks to learn where an atlas prior fails (due to inadequate representation of anatomical variation in the statistical atlas) and utilize an alternative prior derived from a patch driven search of the atlas data. We describe a framework for incorporating this patch-based augmentation of EM (PBAEM) into a 4D age-specific atlas-based segmentation of developing brain anatomy. The proposed approach was evaluated on a set of MRI brain scans of premature neonates with ages ranging from 27.29 to 46.43 gestational weeks (GWs). Results indicated superior performance compared to the conventional atlas-based segmentation method, providing improved segmentation accuracy for gray matter, white matter, ventricles and sulcal CSF regions.

Glioblastoma Segmentation: Comparison of Three Different Software Packages.

PubMed

Fyllingen, Even Hovig; Stensjøen, Anne Line; Berntsen, Erik Magnus; Solheim, Ole; Reinertsen, Ingerid

2016-01-01

To facilitate a more widespread use of volumetric tumor segmentation in clinical studies, there is an urgent need for reliable, user-friendly segmentation software. The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of glioblastoma; namely BrainVoyagerTM QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T1-weighted 1.5 or 3 Tesla Magnetic Resonance Imaging (MRI) scans were obtained in 20 consecutive patients who underwent surgery for glioblastoma. MRI scans were segmented twice in each software package by two investigators. Intra-rater, inter-rater and between-software agreement was compared by using differences of means with 95% limits of agreement (LoA), Dice's similarity coefficients (DSC) and Hausdorff distance (HD). Time expenditure of segmentations was measured using a stopwatch. Eighteen tumors were included in the analyses. Inter-rater agreement was highest for BrainVoyager with difference of means of 0.19 mL and 95% LoA from -2.42 mL to 2.81 mL. Between-software agreement and 95% LoA were very similar for the different software packages. Intra-rater, inter-rater and between-software DSC were ≥ 0.93 in all analyses. Time expenditure was approximately 41 min per segmentation in BrainVoyager, and 18 min per segmentation in both 3D Slicer and ITK-Snap. Our main findings were that there is a high agreement within and between the software packages in terms of small intra-rater, inter-rater and between-software differences of means and high Dice's similarity coefficients. Time expenditure was highest for BrainVoyager, but all software packages were relatively time-consuming, which may limit usability in an everyday clinical setting.

Patient-tailored multimodal neuroimaging, visualization and quantification of human intra-cerebral hemorrhage

NASA Astrophysics Data System (ADS)

Goh, Sheng-Yang M.; Irimia, Andrei; Vespa, Paul M.; Van Horn, John D.

2016-03-01

In traumatic brain injury (TBI) and intracerebral hemorrhage (ICH), the heterogeneity of lesion sizes and types necessitates a variety of imaging modalities to acquire a comprehensive perspective on injury extent. Although it is advantageous to combine imaging modalities and to leverage their complementary benefits, there are difficulties in integrating information across imaging types. Thus, it is important that efforts be dedicated to the creation and sustained refinement of resources for multimodal data integration. Here, we propose a novel approach to the integration of neuroimaging data acquired from human patients with TBI/ICH using various modalities; we also demonstrate the integrated use of multimodal magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) data for TBI analysis based on both visual observations and quantitative metrics. 3D models of healthy-appearing tissues and TBIrelated pathology are generated, both of which are derived from multimodal imaging data. MRI volumes acquired using FLAIR, SWI, and T2 GRE are used to segment pathology. Healthy tissues are segmented using user-supervised tools, and results are visualized using a novel graphical approach called a `connectogram', where brain connectivity information is depicted within a circle of radially aligned elements. Inter-region connectivity and its strength are represented by links of variable opacities drawn between regions, where opacity reflects the percentage longitudinal change in brain connectivity density. Our method for integrating, analyzing and visualizing structural brain changes due to TBI and ICH can promote knowledge extraction and enhance the understanding of mechanisms underlying recovery.

Quantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging

PubMed Central

Ahdidan, Jamila; Raji, Cyrus A.; DeYoe, Edgar A.; Mathis, Jedidiah; Noe, Karsten Ø.; Rimestad, Jens; Kjeldsen, Thomas K.; Mosegaard, Jesper; Becker, James T.; Lopez, Oscar

2015-01-01

Background: Multiple neurological disorders including Alzheimer’s disease (AD), mesial temporal sclerosis, and mild traumatic brain injury manifest with volume loss on brain MRI. Subtle volume loss is particularly seen early in AD. While prior research has demonstrated the value of this additional information from quantitative neuroimaging, very few applications have been approved for clinical use. Here we describe a US FDA cleared software program, NeuroreaderTM, for assessment of clinical hippocampal volume on brain MRI. Objective: To present the validation of hippocampal volumetrics on a clinical software program. Method: Subjects were drawn (n = 99) from the Alzheimer Disease Neuroimaging Initiative study. Volumetric brain MR imaging was acquired in both 1.5 T (n = 59) and 3.0 T (n = 40) scanners in participants with manual hippocampal segmentation. Fully automated hippocampal segmentation and measurement was done using a multiple atlas approach. The Dice Similarity Coefficient (DSC) measured the level of spatial overlap between NeuroreaderTM and gold standard manual segmentation from 0 to 1 with 0 denoting no overlap and 1 representing complete agreement. DSC comparisons between 1.5 T and 3.0 T scanners were done using standard independent samples T-tests. Results: In the bilateral hippocampus, mean DSC was 0.87 with a range of 0.78–0.91 (right hippocampus) and 0.76–0.91 (left hippocampus). Automated segmentation agreement with manual segmentation was essentially equivalent at 1.5 T (DSC = 0.879) versus 3.0 T (DSC = 0.872). Conclusion: This work provides a description and validation of a software program that can be applied in measuring hippocampal volume, a biomarker that is frequently abnormal in AD and other neurological disorders. PMID:26484924

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications.

PubMed

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

2016-12-21

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8.8  ±  12.6 mm, an MSSD of 1.5  ±  3.2 mm, and an SDSSD of 1.8  ±  3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

NASA Astrophysics Data System (ADS)

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

2016-12-01

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8.8  ±  12.6 mm, an MSSD of 1.5  ±  3.2 mm, and an SDSSD of 1.8  ±  3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

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.

A diabetic retinopathy detection method using an improved pillar K-means algorithm.

PubMed

Gogula, Susmitha Valli; Divakar, Ch; Satyanarayana, Ch; Rao, Allam Appa

2014-01-01

The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.

Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

NASA Astrophysics Data System (ADS)

Scorza, Davide; Moccia, Sara; De Luca, Giuseppe; Plaino, Lisa; Cardinale, Francesco; Mattos, Leonardo S.; Kabongo, Luis; De Momi, Elena

2017-03-01

Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

Quantitative comparison of 3D third harmonic generation and fluorescence microscopy images.

PubMed

Zhang, Zhiqing; Kuzmin, Nikolay V; Groot, Marie Louise; de Munck, Jan C

2018-01-01

Third harmonic generation (THG) microscopy is a label-free imaging technique that shows great potential for rapid pathology of brain tissue during brain tumor surgery. However, the interpretation of THG brain images should be quantitatively linked to images of more standard imaging techniques, which so far has been done qualitatively only. We establish here such a quantitative link between THG images of mouse brain tissue and all-nuclei-highlighted fluorescence images, acquired simultaneously from the same tissue area. For quantitative comparison of a substantial pair of images, we present here a segmentation workflow that is applicable for both THG and fluorescence images, with a precision of 91.3 % and 95.8 % achieved respectively. We find that the correspondence between the main features of the two imaging modalities amounts to 88.9 %, providing quantitative evidence of the interpretation of dark holes as brain cells. Moreover, 80 % bright objects in THG images overlap with nuclei highlighted in the fluorescence images, and they are 2 times smaller than the dark holes, showing that cells of different morphologies can be recognized in THG images. We expect that the described quantitative comparison is applicable to other types of brain tissue and with more specific staining experiments for cell type identification. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ex vivo micro-CT imaging of murine brain models using non-ionic iodinated contrast

NASA Astrophysics Data System (ADS)

Salas Bautista, N.; Martínez-Dávalos, A.; Rodríguez-Villafuerte, M.; Murrieta-Rodríguez, T.; Manjarrez-Marmolejo, J.; Franco-Pérez, J.; Calvillo-Velasco, M. E.

2014-11-01

Preclinical investigation of brain tumors is frequently carried out by means of intracranial implantation of brain tumor xenografts or allografts, with subsequent analysis of tumor growth using conventional histopathology. However, very little has been reported on the use contrast-enhanced techniques in micro-CT imaging for the study of malignant brain tumors in small animal models. The aim of this study has been to test a protocol for ex vivo imaging of murine brain models of glioblastoma multiforme (GBM) after treatment with non-ionic iodinated solution, using an in-house developed laboratory micro-CT. We have found that the best compromise between acquisition time and image quality is obtained using a 50 kVp, 0.5 mAs, 1° angular step on a 360 degree orbit acquisition protocol, with 70 μm reconstructed voxel size using the Feldkamp algorithm. With this parameters up to 4 murine brains can be scanned in tandem in less than 15 minutes. Image segmentation and analysis of three sample brains allowed identifying tumor volumes as small as 0.4 mm3.

Multi-atlas segmentation for abdominal organs with Gaussian mixture models

NASA Astrophysics Data System (ADS)

Burke, Ryan P.; Xu, Zhoubing; Lee, Christopher P.; Baucom, Rebeccah B.; Poulose, Benjamin K.; Abramson, Richard G.; Landman, Bennett A.

2015-03-01

Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid / gray matter / white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms. In the context of variable abdominal anatomy and rich algorithms, it is difficult to assess the marginal contribution of GMM. Herein, we characterize the efficacy of an a posteriori framework that integrates GMM of organ-wise intensity likelihood with spatial priors from multiple target-specific registered labels. In our study, we first manually labeled 100 CT images. Then, we assigned 40 images to use as training data for constructing target-specific spatial priors and intensity likelihoods. The remaining 60 images were evaluated as test targets for segmenting 12 abdominal organs. The overlap between the true and the automatic segmentations was measured by Dice similarity coefficient (DSC). A median improvement of 145% was achieved by integrating the GMM intensity likelihood against the specific spatial prior. The proposed framework opens the opportunities for abdominal organ segmentation by efficiently using both the spatial and appearance information from the atlases, and creates a benchmark for large-scale automatic abdominal segmentation.

Computer aided solution for segmenting the neuron line in hippocampal microscope images

NASA Astrophysics Data System (ADS)

Albaidhani, Tahseen; Jassim, Sabah; Al-Assam, Hisham

2017-05-01

The brain Hippocampus component is known to be responsible for memory and spatial navigation. Its functionality depends on the status of different blood vessels within the Hippocampus and is severely impaired by Alzheimer's disease as a result blockage of increasing number of blood vessels by accumulation of amyloid-beta (Aβ) protein. Accurate counting of blood vessels within the Hippocampus of mice brain, from microscopic images, is an active research area for the understanding of Alzheimer's disease. Here, we report our work on automatic detection of the Region of Interest, i.e. the region in which blood vessels are located. This area typically falls between the hippocampus edge and the line of neurons within the Hippocampus. This paper proposes a new method to detect and exclude the neuron line to improve the accuracy of blood vessel counting because some neurons on it might lead to false positive cases as they look like blood vessels. Our proposed solution is based on using trainable segmentation approach with morphological operations, taking into account variation in colour, intensity values, and image texture. Experiments on a sufficient number of microscopy images of mouse brain demonstrate the effectiveness of the developed solution in preparation for blood vessels counting.

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.

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans.

PubMed

Mendrik, Adriënne M; Vincken, Koen L; Kuijf, Hugo J; Breeuwer, Marcel; Bouvy, Willem H; de Bresser, Jeroen; Alansary, Amir; de Bruijne, Marleen; Carass, Aaron; El-Baz, Ayman; Jog, Amod; Katyal, Ranveer; Khan, Ali R; van der Lijn, Fedde; Mahmood, Qaiser; Mukherjee, Ryan; van Opbroek, Annegreet; Paneri, Sahil; Pereira, Sérgio; Persson, Mikael; Rajchl, Martin; Sarikaya, Duygu; Smedby, Örjan; Silva, Carlos A; Vrooman, Henri A; Vyas, Saurabh; Wang, Chunliang; Zhao, Liang; Biessels, Geert Jan; Viergever, Max A

2015-01-01

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

PubMed Central

Mendrik, Adriënne M.; Vincken, Koen L.; Kuijf, Hugo J.; Breeuwer, Marcel; Bouvy, Willem H.; de Bresser, Jeroen; Alansary, Amir; de Bruijne, Marleen; Carass, Aaron; El-Baz, Ayman; Jog, Amod; Katyal, Ranveer; Khan, Ali R.; van der Lijn, Fedde; Mahmood, Qaiser; Mukherjee, Ryan; van Opbroek, Annegreet; Paneri, Sahil; Pereira, Sérgio; Rajchl, Martin; Sarikaya, Duygu; Smedby, Örjan; Silva, Carlos A.; Vrooman, Henri A.; Vyas, Saurabh; Wang, Chunliang; Zhao, Liang; Biessels, Geert Jan; Viergever, Max A.

2015-01-01

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand. PMID:26759553

SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

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

Liu, Y; Stojadinovic, S; Jiang, S

Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similaritymore » metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0.078, SSIM 0.999±0.001, and HD 5.926±6.141mm. Conclusion: The developed automatic segmentation strategy, which yields accurate brain tumor delineation in evaluation cases, is promising for its application in SRS treatment planning.« less

Brain abnormality segmentation based on l1-norm minimization

NASA Astrophysics Data System (ADS)

Zeng, Ke; Erus, Guray; Tanwar, Manoj; Davatzikos, Christos

2014-03-01

We present a method that uses sparse representations to model the inter-individual variability of healthy anatomy from a limited number of normal medical images. Abnormalities in MR images are then defined as deviations from the normal variation. More precisely, we model an abnormal (pathological) signal y as the superposition of a normal part ~y that can be sparsely represented under an example-based dictionary, and an abnormal part r. Motivated by a dense error correction scheme recently proposed for sparse signal recovery, we use l1- norm minimization to separate ~y and r. We extend the existing framework, which was mainly used on robust face recognition in a discriminative setting, to address challenges of brain image analysis, particularly the high dimensionality and low sample size problem. The dictionary is constructed from local image patches extracted from training images aligned using smooth transformations, together with minor perturbations of those patches. A multi-scale sliding-window scheme is applied to capture anatomical variations ranging from fine and localized to coarser and more global. The statistical significance of the abnormality term r is obtained by comparison to its empirical distribution through cross-validation, and is used to assign an abnormality score to each voxel. In our validation experiments the method is applied for segmenting abnormalities on 2-D slices of FLAIR images, and we obtain segmentation results consistent with the expert-defined masks.

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

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

PubMed

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

2012-01-01

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

Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters.

PubMed

Dill, Vanderson; Klein, Pedro Costa; Franco, Alexandre Rosa; Pinho, Márcio Sarroglia

2018-04-01

Current state-of-the-art methods for whole and subfield hippocampus segmentation use pre-segmented templates, also known as atlases, in the pre-processing stages. Typically, the input image is registered to the template, which provides prior information for the segmentation process. Using a single standard atlas increases the difficulty in dealing with individuals who have a brain anatomy that is morphologically different from the atlas, especially in older brains. To increase the segmentation precision in these cases, without any manual intervention, multiple atlases can be used. However, registration to many templates leads to a high computational cost. Researchers have proposed to use an atlas pre-selection technique based on meta-information followed by the selection of an atlas based on image similarity. Unfortunately, this method also presents a high computational cost due to the image-similarity process. Thus, it is desirable to pre-select a smaller number of atlases as long as this does not impact on the segmentation quality. To pick out an atlas that provides the best registration, we evaluate the use of three meta-information parameters (medical condition, age range, and gender) to choose the atlas. In this work, 24 atlases were defined and each is based on the combination of the three meta-information parameters. These atlases were used to segment 352 vol from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Hippocampus segmentation with each of these atlases was evaluated and compared to reference segmentations of the hippocampus, which are available from ADNI. The use of atlas selection by meta-information led to a significant gain in the Dice similarity coefficient, which reached 0.68 ± 0.11, compared to 0.62 ± 0.12 when using only the standard MNI152 atlas. Statistical analysis showed that the three meta-information parameters provided a significant improvement in the segmentation accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automatic atlas-based three-label cartilage segmentation from MR knee images

PubMed Central

Shan, Liang; Zach, Christopher; Charles, Cecil; Niethammer, Marc

2016-01-01

Osteoarthritis (OA) is the most common form of joint disease and often characterized by cartilage changes. Accurate quantitative methods are needed to rapidly screen large image databases to assess changes in cartilage morphology. We therefore propose a new automatic atlas-based cartilage segmentation method for future automatic OA studies. Atlas-based segmentation methods have been demonstrated to be robust and accurate in brain imaging and therefore also hold high promise to allow for reliable and high-quality segmentations of cartilage. Nevertheless, atlas-based methods have not been well explored for cartilage segmentation. A particular challenge is the thinness of cartilage, its relatively small volume in comparison to surrounding tissue and the difficulty to locate cartilage interfaces – for example the interface between femoral and tibial cartilage. This paper focuses on the segmentation of femoral and tibial cartilage, proposing a multi-atlas segmentation strategy with non-local patch-based label fusion which can robustly identify candidate regions of cartilage. This method is combined with a novel three-label segmentation method which guarantees the spatial separation of femoral and tibial cartilage, and ensures spatial regularity while preserving the thin cartilage shape through anisotropic regularization. Our segmentation energy is convex and therefore guarantees globally optimal solutions. We perform an extensive validation of the proposed method on 706 images of the Pfizer Longitudinal Study. Our validation includes comparisons of different atlas segmentation strategies, different local classifiers, and different types of regularizers. To compare to other cartilage segmentation approaches we validate based on the 50 images of the SKI10 dataset. PMID:25128683

High resolution, MRI-based, segmented, computerized head phantom

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

Zubal, I.G.; Harrell, C.R.; Smith, E.O.

1999-01-01

The authors have created a high-resolution software phantom of the human brain which is applicable to voxel-based radiation transport calculations yielding nuclear medicine simulated images and/or internal dose estimates. A software head phantom was created from 124 transverse MRI images of a healthy normal individual. The transverse T2 slices, recorded in a 256x256 matrix from a GE Signa 2 scanner, have isotropic voxel dimensions of 1.5 mm and were manually segmented by the clinical staff. Each voxel of the phantom contains one of 62 index numbers designating anatomical, neurological, and taxonomical structures. The result is stored as a 256x256x128 bytemore » array. Internal volumes compare favorably to those described in the ICRP Reference Man. The computerized array represents a high resolution model of a typical human brain and serves as a voxel-based anthropomorphic head phantom suitable for computer-based modeling and simulation calculations. It offers an improved realism over previous mathematically described software brain phantoms, and creates a reference standard for comparing results of newly emerging voxel-based computations. Such voxel-based computations lead the way to developing diagnostic and dosimetry calculations which can utilize patient-specific diagnostic images. However, such individualized approaches lack fast, automatic segmentation schemes for routine use; therefore, the high resolution, typical head geometry gives the most realistic patient model currently available.« less

S-values calculated from a tomographic head/brain model for brain imaging

NASA Astrophysics Data System (ADS)

Chao, Tsi-chian; Xu, X. George

2004-11-01

A tomographic head/brain model was developed from the Visible Human images and used to calculate S-values for brain imaging procedures. This model contains 15 segmented sub-regions including caudate nucleus, cerebellum, cerebral cortex, cerebral white matter, corpus callosum, eyes, lateral ventricles, lenses, lentiform nucleus, optic chiasma, optic nerve, pons and middle cerebellar peduncle, skull CSF, thalamus and thyroid. S-values for C-11, O-15, F-18, Tc-99m and I-123 have been calculated using this model and a Monte Carlo code, EGS4. Comparison of the calculated S-values with those calculated from the MIRD (1999) stylized head/brain model shows significant differences. In many cases, the stylized head/brain model resulted in smaller S-values (as much as 88%), suggesting that the doses to a specific patient similar to the Visible Man could have been underestimated using the existing clinical dosimetry.

Pupil-segmentation-based adaptive optical correction of a high-numerical-aperture gradient refractive index lens for two-photon fluorescence endoscopy.

PubMed

Wang, Chen; Ji, Na

2012-06-01

The intrinsic aberrations of high-NA gradient refractive index (GRIN) lenses limit their image quality as well as field of view. Here we used a pupil-segmentation-based adaptive optical approach to correct the inherent aberrations in a two-photon fluorescence endoscope utilizing a 0.8 NA GRIN lens. By correcting the field-dependent aberrations, we recovered diffraction-limited performance across a large imaging field. The consequent improvements in imaging signal and resolution allowed us to detect fine structures that were otherwise invisible inside mouse brain slices.

Corpus Callosum Area and Brain Volume in Autism Spectrum Disorder: Quantitative Analysis of Structural MRI from the ABIDE Database

ERIC Educational Resources Information Center

Kucharsky Hiess, R.; Alter, R.; Sojoudi, S.; Ardekani, B. A.; Kuzniecky, R.; Pardoe, H. R.

2015-01-01

Reduced corpus callosum area and increased brain volume are two commonly reported findings in autism spectrum disorder (ASD). We investigated these two correlates in ASD and healthy controls using T1-weighted MRI scans from the Autism Brain Imaging Data Exchange (ABIDE). Automated methods were used to segment the corpus callosum and intracranial…

Interpreting CARS images of tissue within the C-H-stretching region

NASA Astrophysics Data System (ADS)

Dietzek, Benjamin; Meyer, Tobias; Medyukhina, Anna; Bergner, Norbert; Krafft, Christoph; Romeike, Bernd F. M.; Reichart, Rupert; Kalff, Rolf; Schmitt, Michael; Popp, Jürgen

2014-03-01

Single band coherent anti-Stokes Raman scattering (CARS) microscopy within the CH-stretching region is applied to detect individual cells and nuclei of human brain tissue and brain tumors - an information which allows for histopathologic grading of the tissue. The CARS image contrast within the C-H-stretching region correlated to the tissue composition. Based on the specific application example of identifying nuclei within (coherent) Raman images of neurotissue sections, we shall derive general design parameters for lasers optimally suited to serve in a clinical environment and discuss the potential of recently developed methods to analyze spectrally resolved CARS images and image segmentation algorithms.

Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Dvořák, P.; Kropatsch, W. G.; Bartušek, K.

2013-10-01

This work focuses on fully automatic detection of brain tumors. The first aim is to determine, whether the image contains a brain with a tumor, and if it does, localize it. The goal of this work is not the exact segmentation of tumors, but the localization of their approximate position. The test database contains 203 T2-weighted images of which 131 are images of healthy brain and the remaining 72 images contain brain with pathological area. The estimation, whether the image shows an afflicted brain and where a pathological area is, is done by multi resolution symmetry analysis. The first goal was tested by five-fold cross-validation technique with 100 repetitions to avoid the result dependency on sample order. This part of the proposed method reaches the true positive rate of 87.52% and the true negative rate of 93.14% for an afflicted brain detection. The evaluation of the second part of the algorithm was carried out by comparing the estimated location to the true tumor location. The detection of the tumor location reaches the rate of 95.83% of correct anomaly detection and the rate 87.5% of correct tumor location.

Towards the development of a spring-based continuum robot for neurosurgery

NASA Astrophysics Data System (ADS)

Kim, Yeongjin; Cheng, Shing Shin; Desai, Jaydev P.

2015-03-01

Brain tumor is usually life threatening due to the uncontrolled growth of abnormal cells native to the brain or the spread of tumor cells from outside the central nervous system to the brain. The risks involved in carrying out surgery within such a complex organ can cause severe anxiety in cancer patients. However, neurosurgery, which remains one of the more effective ways of treating brain tumors focused in a confined volume, can have a tremendously increased success rate if the appropriate imaging modality is used for complete tumor removal. Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast and is the imaging modality of choice for brain tumor imaging. MRI combined with continuum soft robotics has immense potential to be the revolutionary treatment technique in the field of brain cancer. It eliminates the concern of hand tremor and guarantees a more precise procedure. One of the prototypes of Minimally Invasive Neurosurgical Intracranial Robot (MINIR-II), which can be classified as a continuum soft robot, consists of a snake-like body made of three segments of rapid prototyped plastic springs. It provides improved dexterity with higher degrees of freedom and independent joint control. It is MRI-compatible, allowing surgeons to track and determine the real-time location of the robot relative to the brain tumor target. The robot was manufactured in a single piece using rapid prototyping technology at a low cost, allowing it to disposable after each use. MINIR-II has two DOFs at each segment with both joints controlled by two pairs of MRI-compatible SMA spring actuators. Preliminary motion tests have been carried out using vision-tracking method and the robot was able to move to different positions based on user commands.

Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR Images of Epileptic Patients

PubMed Central

Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad R.; Pompili, Dario; Soltanian-Zadeh, Hamid

2015-01-01

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the benchmark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively. PMID:25571043

Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features.

PubMed

Pinto, Adriano; Pereira, Sergio; Correia, Higino; Oliveira, J; Rasteiro, Deolinda M L D; Silva, Carlos A

2015-08-01

Gliomas are among the most common and aggressive brain tumours. Segmentation of these tumours is important for surgery and treatment planning, but also for follow-up evaluations. However, it is a difficult task, given that its size and locations are variable, and the delineation of all tumour tissue is not trivial, even with all the different modalities of the Magnetic Resonance Imaging (MRI). We propose a discriminative and fully automatic method for the segmentation of gliomas, using appearance- and context-based features to feed an Extremely Randomized Forest (Extra-Trees). Some of these features are computed over a non-linear transformation of the image. The proposed method was evaluated using the publicly available Challenge database from BraTS 2013, having obtained a Dice score of 0.83, 0.78 and 0.73 for the complete tumour, and the core and the enhanced regions, respectively. Our results are competitive, when compared against other results reported using the same database.

Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI.

PubMed

Wu, Dan; Ma, Ting; Ceritoglu, Can; Li, Yue; Chotiyanonta, Jill; Hou, Zhipeng; Hsu, John; Xu, Xin; Brown, Timothy; Miller, Michael I; Mori, Susumu

2016-01-15

Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation. Copyright © 2015 Elsevier Inc. All rights reserved.

BRAIN TUMOR SEGMENTATION WITH SYMMETRIC TEXTURE AND SYMMETRIC INTENSITY-BASED DECISION FORESTS.

PubMed

Bianchi, Anthony; Miller, James V; Tan, Ek Tsoon; Montillo, Albert

2013-04-01

Accurate automated segmentation of brain tumors in MR images is challenging due to overlapping tissue intensity distributions and amorphous tumor shape. However, a clinically viable solution providing precise quantification of tumor and edema volume would enable better pre-operative planning, treatment monitoring and drug development. Our contributions are threefold. First, we design efficient gradient and LBPTOP based texture features which improve classification accuracy over standard intensity features. Second, we extend our texture and intensity features to symmetric texture and symmetric intensity which further improve the accuracy for all tissue classes. Third, we demonstrate further accuracy enhancement by extending our long range features from 100mm to a full 200mm. We assess our brain segmentation technique on 20 patients in the BraTS 2012 dataset. Impact from each contribution is measured and the combination of all the features is shown to yield state-of-the-art accuracy and speed.

Robust skull stripping using multiple MR image contrasts insensitive to pathology.

PubMed

Roy, Snehashis; Butman, John A; Pham, Dzung L

2017-02-01

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T 1 -w MR images of normal brains, especially because high resolution T 1 -w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T 1 -w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR), 2 where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask. We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T 1 -w, T 2 -w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping.

PubMed

Studholme, Colin

2011-08-15

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

PubMed

Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

2018-04-19

White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

[(18)F]-fluorodeoxyglucose positron emission tomography of the cat brain: A feasibility study to investigate osteoarthritis-associated pain.

PubMed

Guillot, Martin; Chartrand, Gabriel; Chav, Ramnada; Rousseau, Jacques; Beaudoin, Jean-François; Martel-Pelletier, Johanne; Pelletier, Jean-Pierre; Lecomte, Roger; de Guise, Jacques A; Troncy, Eric

2015-06-01

The objective of this pilot study was to investigate central nervous system (CNS) changes related to osteoarthritis (OA)-associated chronic pain in cats using [(18)F]-fluorodeoxyglucose ((18)FDG) positron emission tomography (PET) imaging. The brains of five normal, healthy (non-OA) cats and seven cats with pain associated with naturally occurring OA were imaged using (18)FDG-PET during a standardized mild anesthesia protocol. The PET images were co-registered over a magnetic resonance image of a cat brain segmented into several regions of interest. Brain metabolism was assessed in these regions using standardized uptake values. The brain metabolism in the secondary somatosensory cortex, thalamus and periaqueductal gray matter was increased significantly (P ≤ 0.005) in OA cats compared with non-OA cats. This study indicates that (18)FDG-PET brain imaging in cats is feasible to investigate CNS changes related to chronic pain. The results also suggest that OA is associated with sustained nociceptive inputs and increased activity of the descending modulatory pathways. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Time Course of Segmentation and Cue-Selectivity in the Human Visual Cortex

PubMed Central

Appelbaum, Lawrence G.; Ales, Justin M.; Norcia, Anthony M.

2012-01-01

Texture discontinuities are a fundamental cue by which the visual system segments objects from their background. The neural mechanisms supporting texture-based segmentation are therefore critical to visual perception and cognition. In the present experiment we employ an EEG source-imaging approach in order to study the time course of texture-based segmentation in the human brain. Visual Evoked Potentials were recorded to four types of stimuli in which periodic temporal modulation of a central 3° figure region could either support figure-ground segmentation, or have identical local texture modulations but not produce changes in global image segmentation. The image discontinuities were defined either by orientation or phase differences across image regions. Evoked responses to these four stimuli were analyzed both at the scalp and on the cortical surface in retinotopic and functional regions-of-interest (ROIs) defined separately using fMRI on a subject-by-subject basis. Texture segmentation (tsVEP: segmenting versus non-segmenting) and cue-specific (csVEP: orientation versus phase) responses exhibited distinctive patterns of activity. Alternations between uniform and segmented images produced highly asymmetric responses that were larger after transitions from the uniform to the segmented state. Texture modulations that signaled the appearance of a figure evoked a pattern of increased activity starting at ∼143 ms that was larger in V1 and LOC ROIs, relative to identical modulations that didn't signal figure-ground segmentation. This segmentation-related activity occurred after an initial response phase that did not depend on the global segmentation structure of the image. The two cue types evoked similar tsVEPs up to 230 ms when they differed in the V4 and LOC ROIs. The evolution of the response proceeded largely in the feed-forward direction, with only weak evidence for feedback-related activity. PMID:22479566

Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?

PubMed

Khalifé, Maya; Fernandez, Brice; Jaubert, Olivier; Soussan, Michael; Brulon, Vincent; Buvat, Irène; Comtat, Claude

2017-09-21

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units ([Formula: see text]) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into [Formula: see text] was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of [Formula: see text] corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?

NASA Astrophysics Data System (ADS)

Khalifé, Maya; Fernandez, Brice; Jaubert, Olivier; Soussan, Michael; Brulon, Vincent; Buvat, Irène; Comtat, Claude

2017-10-01

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units (HU ) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous μ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into HU was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of 4~mm corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review.

PubMed

Pagnozzi, Alex M; Gal, Yaniv; Boyd, Roslyn N; Fiori, Simona; Fripp, Jurgen; Rose, Stephen; Dowson, Nicholas

2015-12-01

Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non-invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under-utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas-based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape modelling approaches to identify anatomical injury. The benefits of automatic segmentation in CP is important as it has the potential to elucidate the underlying relationship between image derived features and patient outcome, enabling better tailoring of therapy to individual patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of Cortical Morphology Evaluated with Longitudinal MR Brain Images of Preterm Infants

PubMed Central

Moeskops, Pim; Benders, Manon J. N. L.; Kersbergen, Karina J.; Groenendaal, Floris; de Vries, Linda S.; Viergever, Max A.; Išgum, Ivana

2015-01-01

Introduction The cerebral cortex develops rapidly in the last trimester of pregnancy. In preterm infants, brain development is very vulnerable because of their often complicated extra-uterine conditions. The aim of this study was to quantitatively describe cortical development in a cohort of 85 preterm infants with and without brain injury imaged at 30 and 40 weeks postmenstrual age (PMA). Methods In the acquired T2-weighted MR images, unmyelinated white matter (UWM), cortical grey matter (CoGM), and cerebrospinal fluid in the extracerebral space (CSF) were automatically segmented. Based on these segmentations, cortical descriptors evaluating volume, surface area, thickness, gyrification index, and global mean curvature were computed at both time points, for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes separately. Additionally, visual scoring of brain abnormality was performed using a conventional scoring system at 40 weeks PMA. Results The evaluated descriptors showed larger change in the occipital lobes than in the other lobes. Moreover, the cortical descriptors showed an association with the abnormality scores: gyrification index and global mean curvature decreased, whereas, interestingly, median cortical thickness increased with increasing abnormality score. This was more pronounced at 40 weeks PMA than at 30 weeks PMA, suggesting that the period between 30 and 40 weeks PMA might provide a window of opportunity for intervention to prevent delay in cortical development. PMID:26161536

Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation.

PubMed

Subudhi, Badri Narayan; Thangaraj, Veerakumar; Sankaralingam, Esakkirajan; Ghosh, Ashish

2016-11-01

In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures. Copyright © 2016 Elsevier Inc. All rights reserved.

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

PubMed

Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well.

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem

PubMed Central

Wang, Jun Yi; Ngo, Michael M.; Hessl, David; Hagerman, Randi J.; Rivera, Susan M.

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer’s segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well. PMID:27213683

The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities.

PubMed

Xu, Yongchao; Morel, Baptiste; Dahdouh, Sonia; Puybareau, Élodie; Virzì, Alessio; Urien, Héléne; Géraud, Thierry; Adamsbaum, Catherine; Bloch, Isabelle

2018-05-17

Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi- automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max-tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.5T, which corresponds to the usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized. Copyright © 2018 Elsevier B.V. All rights reserved.

Towards Implementing an MR-based PET Attenuation Correction Method for Neurological Studies on the MR-PET Brain Prototype

PubMed Central

Catana, Ciprian; van der Kouwe, Andre; Benner, Thomas; Michel, Christian J.; Hamm, Michael; Fenchel, Matthias; Fischl, Bruce; Rosen, Bruce; Schmand, Matthias; Sorensen, A. Gregory

2013-01-01

A number of factors have to be considered for implementing an accurate attenuation correction (AC) in a combined MR-PET scanner. In this work, some of these challenges were investigated and an AC method based entirely on the MR data obtained with a single dedicated sequence was developed and used for neurological studies performed with the MR-PET human brain scanner prototype. Methods The focus was on the bone/air segmentation problem, the bone linear attenuation coefficient selection and the RF coil positioning. The impact of these factors on the PET data quantification was studied in simulations and experimental measurements performed on the combined MR-PET scanner. A novel dual-echo ultra-short echo time (DUTE) MR sequence was proposed for head imaging. Simultaneous MR-PET data were acquired and the PET images reconstructed using the proposed MR-DUTE-based AC method were compared with the PET images reconstructed using a CT-based AC. Results Our data suggest that incorrectly accounting for the bone tissue attenuation can lead to large underestimations (>20%) of the radiotracer concentration in the cortex. Assigning a linear attenuation coefficient of 0.143 or 0.151 cm−1 to bone tissue appears to give the best trade-off between bias and variability in the resulting images. Not identifying the internal air cavities introduces large overestimations (>20%) in adjacent structures. Based on these results, the segmented CT AC method was established as the “silver standard” for the segmented MR-based AC method. Particular to an integrated MR-PET scanner, ignoring the RF coil attenuation can cause large underestimations (i.e. up to 50%) in the reconstructed images. Furthermore, the coil location in the PET field of view has to be accurately known. Good quality bone/air segmentation can be performed using the DUTE data. The PET images obtained using the MR-DUTE- and CT-based AC methods compare favorably in most of the brain structures. Conclusion An MR-DUTE-based AC method was implemented considering all these factors and our preliminary results suggest that this method could potentially be as accurate as the segmented CT method and it could be used for quantitative neurological MR-PET studies. PMID:20810759

Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype.

PubMed

Catana, Ciprian; van der Kouwe, Andre; Benner, Thomas; Michel, Christian J; Hamm, Michael; Fenchel, Matthias; Fischl, Bruce; Rosen, Bruce; Schmand, Matthias; Sorensen, A Gregory

2010-09-01

Several factors have to be considered for implementing an accurate attenuation-correction (AC) method in a combined MR-PET scanner. In this work, some of these challenges were investigated, and an AC method based entirely on the MRI data obtained with a single dedicated sequence was developed and used for neurologic studies performed with the MR-PET human brain scanner prototype. The focus was on the problem of bone-air segmentation, selection of the linear attenuation coefficient for bone, and positioning of the radiofrequency coil. The impact of these factors on PET data quantification was studied in simulations and experimental measurements performed on the combined MR-PET scanner. A novel dual-echo ultrashort echo time (DUTE) MRI sequence was proposed for head imaging. Simultaneous MR-PET data were acquired, and the PET images reconstructed using the proposed DUTE MRI-based AC method were compared with the PET images that had been reconstructed using a CT-based AC method. Our data suggest that incorrectly accounting for the bone tissue attenuation can lead to large underestimations (>20%) of the radiotracer concentration in the cortex. Assigning a linear attenuation coefficient of 0.143 or 0.151 cm(-1) to bone tissue appears to give the best trade-off between bias and variability in the resulting images. Not identifying the internal air cavities introduces large overestimations (>20%) in adjacent structures. On the basis of these results, the segmented CT AC method was established as the silver standard for the segmented MRI-based AC method. For an integrated MR-PET scanner, in particular, ignoring the radiofrequency coil attenuation can cause large underestimations (i.e.,

The Effects of Changing Water Content, Relaxation Times, and Tissue Contrast on Tissue Segmentation and Measures of Cortical Anatomy in MR Images

PubMed Central

Bansal, Ravi; Hao, Xuejun; Liu, Feng; Xu, Dongrong; Liu, Jun; Peterson, Bradley S.

2013-01-01

Water content is the dominant chemical compound in the brain and it is the primary determinant of tissue contrast in magnetic resonance (MR) images. Water content varies greatly between individuals, and it changes dramatically over time from birth through senescence of the human life span. We hypothesize that the effects that individual- and age-related variations in water content have on contrast of the brain in MR images also has important, systematic effects on in vivo, MRI-based measures of regional brain volumes. We also hypothesize that changes in water content and tissue contrast across time may account for age-related changes in regional volumes, and that differences in water content or tissue contrast across differing neuropsychiatric diagnoses may account for differences in regional volumes across diagnostic groups. We demonstrate in several complementary ways that subtle variations in water content across age and tissue compartments alter tissue contrast, and that changing tissue contrast in turn alters measures of the thickness and volume of the cortical mantle: (1) We derive analytic relations describing how age-related changes in tissue relaxation times produce age-related changes in tissue gray-scale intensity values and tissue contrast; (2) We vary tissue contrast in computer-generated images to assess its effects on tissue segmentation and volumes of gray matter and white matter; and (3) We use real-world imaging data from adults with either Schizophrenia or Bipolar Disorder and age- and sex-matched healthy adults to assess the ways in which variations in tissue contrast across diagnoses affects group differences in tissue segmentation and associated volumes. We conclude that in vivo MRI-based morphological measures of the brain, including regional volumes and measures of cortical thickness, are a product of, or at least are confounded by, differences in tissue contrast across individuals, ages, and diagnostic groups, and that differences in tissue contrast in turn likely derive from corresponding differences in water content of the brain across individuals, ages, and diagnostic groups. PMID:24055410

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

NASA Astrophysics Data System (ADS)

Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

2015-04-01

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

NASA Astrophysics Data System (ADS)

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-01

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets—consisting of 20 and 18 volumes, respectively—provided by the Internet Brain Segmentation Repository.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction.

PubMed

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-07

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.

Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change.

PubMed

Williams, Owen A; Zeestraten, Eva A; Benjamin, Philip; Lambert, Christian; Lawrence, Andrew J; Mackinnon, Andrew D; Morris, Robin G; Markus, Hugh S; Charlton, Rebecca A; Barrick, Thomas R

2017-01-01

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period. 98 patients (aged 43-89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS ( p  < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models ( p  = 0.002). Change in DSEG θ was also related to change in all other MRI markers ( p  < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity. DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs).

Electroencephalographic inverse localization of brain activity in acute traumatic brain injury as a guide to surgery, monitoring and treatment

PubMed Central

Irimia, Andrei; Goh, S.-Y. Matthew; Torgerson, Carinna M.; Stein, Nathan R.; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.

2013-01-01

Objective To inverse-localize epileptiform cortical electrical activity recorded from severe traumatic brain injury (TBI) patients using electroencephalography (EEG). Methods Three acute TBI cases were imaged using computed tomography (CT) and multimodal magnetic resonance imaging (MRI). Semi-automatic segmentation was performed to partition the complete TBI head into 25 distinct tissue types, including 6 tissue types accounting for pathology. Segmentations were employed to generate a finite element method model of the head, and EEG activity generators were modeled as dipolar currents distributed over the cortical surface. Results We demonstrate anatomically faithful localization of EEG generators responsible for epileptiform discharges in severe TBI. By accounting for injury-related tissue conductivity changes, our work offers the most realistic implementation currently available for the inverse estimation of cortical activity in TBI. Conclusion Whereas standard localization techniques are available for electrical activity mapping in uninjured brains, they are rarely applied to acute TBI. Modern models of TBI-induced pathology can inform the localization of epileptogenic foci, improve surgical efficacy, contribute to the improvement of critical care monitoring and provide guidance for patient-tailored treatment. With approaches such as this, neurosurgeons and neurologists can study brain activity in acute TBI and obtain insights regarding injury effects upon brain metabolism and clinical outcome. PMID:24011495

Electroencephalographic inverse localization of brain activity in acute traumatic brain injury as a guide to surgery, monitoring and treatment.

PubMed

Irimia, Andrei; Goh, S-Y Matthew; Torgerson, Carinna M; Stein, Nathan R; Chambers, Micah C; Vespa, Paul M; Van Horn, John D

2013-10-01

To inverse-localize epileptiform cortical electrical activity recorded from severe traumatic brain injury (TBI) patients using electroencephalography (EEG). Three acute TBI cases were imaged using computed tomography (CT) and multimodal magnetic resonance imaging (MRI). Semi-automatic segmentation was performed to partition the complete TBI head into 25 distinct tissue types, including 6 tissue types accounting for pathology. Segmentations were employed to generate a finite element method model of the head, and EEG activity generators were modeled as dipolar currents distributed over the cortical surface. We demonstrate anatomically faithful localization of EEG generators responsible for epileptiform discharges in severe TBI. By accounting for injury-related tissue conductivity changes, our work offers the most realistic implementation currently available for the inverse estimation of cortical activity in TBI. Whereas standard localization techniques are available for electrical activity mapping in uninjured brains, they are rarely applied to acute TBI. Modern models of TBI-induced pathology can inform the localization of epileptogenic foci, improve surgical efficacy, contribute to the improvement of critical care monitoring and provide guidance for patient-tailored treatment. With approaches such as this, neurosurgeons and neurologists can study brain activity in acute TBI and obtain insights regarding injury effects upon brain metabolism and clinical outcome. Published by Elsevier B.V.

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

PubMed

Magliaro, Chiara; Callara, Alejandro L; Vanello, Nicola; Ahluwalia, Arti

2017-01-01

To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual "gold-standard" generated for the competition.

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

NASA Astrophysics Data System (ADS)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Visualizing the anatomical-functional correlation of the human brain

NASA Astrophysics Data System (ADS)

Chang, YuKuang; Rockwood, Alyn P.; Reiman, Eric M.

1995-04-01

Three-dimensional tomographic images obtained from different modalities or from the same modality at different times provide complementary information. For example, while PET shows brain function, images from MRI identify anatomical structures. In this paper, we investigate the problem of displaying available information about structures and function together. Several steps are described to achieve our goal. These include segmentation of the data, registration, resampling, and display. Segmentation is used to identify brain tissue from surrounding tissues, especially in the MRI data. Registration aligns the different modalities as closely as possible. Resampling arises from the registration since two data sets do not usually correspond and the rendering method is most easily achieved if the data correspond to the same grid used in display. We combine several techniques to display the data. MRI data is reconstructed from 2D slices into 3D structures from which isosurfaces are extracted and represented by approximating polygonalizations. These are then displayed using standard graphics pipelines including shaded and transparent images. PET data measures the qualitative rates of cerebral glucose utilization or oxygen consumption. PET image is best displayed as a volume of luminous particles. The combination of both display methods allows the viewer to compare the functional information contained in the PET data with the anatomically more precise MRI data.

MRI-Only Based Radiotherapy Treatment Planning for the Rat Brain on a Small Animal Radiation Research Platform (SARRP).

PubMed

Gutierrez, Shandra; Descamps, Benedicte; Vanhove, Christian

2015-01-01

Computed tomography (CT) is the standard imaging modality in radiation therapy treatment planning (RTP). However, magnetic resonance (MR) imaging provides superior soft tissue contrast, increasing the precision of target volume selection. We present MR-only based RTP for a rat brain on a small animal radiation research platform (SARRP) using probabilistic voxel classification with multiple MR sequences. Six rat heads were imaged, each with one CT and five MR sequences. The MR sequences were: T1-weighted, T2-weighted, zero-echo time (ZTE), and two ultra-short echo time sequences with 20 μs (UTE1) and 2 ms (UTE2) echo times. CT data were manually segmented into air, soft tissue, and bone to obtain the RTP reference. Bias field corrected MR images were automatically segmented into the same tissue classes using a fuzzy c-means segmentation algorithm with multiple images as input. Similarities between segmented CT and automatic segmented MR (ASMR) images were evaluated using Dice coefficient. Three ASMR images with high similarity index were used for further RTP. Three beam arrangements were investigated. Dose distributions were compared by analysing dose volume histograms. The highest Dice coefficients were obtained for the ZTE-UTE2 combination and for the T1-UTE1-T2 combination when ZTE was unavailable. Both combinations, along with UTE1-UTE2, often used to generate ASMR images, were used for further RTP. Using 1 beam, MR based RTP underestimated the dose to be delivered to the target (range: 1.4%-7.6%). When more complex beam configurations were used, the calculated dose using the ZTE-UTE2 combination was the most accurate, with 0.7% deviation from CT, compared to 0.8% for T1-UTE1-T2 and 1.7% for UTE1-UTE2. The presented MR-only based workflow for RTP on a SARRP enables both accurate organ delineation and dose calculations using multiple MR sequences. This method can be useful in longitudinal studies where CT's cumulative radiation dose might contribute to the total dose.

MRI-Only Based Radiotherapy Treatment Planning for the Rat Brain on a Small Animal Radiation Research Platform (SARRP)

PubMed Central

Gutierrez, Shandra; Descamps, Benedicte; Vanhove, Christian

2015-01-01

Computed tomography (CT) is the standard imaging modality in radiation therapy treatment planning (RTP). However, magnetic resonance (MR) imaging provides superior soft tissue contrast, increasing the precision of target volume selection. We present MR-only based RTP for a rat brain on a small animal radiation research platform (SARRP) using probabilistic voxel classification with multiple MR sequences. Six rat heads were imaged, each with one CT and five MR sequences. The MR sequences were: T1-weighted, T2-weighted, zero-echo time (ZTE), and two ultra-short echo time sequences with 20 μs (UTE1) and 2 ms (UTE2) echo times. CT data were manually segmented into air, soft tissue, and bone to obtain the RTP reference. Bias field corrected MR images were automatically segmented into the same tissue classes using a fuzzy c-means segmentation algorithm with multiple images as input. Similarities between segmented CT and automatic segmented MR (ASMR) images were evaluated using Dice coefficient. Three ASMR images with high similarity index were used for further RTP. Three beam arrangements were investigated. Dose distributions were compared by analysing dose volume histograms. The highest Dice coefficients were obtained for the ZTE-UTE2 combination and for the T1-UTE1-T2 combination when ZTE was unavailable. Both combinations, along with UTE1-UTE2, often used to generate ASMR images, were used for further RTP. Using 1 beam, MR based RTP underestimated the dose to be delivered to the target (range: 1.4%-7.6%). When more complex beam configurations were used, the calculated dose using the ZTE-UTE2 combination was the most accurate, with 0.7% deviation from CT, compared to 0.8% for T1-UTE1-T2 and 1.7% for UTE1-UTE2. The presented MR-only based workflow for RTP on a SARRP enables both accurate organ delineation and dose calculations using multiple MR sequences. This method can be useful in longitudinal studies where CT’s cumulative radiation dose might contribute to the total dose. PMID:26633302

Optimizing hippocampal segmentation in infants utilizing MRI post-acquisition processing.

PubMed

Thompson, Deanne K; Ahmadzai, Zohra M; Wood, Stephen J; Inder, Terrie E; Warfield, Simon K; Doyle, Lex W; Egan, Gary F

2012-04-01

This study aims to determine the most reliable method for infant hippocampal segmentation by comparing magnetic resonance (MR) imaging post-acquisition processing techniques: contrast to noise ratio (CNR) enhancement, or reformatting to standard orientation. MR scans were performed with a 1.5 T GE scanner to obtain dual echo T2 and proton density (PD) images at term equivalent (38-42 weeks' gestational age). 15 hippocampi were manually traced four times on ten infant images by 2 independent raters on the original T2 image, as well as images processed by: a) combining T2 and PD images (T2-PD) to enhance CNR; then b) reformatting T2-PD images perpendicular to the long axis of the left hippocampus. CNRs and intraclass correlation coefficients (ICC) were calculated. T2-PD images had 17% higher CNR (15.2) than T2 images (12.6). Original T2 volumes' ICC was 0.87 for rater 1 and 0.84 for rater 2, whereas T2-PD images' ICC was 0.95 for rater 1 and 0.87 for rater 2. Reliability of hippocampal segmentation on T2-PD images was not improved by reformatting images (rater 1 ICC = 0.88, rater 2 ICC = 0.66). Post-acquisition processing can improve CNR and hence reliability of hippocampal segmentation in neonate MR scans when tissue contrast is poor. These findings may be applied to enhance boundary definition in infant segmentation for various brain structures or in any volumetric study where image contrast is sub-optimal, enabling hippocampal structure-function relationships to be explored.

Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm.

PubMed

Kamali, Tahereh; Stashuk, Daniel

2016-10-01

Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity. Copyright © 2016 Elsevier B.V. All rights reserved.

Skull removal in MR images using a modified artificial bee colony optimization algorithm.

PubMed

Taherdangkoo, Mohammad

2014-01-01

Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications.

The L0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images.

PubMed

Duan, Yuping; Chang, Huibin; Huang, Weimin; Zhou, Jiayin; Lu, Zhongkang; Wu, Chunlin

2015-11-01

We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.

TH-CD-202-05: DECT Based Tissue Segmentation as Input to Monte Carlo Simulations for Proton Treatment Verification Using PET Imaging

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

Berndt, B; Wuerl, M; Dedes, G

Purpose: To improve agreement of predicted and measured positron emitter yields in patients, after proton irradiation for PET-based treatment verification, using a novel dual energy CT (DECT) tissue segmentation approach, overcoming known deficiencies from single energy CT (SECT). Methods: DECT head scans of 5 trauma patients were segmented and compared to existing decomposition methods with a first focus on the brain. For validation purposes, three brain equivalent solutions [water, white matter (WM) and grey matter (GM) – equivalent with respect to their reference carbon and oxygen contents and CT numbers at 90kVp and 150kVp] were prepared from water, ethanol, sucrosemore » and salt. The activities of all brain solutions, measured during a PET scan after uniform proton irradiation, were compared to Monte Carlo simulations. Simulation inputs were various solution compositions obtained from different segmentation approaches from DECT, SECT scans, and known reference composition. Virtual GM solution salt concentration corrections were applied based on DECT measurements of solutions with varying salt concentration. Results: The novel tissue segmentation showed qualitative improvements in %C for patient brain scans (ground truth unavailable). The activity simulations based on reference solution compositions agree with the measurement within 3–5% (4–8Bq/ml). These reference simulations showed an absolute activity difference between WM (20%C) and GM (10%C) to H2O (0%C) of 43 Bq/ml and 22 Bq/ml, respectively. Activity differences between reference simulations and segmented ones varied from −6 to 1 Bq/ml for DECT and −79 to 8 Bq/ml for SECT. Conclusion: Compared to the conventionally used SECT segmentation, the DECT based segmentation indicates a qualitative and quantitative improvement. In controlled solutions, a MC input based on DECT segmentation leads to better agreement with the reference. Future work will address the anticipated improvement of quantification accuracy in patients, comparing different tissue decomposition methods with an MR brain segmentation. Acknowledgement: DFG-MAP and HIT-Heidelberg Deutsche Forschungsgemeinschaft (MAP); Bundesministerium fur Bildung und Forschung (01IB13001)« less

Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.

PubMed

García-Lorenzo, Daniel; Lecoeur, Jeremy; Arnold, Douglas L; Collins, D Louis; Barillot, Christian

2009-01-01

Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.

Texture analysis of high-resolution FLAIR images for TLE

NASA Astrophysics Data System (ADS)

Jafari-Khouzani, Kourosh; Soltanian-Zadeh, Hamid; Elisevich, Kost

2005-04-01

This paper presents a study of the texture information of high-resolution FLAIR images of the brain with the aim of determining the abnormality and consequently the candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. Intensity and volume features of the hippocampus from FLAIR images of the brain have been previously shown to be useful in detecting the abnormal hippocampus in TLE. However, the small size of the hippocampus may limit the texture information. High-resolution FLAIR images show more details of the abnormal intensity variations of the hippocampi and therefore are more suitable for texture analysis. We study and compare the low and high-resolution FLAIR images of six epileptic patients. The hippocampi are segmented manually by an expert from T1-weighted MR images. Then the segmented regions are mapped on the corresponding FLAIR images for texture analysis. The 2-D wavelet transforms of the hippocampi are employed for feature extraction. We compare the ability of the texture features from regular and high-resolution FLAIR images to distinguish normal and abnormal hippocampi. Intracranial EEG results as well as surgery outcome are used as gold standard. The results show that the intensity variations of the hippocampus are related to the abnormalities in the TLE.

A fast 3D region growing approach for CT angiography applications

NASA Astrophysics Data System (ADS)

Ye, Zhen; Lin, Zhongmin; Lu, Cheng-chang

2004-05-01

Region growing is one of the most popular methods for low-level image segmentation. Many researches on region growing have focused on the definition of the homogeneity criterion or growing and merging criterion. However, one disadvantage of conventional region growing is redundancy. It requires a large memory usage, and the computation-efficiency is very low especially for 3D images. To overcome this problem, a non-recursive single-pass 3D region growing algorithm named SymRG is implemented and successfully applied to 3D CT angiography (CTA) applications for vessel segmentation and bone removal. The method consists of three steps: segmenting one-dimensional regions of each row; doing region merging to adjacent rows to obtain the region segmentation of each slice; and doing region merging to adjacent slices to obtain the final region segmentation of 3D images. To improve the segmentation speed for very large volume 3D CTA images, this algorithm is applied repeatedly to newly updated local cubes. The next new cube can be estimated by checking isolated segmented regions on all 6 faces of the current local cube. This local non-recursive 3D region-growing algorithm is memory-efficient and computation-efficient. Clinical testings of this algorithm on Brain CTA show this technique could effectively remove whole skull, most of the bones on the skull base, and reveal the cerebral vascular structures clearly.

A graph-based watershed merging using fuzzy C-means and simulated annealing for image segmentation

NASA Astrophysics Data System (ADS)

Vadiveloo, Mogana; Abdullah, Rosni; Rajeswari, Mandava

2015-12-01

In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.

Characterization of the Distance Relationship Between Localized Serotonin Receptors and Glia Cells on Fluorescence Microscopy Images of Brain Tissue.

PubMed

Jacak, Jaroslaw; Schaller, Susanne; Borgmann, Daniela; Winkler, Stephan M

2015-08-01

We here present two new methods for the characterization of fluorescent localization microscopy images obtained from immunostained brain tissue sections. Direct stochastic optical reconstruction microscopy images of 5-HT1A serotonin receptors and glial fibrillary acidic proteins in healthy cryopreserved brain tissues are analyzed. In detail, we here present two image processing methods for characterizing differences in receptor distribution on glial cells and their distribution on neural cells: One variant relies on skeleton extraction and adaptive thresholding, the other on k-means based discrete layer segmentation. Experimental results show that both methods can be applied for distinguishing classes of images with respect to serotonin receptor distribution. Quantification of nanoscopic changes in relative protein expression on particular cell types can be used to analyze degeneration in tissues caused by diseases or medical treatment.

Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control.

PubMed

Ciofolo, Cybèle; Barillot, Christian

2009-06-01

We propose a novel approach for the simultaneous segmentation of multiple structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the boundaries of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real magnetic resonance (MR) images are presented, quantitatively assessed and discussed.

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates.

PubMed

Zhao, Gengyan; Liu, Fang; Oler, Jonathan A; Meyerand, Mary E; Kalin, Ned H; Birn, Rasmus M

2018-07-15

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10 -4 , two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases. Copyright © 2018 Elsevier Inc. All rights reserved.

Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

PubMed

Lao, Zhiqiang; Shen, Dinggang; Liu, Dengfeng; Jawad, Abbas F; Melhem, Elias R; Launer, Lenore J; Bryan, R Nick; Davatzikos, Christos

2008-03-01

Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans. Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set. Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.

3D Segmentation with an application of level set-method using MRI volumes for image guided surgery.

PubMed

Bosnjak, A; Montilla, G; Villegas, R; Jara, I

2007-01-01

This paper proposes an innovation in the application for image guided surgery using a comparative study of three different method of segmentation. This segmentation method is faster than the manual segmentation of images, with the advantage that it allows to use the same patient as anatomical reference, which has more precision than a generic atlas. This new methodology for 3D information extraction is based on a processing chain structured of the following modules: 1) 3D Filtering: the purpose is to preserve the contours of the structures and to smooth the homogeneous areas; several filters were tested and finally an anisotropic diffusion filter was used. 2) 3D Segmentation. This module compares three different methods: Region growing Algorithm, Cubic spline hand assisted, and Level Set Method. It then proposes a Level Set-based on the front propagation method that allows the making of the reconstruction of the internal walls of the anatomical structures of the brain. 3) 3D visualization. The new contribution of this work consists on the visualization of the segmented model and its use in the pre-surgery planning.

Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.

PubMed

Sun, Xiaofei; Shi, Lin; Luo, Yishan; Yang, Wei; Li, Hongpeng; Liang, Peipeng; Li, Kuncheng; Mok, Vincent C T; Chu, Winnie C W; Wang, Defeng

2015-07-28

Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value; and (2) histogram normalization (HN),where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. We have proposed a histogram-based MRI intensity normalization method. The method can normalize scans which were acquired on different MRI units. We have validated that the method can greatly improve the image analysis performance. Furthermore, it is demonstrated that with the help of our normalization method, we can create a higher quality Chinese brain template.

Dataset variability leverages white-matter lesion segmentation performance with convolutional neural network

NASA Astrophysics Data System (ADS)

Ravnik, Domen; Jerman, Tim; Pernuš, Franjo; Likar, Boštjan; Å piclin, Žiga

2018-03-01

Performance of a convolutional neural network (CNN) based white-matter lesion segmentation in magnetic resonance (MR) brain images was evaluated under various conditions involving different levels of image preprocessing and augmentation applied and different compositions of the training dataset. On images of sixty multiple sclerosis patients, half acquired on one and half on another scanner of different vendor, we first created a highly accurate multi-rater consensus based lesion segmentations, which were used in several experiments to evaluate the CNN segmentation result. First, the CNN was trained and tested without preprocessing the images and by using various combinations of preprocessing techniques, namely histogram-based intensity standardization, normalization by whitening, and train dataset augmentation by flipping the images across the midsagittal plane. Then, the CNN was trained and tested on images of the same, different or interleaved scanner datasets using a cross-validation approach. The results indicate that image preprocessing has little impact on performance in a same-scanner situation, while between-scanner performance benefits most from intensity standardization and normalization, but also further by incorporating heterogeneous multi-scanner datasets in the training phase. Under such conditions the between-scanner performance of the CNN approaches that of the ideal situation, when the CNN is trained and tested on the same scanner dataset.

Automated pixel-wise brain tissue segmentation of diffusion-weighted images via machine learning.

PubMed

Ciritsis, Alexander; Boss, Andreas; Rossi, Cristina

2018-04-26

The diffusion-weighted (DW) MR signal sampled over a wide range of b-values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T 2 relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW-MRI datasets, and to determine the optimal sub-set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b-values and 20 diffusion-encoding directions. The pixel-wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T 1 -weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI-based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over-fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b-values of 5/10/500/1200 s/mm 2 and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information. Copyright © 2018 John Wiley & Sons, Ltd.

A scalable method to improve gray matter segmentation at ultra high field MRI.

PubMed

Gulban, Omer Faruk; Schneider, Marian; Marquardt, Ingo; Haast, Roy A M; De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

A scalable method to improve gray matter segmentation at ultra high field MRI

PubMed Central

De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data. PMID:29874295

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

Effects of peripubertal gonadotropin-releasing hormone agonist on brain development in sheep--a magnetic resonance imaging study.

PubMed

Nuruddin, Syed; Bruchhage, Muriel; Ropstad, Erik; Krogenæs, Anette; Evans, Neil P; Robinson, Jane E; Endestad, Tor; Westlye, Lars T; Madison, Cindee; Haraldsen, Ira Ronit Hebold

2013-10-01

In many species sexual dimorphisms in brain structures and functions have been documented. In ovine model, we have previously demonstrated that peri-pubertal pharmacological blockade of gonadotropin releasing hormone (GnRH) action increased sex-differences of executive emotional behavior. The structural substrate of this behavioral alteration however is unknown. In this magnetic resonance image (MRI) study on the same animals, we investigated the effects of GnRH agonist (GnRHa) treatment on the volume of total brain, hippocampus and amygdala. In total 41 brains (17 treated; 10 females and 7 males, and 24 controls; 11 females and 13 males) were included in the MRI study. Image acquisition was performed with 3-T MRI scanner. Segmentation of the amygdala and the hippocampus was done by manual tracing and total gray and white matter volumes were estimated by means of automated brain volume segmentation of the individual T2-weighted MRI volumes. Statistical comparisons were performed with general linear models. Highly significant GnRHa treatment effects were found on the volume of left and right amygdala, indicating larger amygdalae in treated animals. Significant sex differences were found for total gray matter and right amygdala, indicating larger volumes in male compared to female animals. Additionally, we observed a significant interaction between sex and treatment on left amygdala volume, indicating stronger effects of treatment in female compared to male animals. The effects of GnRHa treatment on amygdala volumes indicate that increasing GnRH concentration during puberty may have an important impact on normal brain development in mammals. These novel findings substantiate the need for further studies investigating potential neurobiological side effects of GnRHa treatment on the brains of young animals and humans. Copyright © 2013 Elsevier Ltd. All rights reserved.

A New MRI Masking Technique Based on Multi-Atlas Brain Segmentation in Controls and Schizophrenia: A Rapid and Viable Alternative to Manual Masking.

PubMed

Del Re, Elisabetta C; Gao, Yi; Eckbo, Ryan; Petryshen, Tracey L; Blokland, Gabriëlla A M; Seidman, Larry J; Konishi, Jun; Goldstein, Jill M; McCarley, Robert W; Shenton, Martha E; Bouix, Sylvain

2016-01-01

Brain masking of MRI images separates brain from surrounding tissue and its accuracy is important for further imaging analyses. We implemented a new brain masking technique based on multi-atlas brain segmentation (MABS) and compared MABS to masks generated using FreeSurfer (FS; version 5.3), Brain Extraction Tool (BET), and Brainwash, using manually defined masks (MM) as the gold standard. We further determined the effect of different masking techniques on cortical and subcortical volumes generated by FreeSurfer. Images were acquired on a 3-Tesla MR Echospeed system General Electric scanner on five control and five schizophrenia subjects matched on age, sex, and IQ. Automated masks were generated from MABS, FS, BET, and Brainwash, and compared to MM using these metrics: a) volume difference from MM; b) Dice coefficients; and c) intraclass correlation coefficients. Mean volume difference between MM and MABS masks was significantly less than the difference between MM and FS or BET masks. Dice coefficient between MM and MABS was significantly higher than Dice coefficients between MM and FS, BET, or Brainwash. For subcortical and left cortical regions, MABS volumes were closer to MM volumes than were BET or FS volumes. For right cortical regions, MABS volumes were closer to MM volumes than were BET volumes. Brain masks generated using FreeSurfer, BET, and Brainwash are rapidly obtained, but are less accurate than manually defined masks. Masks generated using MABS, in contrast, resemble more closely the gold standard of manual masking, thereby offering a rapid and viable alternative. Copyright © 2015 by the American Society of Neuroimaging.

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

PubMed Central

Wang, Hongzhi; Yushkevich, Paul A.

2013-01-01

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

3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach.

PubMed

Njeh, Ines; Sallemi, Lamia; Ayed, Ismail Ben; Chtourou, Khalil; Lehericy, Stephane; Galanaud, Damien; Hamida, Ahmed Ben

2015-03-01

This study investigates a fast distribution-matching, data-driven algorithm for 3D multimodal MRI brain glioma tumor and edema segmentation in different modalities. We learn non-parametric model distributions which characterize the normal regions in the current data. Then, we state our segmentation problems as the optimization of several cost functions of the same form, each containing two terms: (i) a distribution matching prior, which evaluates a global similarity between distributions, and (ii) a smoothness prior to avoid the occurrence of small, isolated regions in the solution. Obtained following recent bound-relaxation results, the optima of the cost functions yield the complement of the tumor region or edema region in nearly real-time. Based on global rather than pixel wise information, the proposed algorithm does not require an external learning from a large, manually-segmented training set, as is the case of the existing methods. Therefore, the ensuing results are independent of the choice of a training set. Quantitative evaluations over the publicly available training and testing data set from the MICCAI multimodal brain tumor segmentation challenge (BraTS 2012) demonstrated that our algorithm yields a highly competitive performance for complete edema and tumor segmentation, among nine existing competing methods, with an interesting computing execution time (less than 0.5s per image). Copyright © 2014 Elsevier Ltd. All rights reserved.

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction.

PubMed

Makropoulos, Antonios; Robinson, Emma C; Schuh, Andreas; Wright, Robert; Fitzgibbon, Sean; Bozek, Jelena; Counsell, Serena J; Steinweg, Johannes; Vecchiato, Katy; Passerat-Palmbach, Jonathan; Lenz, Gregor; Mortari, Filippo; Tenev, Tencho; Duff, Eugene P; Bastiani, Matteo; Cordero-Grande, Lucilio; Hughes, Emer; Tusor, Nora; Tournier, Jacques-Donald; Hutter, Jana; Price, Anthony N; Teixeira, Rui Pedro A G; Murgasova, Maria; Victor, Suresh; Kelly, Christopher; Rutherford, Mary A; Smith, Stephen M; Edwards, A David; Hajnal, Joseph V; Jenkinson, Mark; Rueckert, Daniel

2018-06-01

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity. Copyright © 2018 Elsevier Inc. All rights reserved.

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

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

Abdulbaqi, Hayder Saad; Department of Physics, College of Education, University of Al-Qadisiya, Al-Qadisiya; Jafri, Mohd Zubir Mat

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introducemore » a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.« less

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images.

PubMed

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S; Lin, Weili; Shen, Dinggang

2015-09-01

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images

PubMed Central

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [18F]FDG PET image by low-dose brain [18F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [18F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [18F]FDG PET image and substantially enhanced image quality of low-dose brain [18F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [18F]FDG PET image using low-dose brain [18F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [18F]FDG PET can be well-predicted using MRI and low-dose brain [18F]FDG PET. PMID:26328979

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

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

Kang, Jiayin; Gao, Yaozong; Shi, Feng

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. Asmore » yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.« less

Automated MRI segmentation for individualized modeling of current flow in the human head.

PubMed

Huang, Yu; Dmochowski, Jacek P; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C

2013-12-01

High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials.

Optimization-based interactive segmentation interface for multiregion problems

PubMed Central

Baxter, John S. H.; Rajchl, Martin; Peters, Terry M.; Chen, Elvis C. S.

2016-01-01

Abstract. Interactive segmentation is becoming of increasing interest to the medical imaging community in that it combines the positive aspects of both manual and automated segmentation. However, general-purpose tools have been lacking in terms of segmenting multiple regions simultaneously with a high degree of coupling between groups of labels. Hierarchical max-flow segmentation has taken advantage of this coupling for individual applications, but until recently, these algorithms were constrained to a particular hierarchy and could not be considered general-purpose. In a generalized form, the hierarchy for any given segmentation problem is specified in run-time, allowing different hierarchies to be quickly explored. We present an interactive segmentation interface, which uses generalized hierarchical max-flow for optimization-based multiregion segmentation guided by user-defined seeds. Applications in cardiac and neonatal brain segmentation are given as example applications of its generality. PMID:27335892

Preserved figure-ground segregation and symmetry perception in visual neglect.

PubMed

Driver, J; Baylis, G C; Rafal, R D

1992-11-05

A central controversy in current research on visual attention is whether figures are segregated from their background preattentively, or whether attention is first directed to unstructured regions of the image. Here we present neurological evidence for the former view from studies of a brain-injured patient with visual neglect. His attentional impairment arises after normal segmentation of the image into figures and background has taken place. Our results indicate that information which is neglected and unavailable to higher levels of visual processing can nevertheless be processed by earlier stages in the visual system concerned with segmentation.

Probabilistic atlas and geometric variability estimation to drive tissue segmentation.

PubMed

Xu, Hao; Thirion, Bertrand; Allassonnière, Stéphanie

2014-09-10

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

SyMRI of the Brain

PubMed Central

Hagiwara, Akifumi; Warntjes, Marcel; Hori, Masaaki; Andica, Christina; Nakazawa, Misaki; Kumamaru, Kanako Kunishima; Abe, Osamu; Aoki, Shigeki

2017-01-01

Abstract Conventional magnetic resonance images are usually evaluated using the image signal contrast between tissues and not based on their absolute signal intensities. Quantification of tissue parameters, such as relaxation rates and proton density, would provide an absolute scale; however, these methods have mainly been performed in a research setting. The development of rapid quantification, with scan times in the order of 6 minutes for full head coverage, has provided the prerequisites for clinical use. The aim of this review article was to introduce a specific quantification method and synthesis of contrast-weighted images based on the acquired absolute values, and to present automatic segmentation of brain tissues and measurement of myelin based on the quantitative values, along with application of these techniques to various brain diseases. The entire technique is referred to as “SyMRI” in this review. SyMRI has shown promising results in previous studies when used for multiple sclerosis, brain metastases, Sturge-Weber syndrome, idiopathic normal pressure hydrocephalus, meningitis, and postmortem imaging. PMID:28257339

Random forest feature selection approach for image segmentation

NASA Astrophysics Data System (ADS)

Lefkovits, László; Lefkovits, Szidónia; Emerich, Simina; Vaida, Mircea Florin

2017-03-01

In the field of image segmentation, discriminative models have shown promising performance. Generally, every such model begins with the extraction of numerous features from annotated images. Most authors create their discriminative model by using many features without using any selection criteria. A more reliable model can be built by using a framework that selects the important variables, from the point of view of the classification, and eliminates the unimportant once. In this article we present a framework for feature selection and data dimensionality reduction. The methodology is built around the random forest (RF) algorithm and its variable importance evaluation. In order to deal with datasets so large as to be practically unmanageable, we propose an algorithm based on RF that reduces the dimension of the database by eliminating irrelevant features. Furthermore, this framework is applied to optimize our discriminative model for brain tumor segmentation.

SU-E-J-252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi-Automated Segmentation Methods

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

Lee, M; Woo, B; Kim, J

Purpose: Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi-automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM). Methods: MRI examinations with T1 post-gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi-automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automaticallymore » from the segmented tumor. The inter-observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility. Results: Inter-observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE-T1W1. Conclusion: Comparison of two semi-automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI.« less

Confidence-based ensemble for GBM brain tumor segmentation

NASA Astrophysics Data System (ADS)

Huo, Jing; van Rikxoort, Eva M.; Okada, Kazunori; Kim, Hyun J.; Pope, Whitney; Goldin, Jonathan; Brown, Matthew

2011-03-01

It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.

T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm.

PubMed

Lüsebrink, Falk; Sciarra, Alessandro; Mattern, Hendrik; Yakupov, Renat; Speck, Oliver

2017-03-14

We present an ultrahigh resolution in vivo human brain magnetic resonance imaging (MRI) dataset. It consists of T 1 -weighted whole brain anatomical data acquired at 7 Tesla with a nominal isotropic resolution of 250 μm of a single young healthy Caucasian subject and was recorded using prospective motion correction. The raw data amounts to approximately 1.2 TB and was acquired in eight hours total scan time. The resolution of this dataset is far beyond any previously published in vivo structural whole brain dataset. Its potential use is to build an in vivo MR brain atlas. Methods for image reconstruction and image restoration can be improved as the raw data is made available. Pre-processing and segmentation procedures can possibly be enhanced for high magnetic field strength and ultrahigh resolution data. Furthermore, potential resolution induced changes in quantitative data analysis can be assessed, e.g., cortical thickness or volumetric measures, as high quality images with an isotropic resolution of 1 and 0.5 mm of the same subject are included in the repository as well.

T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm

NASA Astrophysics Data System (ADS)

Lüsebrink, Falk; Sciarra, Alessandro; Mattern, Hendrik; Yakupov, Renat; Speck, Oliver

2017-03-01

We present an ultrahigh resolution in vivo human brain magnetic resonance imaging (MRI) dataset. It consists of T1-weighted whole brain anatomical data acquired at 7 Tesla with a nominal isotropic resolution of 250 μm of a single young healthy Caucasian subject and was recorded using prospective motion correction. The raw data amounts to approximately 1.2 TB and was acquired in eight hours total scan time. The resolution of this dataset is far beyond any previously published in vivo structural whole brain dataset. Its potential use is to build an in vivo MR brain atlas. Methods for image reconstruction and image restoration can be improved as the raw data is made available. Pre-processing and segmentation procedures can possibly be enhanced for high magnetic field strength and ultrahigh resolution data. Furthermore, potential resolution induced changes in quantitative data analysis can be assessed, e.g., cortical thickness or volumetric measures, as high quality images with an isotropic resolution of 1 and 0.5 mm of the same subject are included in the repository as well.

Reconstruction of 7T-Like Images From 3T MRI

PubMed Central

Bahrami, Khosro; Shi, Feng; Zong, Xiaopeng; Shin, Hae Won; An, Hongyu

2016-01-01

In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. By doing so, the post-processing tasks, such as tissue segmentation, can be done more accurately and brain tissues details can be seen with higher resolution and contrast. To do this, we have acquired a unique dataset which includes paired 3T and 7T images scanned from same subjects, and then propose a hierarchical reconstruction based on group sparsity in a novel multi-level Canonical Correlation Analysis (CCA) space, to improve the quality of 3T MR image to be 7T-like MRI. First, overlapping patches are extracted from the input 3T MR image. Then, by extracting the most similar patches from all the aligned 3T and 7T images in the training set, the paired 3T and 7T dictionaries are constructed for each patch. It is worth noting that, for the training, we use pairs of 3T and 7T MR images from each training subject. Then, we propose multi-level CCA to map the paired 3T and 7T patch sets to a common space to increase their correlations. In such space, each input 3T MRI patch is sparsely represented by the 3T dictionary and then the obtained sparse coefficients are used together with the corresponding 7T dictionary to reconstruct the 7T-like patch. Also, to have the structural consistency between adjacent patches, the group sparsity is employed. This reconstruction is performed with changing patch sizes in a hierarchical framework. Experiments have been done using 13 subjects with both 3T and 7T MR images. The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images. PMID:27046894

Automatic corpus callosum segmentation for standardized MR brain scanning

NASA Astrophysics Data System (ADS)

Xu, Qing; Chen, Hong; Zhang, Li; Novak, Carol L.

2007-03-01

Magnetic Resonance (MR) brain scanning is often planned manually with the goal of aligning the imaging plane with key anatomic landmarks. The planning is time-consuming and subject to inter- and intra- operator variability. An automatic and standardized planning of brain scans is highly useful for clinical applications, and for maximum utility should work on patients of all ages. In this study, we propose a method for fully automatic planning that utilizes the landmarks from two orthogonal images to define the geometry of the third scanning plane. The corpus callosum (CC) is segmented in sagittal images by an active shape model (ASM), and the result is further improved by weighting the boundary movement with confidence scores and incorporating region based refinement. Based on the extracted contour of the CC, several important landmarks are located and then combined with landmarks from the coronal or transverse plane to define the geometry of the third plane. Our automatic method is tested on 54 MR images from 24 patients and 3 healthy volunteers, with ages ranging from 4 months to 70 years old. The average accuracy with respect to two manually labeled points on the CC is 3.54 mm and 4.19 mm, and differed by an average of 2.48 degrees from the orientation of the line connecting them, demonstrating that our method is sufficiently accurate for clinical use.

Segmentation of neuroanatomy in magnetic resonance images

NASA Astrophysics Data System (ADS)

Simmons, Andrew; Arridge, Simon R.; Barker, G. J.; Tofts, Paul S.

1992-06-01

Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

3D reconstruction of synapses with deep learning based on EM Images

NASA Astrophysics Data System (ADS)

Xiao, Chi; Rao, Qiang; Zhang, Dandan; Chen, Xi; Han, Hua; Xie, Qiwei

2017-03-01

Recently, due to the rapid development of electron microscope (EM) with its high resolution, stacks delivered by EM can be used to analyze a variety of components that are critical to understand brain function. Since synaptic study is essential in neurobiology and can be analyzed by EM stacks, the automated routines for reconstruction of synapses based on EM Images can become a very useful tool for analyzing large volumes of brain tissue and providing the ability to understand the mechanism of brain. In this article, we propose a novel automated method to realize 3D reconstruction of synapses for Automated Tapecollecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) with deep learning. Being different from other reconstruction algorithms, which employ classifier to segment synaptic clefts directly. We utilize deep learning method and segmentation algorithm to obtain synaptic clefts as well as promote the accuracy of reconstruction. The proposed method contains five parts: (1) using modified Moving Least Square (MLS) deformation algorithm and Scale Invariant Feature Transform (SIFT) features to register adjacent sections, (2) adopting Faster Region Convolutional Neural Networks (Faster R-CNN) algorithm to detect synapses, (3) utilizing screening method which takes context cues of synapses into consideration to reduce the false positive rate, (4) combining a practical morphology algorithm with a suitable fitting function to segment synaptic clefts and optimize the shape of them, (5) applying the plugin in FIJI to show the final 3D visualization of synapses. Experimental results on ATUM-SEM images demonstrate the effectiveness of our proposed method.

Deep learning and shapes similarity for joint segmentation and tracing single neurons in SEM images

NASA Astrophysics Data System (ADS)

Rao, Qiang; Xiao, Chi; Han, Hua; Chen, Xi; Shen, Lijun; Xie, Qiwei

2017-02-01

Extracting the structure of single neurons is critical for understanding how they function within the neural circuits. Recent developments in microscopy techniques, and the widely recognized need for openness and standardization provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. In order to look into the fine structure of neurons, we use the Automated Tape-collecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) to get images sequence of serial sections of animal brain tissue that densely packed with neurons. Different from other neuron reconstruction method, we propose a method that enhances the SEM images by detecting the neuronal membranes with deep convolutional neural network (DCNN) and segments single neurons by active contour with group shape similarity. We joint the segmentation and tracing together and they interact with each other by alternate iteration that tracing aids the selection of candidate region patch for active contour segmentation while the segmentation provides the neuron geometrical features which improve the robustness of tracing. The tracing model mainly relies on the neuron geometrical features and is updated after neuron being segmented on the every next section. Our method enables the reconstruction of neurons of the drosophila mushroom body which is cut to serial sections and imaged under SEM. Our method provides an elementary step for the whole reconstruction of neuronal networks.

Multimodal navigated skull base tumor resection using image-based vascular and cranial nerve segmentation: A prospective pilot study

PubMed Central

Dolati, Parviz; Gokoglu, Abdulkerim; Eichberg, Daniel; Zamani, Amir; Golby, Alexandra; Al-Mefty, Ossama

2015-01-01

Background: Skull base tumors frequently encase or invade adjacent normal neurovascular structures. For this reason, optimal tumor resection with incomplete knowledge of patient anatomy remains a challenge. Methods: To determine the accuracy and utility of image-based preoperative segmentation in skull base tumor resections, we performed a prospective study. Ten patients with skull base tumors underwent preoperative 3T magnetic resonance imaging, which included thin section three-dimensional (3D) space T2, 3D time of flight, and magnetization-prepared rapid acquisition gradient echo sequences. Imaging sequences were loaded in the neuronavigation system for segmentation and preoperative planning. Five different neurovascular landmarks were identified in each case and measured for accuracy using the neuronavigation system. Each segmented neurovascular element was validated by manual placement of the navigation probe, and errors of localization were measured. Results: Strong correspondence between image-based segmentation and microscopic view was found at the surface of the tumor and tumor-normal brain interfaces in all cases. The accuracy of the measurements was 0.45 ± 0.21 mm (mean ± standard deviation). This information reassured the surgeon and prevented vascular injury intraoperatively. Preoperative segmentation of the related cranial nerves was possible in 80% of cases and helped the surgeon localize involved cranial nerves in all cases. Conclusion: Image-based preoperative vascular and neural element segmentation with 3D reconstruction is highly informative preoperatively and could increase the vigilance of neurosurgeons for preventing neurovascular injury during skull base surgeries. Additionally, the accuracy found in this study is superior to previously reported measurements. This novel preliminary study is encouraging for future validation with larger numbers of patients. PMID:26674155

Multimodal navigated skull base tumor resection using image-based vascular and cranial nerve segmentation: A prospective pilot study.

PubMed

Dolati, Parviz; Gokoglu, Abdulkerim; Eichberg, Daniel; Zamani, Amir; Golby, Alexandra; Al-Mefty, Ossama

2015-01-01

Skull base tumors frequently encase or invade adjacent normal neurovascular structures. For this reason, optimal tumor resection with incomplete knowledge of patient anatomy remains a challenge. To determine the accuracy and utility of image-based preoperative segmentation in skull base tumor resections, we performed a prospective study. Ten patients with skull base tumors underwent preoperative 3T magnetic resonance imaging, which included thin section three-dimensional (3D) space T2, 3D time of flight, and magnetization-prepared rapid acquisition gradient echo sequences. Imaging sequences were loaded in the neuronavigation system for segmentation and preoperative planning. Five different neurovascular landmarks were identified in each case and measured for accuracy using the neuronavigation system. Each segmented neurovascular element was validated by manual placement of the navigation probe, and errors of localization were measured. Strong correspondence between image-based segmentation and microscopic view was found at the surface of the tumor and tumor-normal brain interfaces in all cases. The accuracy of the measurements was 0.45 ± 0.21 mm (mean ± standard deviation). This information reassured the surgeon and prevented vascular injury intraoperatively. Preoperative segmentation of the related cranial nerves was possible in 80% of cases and helped the surgeon localize involved cranial nerves in all cases. Image-based preoperative vascular and neural element segmentation with 3D reconstruction is highly informative preoperatively and could increase the vigilance of neurosurgeons for preventing neurovascular injury during skull base surgeries. Additionally, the accuracy found in this study is superior to previously reported measurements. This novel preliminary study is encouraging for future validation with larger numbers of patients.

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.

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

Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.

PubMed

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

2012-05-01

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

MRIVIEW: An interactive computational tool for investigation of brain structure and function

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

Ranken, D.; George, J.

MRIVIEW is a software system which uses image processing and visualization to provide neuroscience researchers with an integrated environment for combining functional and anatomical information. Key features of the software include semi-automated segmentation of volumetric head data and an interactive coordinate reconciliation method which utilizes surface visualization. The current system is a precursor to a computational brain atlas. We describe features this atlas will incorporate, including methods under development for visualizing brain functional data obtained from several different research modalities.

A pediatric brain structure atlas from T1-weighted MR images

NASA Astrophysics Data System (ADS)

Shan, Zuyao Y.; Parra, Carlos; Ji, Qing; Ogg, Robert J.; Zhang, Yong; Laningham, Fred H.; Reddick, Wilburn E.

2006-03-01

In this paper, we have developed a digital atlas of the pediatric human brain. Human brain atlases, used to visualize spatially complex structures of the brain, are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1 weighted MR data set of a 9 year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. Higher curvature surfaces were simplified with more reference points; lower curvature surfaces, with fewer. We constructed a 3D triangular mesh model for each structure by triangulation of the structure's reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/BrainAtlas) can be used to plan treatment, to conduct knowledge and modeldriven segmentation, and to analyze the shapes of brain structures in pediatric patients.

Computation of an MRI brain atlas from a population of Parkinson’s disease patients

NASA Astrophysics Data System (ADS)

Angelidakis, L.; Papageorgiou, I. E.; Damianou, C.; Psychogios, M. N.; Lingor, P.; von Eckardstein, K.; Hadjidemetriou, S.

2017-11-01

Parkinson’s Disease (PD) is a degenerative disorder of the brain. This study presents an MRI-based brain atlas of PD to characterize associated alterations for diagnostic and interventional purposes. The atlas standardizes primarily the implicated subcortical regions such as the globus pallidus (GP), substantia nigra (SN), subthalamic nucleus (STN), caudate nucleus (CN), thalamus (TH), putamen (PUT), and red nucleus (RN). The data were 3.0 T MRI brain images from 16 PD patients and 10 matched controls. The images used were T1-weighted (T 1 w), T2-weighted (T 2 w) images, and Susceptibility Weighted Images (SWI). The T1w images were the reference for the inter-subject non-rigid registration available from 3DSlicer. Anatomic labeling was achieved with BrainSuite and regions were refined with the level sets segmentation of ITK-Snap. The subcortical centers were analyzed for their volume and signal intensity. Comparison with an age-matched control group unravels a significant PD-related T1w signal loss in the striatum (CN and PUT) centers, but approximately a constant volume. The results in this study improve MRI based PD localization and can lead to the development of novel biomarkers.

Mindcontrol: A web application for brain segmentation quality control.

PubMed

Keshavan, Anisha; Datta, Esha; M McDonough, Ian; Madan, Christopher R; Jordan, Kesshi; Henry, Roland G

2018-04-15

Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A Patch-Based Approach for the Segmentation of Pathologies: Application to Glioma Labelling.

PubMed

Cordier, Nicolas; Delingette, Herve; Ayache, Nicholas

2016-04-01

In this paper, we describe a novel and generic approach to address fully-automatic segmentation of brain tumors by using multi-atlas patch-based voting techniques. In addition to avoiding the local search window assumption, the conventional patch-based framework is enhanced through several simple procedures: an improvement of the training dataset in terms of both label purity and intensity statistics, augmented features to implicitly guide the nearest-neighbor-search, multi-scale patches, invariance to cube isometries, stratification of the votes with respect to cases and labels. A probabilistic model automatically delineates regions of interest enclosing high-probability tumor volumes, which allows the algorithm to achieve highly competitive running time despite minimal processing power and resources. This method was evaluated on Multimodal Brain Tumor Image Segmentation challenge datasets. State-of-the-art results are achieved, with a limited learning stage thus restricting the risk of overfit. Moreover, segmentation smoothness does not involve any post-processing.

Deep learning for segmentation of brain tumors: can we train with images from different institutions?

NASA Astrophysics Data System (ADS)

Paredes, David; Saha, Ashirbani; Mazurowski, Maciej A.

2017-03-01

Deep learning and convolutional neural networks (CNNs) in particular are increasingly popular tools for segmentation and classification of medical images. CNNs were shown to be successful for segmentation of brain tumors into multiple regions or labels. However, in the environment which fosters data-sharing and collection of multi-institutional datasets, a question arises: does training with data from another institution with potentially different imaging equipment, contrast protocol, and patient population impact the segmentation performance of the CNN? Our study presents preliminary data towards answering this question. Specifically, we used MRI data of glioblastoma (GBM) patients for two institutions present in The Cancer Imaging Archive. We performed a process of training and testing CNN multiple times such that half of the time the CNN was tested on data from the same institution that was used for training and half of the time it was tested on another institution, keeping the training and testing set size constant. We observed a decrease in performance as measured by Dice coefficient when the CNN was trained with data from a different institution as compared to training with data from the same institution. The changes in performance for the entire tumor and for four different labels within the tumor were: 0.72 to 0.65 (p=0.06), 0.61 to 0.58 (p=0.49), 0.54 to 0.51 (p=0.82), 0.31 to 0.24 (p<0.03), and 0.43 to 0.31(p<0.003) respectively. In summary, we found that while data across institutions can be used for development of CNNs, this might be associated with a decrease in performance.

An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy

PubMed Central

Cardona, Albert; Saalfeld, Stephan; Preibisch, Stephan; Schmid, Benjamin; Cheng, Anchi; Pulokas, Jim; Tomancak, Pavel; Hartenstein, Volker

2010-01-01

The analysis of microcircuitry (the connectivity at the level of individual neuronal processes and synapses), which is indispensable for our understanding of brain function, is based on serial transmission electron microscopy (TEM) or one of its modern variants. Due to technical limitations, most previous studies that used serial TEM recorded relatively small stacks of individual neurons. As a result, our knowledge of microcircuitry in any nervous system is very limited. We applied the software package TrakEM2 to reconstruct neuronal microcircuitry from TEM sections of a small brain, the early larval brain of Drosophila melanogaster. TrakEM2 enables us to embed the analysis of the TEM image volumes at the microcircuit level into a light microscopically derived neuro-anatomical framework, by registering confocal stacks containing sparsely labeled neural structures with the TEM image volume. We imaged two sets of serial TEM sections of the Drosophila first instar larval brain neuropile and one ventral nerve cord segment, and here report our first results pertaining to Drosophila brain microcircuitry. Terminal neurites fall into a small number of generic classes termed globular, varicose, axiform, and dendritiform. Globular and varicose neurites have large diameter segments that carry almost exclusively presynaptic sites. Dendritiform neurites are thin, highly branched processes that are almost exclusively postsynaptic. Due to the high branching density of dendritiform fibers and the fact that synapses are polyadic, neurites are highly interconnected even within small neuropile volumes. We describe the network motifs most frequently encountered in the Drosophila neuropile. Our study introduces an approach towards a comprehensive anatomical reconstruction of neuronal microcircuitry and delivers microcircuitry comparisons between vertebrate and insect neuropile. PMID:20957184

Classification and localization of early-stage Alzheimer's disease in magnetic resonance images using a patch-based classifier ensemble.

PubMed

Simões, Rita; van Cappellen van Walsum, Anne-Marie; Slump, Cornelis H

2014-09-01

Classification methods have been proposed to detect Alzheimer’s disease (AD) using magnetic resonance images. Most rely on features such as the shape/volume of brain structures that need to be defined a priori. In this work, we propose a method that does not require either the segmentation of specific brain regions or the nonlinear alignment to a template. Besides classification, we also analyze which brain regions are discriminative between a group of normal controls and a group of AD patients. We perform 3D texture analysis using Local Binary Patterns computed at local image patches in the whole brain, combined in a classifier ensemble.We evaluate our method in a publicly available database including very mild-to-mild AD subjects and healthy elderly controls. For the subject cohort including only mild AD subjects, the best results are obtained using a combination of large (30×30×30 and 40×40×40 voxels) patches. A spatial analysis on the best performing patches shows that these are located in the medial-temporal lobe and in the periventricular regions. When very mild AD subjects are included in the dataset, the small (10×10×10 voxels) patches perform best, with the most discriminative ones being located near the left hippocampus. We show that our method is able not only to perform accurate classification, but also to localize dis-criminative brain regions, which are in accordance with the medical literature. This is achieved without the need to segment-specific brain structures and without performing nonlinear registration to a template, indicating that the method may be suitable for a clinical implementation that can help to diagnose AD at an earlier stage.

Automatic iterative segmentation of multiple sclerosis lesions using Student's t mixture models and probabilistic anatomical atlases in FLAIR images.

PubMed

Freire, Paulo G L; Ferrari, Ricardo J

2016-06-01

Multiple sclerosis (MS) is a demyelinating autoimmune disease that attacks the central nervous system (CNS) and affects more than 2 million people worldwide. The segmentation of MS lesions in magnetic resonance imaging (MRI) is a very important task to assess how a patient is responding to treatment and how the disease is progressing. Computational approaches have been proposed over the years to segment MS lesions and reduce the amount of time spent on manual delineation and inter- and intra-rater variability and bias. However, fully-automatic segmentation of MS lesions still remains an open problem. In this work, we propose an iterative approach using Student's t mixture models and probabilistic anatomical atlases to automatically segment MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) images. Our technique resembles a refinement approach by iteratively segmenting brain tissues into smaller classes until MS lesions are grouped as the most hyperintense one. To validate our technique we used 21 clinical images from the 2015 Longitudinal Multiple Sclerosis Lesion Segmentation Challenge dataset. Evaluation using Dice Similarity Coefficient (DSC), True Positive Ratio (TPR), False Positive Ratio (FPR), Volume Difference (VD) and Pearson's r coefficient shows that our technique has a good spatial and volumetric agreement with raters' manual delineations. Also, a comparison between our proposal and the state-of-the-art shows that our technique is comparable and, in some cases, better than some approaches, thus being a viable alternative for automatic MS lesion segmentation in MRI. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

PubMed Central

Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

2012-01-01

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI. PMID:22741026

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

NASA Astrophysics Data System (ADS)

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

2012-02-01

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

Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation.

PubMed

Khalilzadeh, Mohammad Mahdi; Fatemizadeh, Emad; Behnam, Hamid

2013-06-01

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods whose results have been improved. Copyright © 2013 Elsevier Inc. All rights reserved.

Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.

PubMed

Dong, Pei; Guo, Yangrong; Gao, Yue; Liang, Peipeng; Shi, Yonghong; Wang, Qian; Shen, Dinggang; Wu, Guorong

2016-10-01

Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. First , we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. Second , besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. Third , since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data.

PubMed

Guan, Jiangheng; Li, Jingcheng; Liang, Shanshan; Li, Ruijie; Li, Xingyi; Shi, Xiaozhe; Huang, Ciyu; Zhang, Jianxiong; Pan, Junxia; Jia, Hongbo; Zhang, Le; Chen, Xiaowei; Liao, Xiang

2018-01-01

Two-photon Ca 2+ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In particular, manually drawing regions of interest is the most time-consuming aspect of data analysis. However, the development of automated image analysis pipelines, which will be essential for dealing with the likely future deluge of imaging data, remains a major challenge. To address this issue, we developed NeuroSeg, an open-source MATLAB program that can facilitate the accurate and efficient segmentation of neurons in two-photon Ca 2+ imaging data. We proposed an approach using a generalized Laplacian of Gaussian filter to detect cells and weighting-based segmentation to separate individual cells from the background. We tested this approach on an in vivo two-photon Ca 2+ imaging dataset obtained from mouse cortical neurons with differently sized view fields. We show that this approach exhibits superior performance for cell detection and segmentation compared with the existing published tools. In addition, we integrated the previously reported, activity-based segmentation into our approach and found that this combined method was even more promising. The NeuroSeg software, including source code and graphical user interface, is freely available and will be a useful tool for in vivo brain activity mapping.

An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging.

PubMed

Izquierdo-Garcia, David; Hansen, Adam E; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T; Chonde, Daniel B; Catana, Ciprian

2014-11-01

We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners. Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed. The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement. We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. © 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Advanced MR Imaging of the Human Nucleus Accumbens--Additional Guiding Tool for Deep Brain Stimulation.

PubMed

Lucas-Neto, Lia; Reimão, Sofia; Oliveira, Edson; Rainha-Campos, Alexandre; Sousa, João; Nunes, Rita G; Gonçalves-Ferreira, António; Campos, Jorge G

2015-07-01

The human nucleus accumbens (Acc) has become a target for deep brain stimulation (DBS) in some neuropsychiatric disorders. Nonetheless, even with the most recent advances in neuroimaging it remains difficult to accurately delineate the Acc and closely related subcortical structures, by conventional MRI sequences. It is our purpose to perform a MRI study of the human Acc and to determine whether there are reliable anatomical landmarks that enable the precise location and identification of the nucleus and its core/shell division. For the Acc identification and delineation, based on anatomical landmarks, T1WI, T1IR and STIR 3T-MR images were acquired in 10 healthy volunteers. Additionally, 32-direction DTI was obtained for Acc segmentation. Seed masks for the Acc were generated with FreeSurfer and probabilistic tractography was performed using FSL. The probability of connectivity between the seed voxels and distinct brain areas was determined and subjected to k-means clustering analysis, defining 2 different regions. With conventional T1WI, the Acc borders are better defined through its surrounding anatomical structures. The DTI color-coded vector maps and IR sequences add further detail in the Acc identification and delineation. Additionally, using probabilistic tractography it is possible to segment the Acc into a core and shell division and establish its structural connectivity with different brain areas. Advanced MRI techniques allow in vivo delineation and segmentation of the human Acc and represent an additional guiding tool in the precise and safe target definition for DBS. © 2015 International Neuromodulation Society.

Towards automated segmentation of cells and cell nuclei in nonlinear optical microscopy.

PubMed

Medyukhina, Anna; Meyer, Tobias; Schmitt, Michael; Romeike, Bernd F M; Dietzek, Benjamin; Popp, Jürgen

2012-11-01

Nonlinear optical (NLO) imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or two photon excited fluorescence (TPEF) show great potential for biomedical imaging. In order to facilitate the diagnostic process based on NLO imaging, there is need for an automated calculation of quantitative values such as cell density, nucleus-to-cytoplasm ratio, average nuclear size. Extraction of these parameters is helpful for the histological assessment in general and specifically e.g. for the determination of tumor grades. This requires an accurate image segmentation and detection of locations and boundaries of cells and nuclei. Here we present an image processing approach for the detection of nuclei and cells in co-registered TPEF and CARS images. The algorithm developed utilizes the gray-scale information for the detection of the nuclei locations and the gradient information for the delineation of the nuclear and cellular boundaries. The approach reported is capable for an automated segmentation of cells and nuclei in multimodal TPEF-CARS images of human brain tumor samples. The results are important for the development of NLO microscopy into a clinically relevant diagnostic tool. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Possible functions of contextual modulations and receptive field nonlinearities: pop-out and texture segmentation

PubMed Central

Schmid, Anita M.; Victor, Jonathan D.

2014-01-01

When analyzing a visual image, the brain has to achieve several goals quickly. One crucial goal is to rapidly detect parts of the visual scene that might be behaviorally relevant, while another one is to segment the image into objects, to enable an internal representation of the world. Both of these processes can be driven by local variations in any of several image attributes such as luminance, color, and texture. Here, focusing on texture defined by local orientation, we propose that the two processes are mediated by separate mechanisms that function in parallel. More specifically, differences in orientation can cause an object to “pop out” and attract visual attention, if its orientation differs from that of the surrounding objects. Differences in orientation can also signal a boundary between objects and therefore provide useful information for image segmentation. We propose that contextual response modulations in primary visual cortex (V1) are responsible for orientation pop-out, while a different kind of receptive field nonlinearity in secondary visual cortex (V2) is responsible for orientation-based texture segmentation. We review a recent experiment that led us to put forward this hypothesis along with other research literature relevant to this notion. PMID:25064441

Multi-channel MRI segmentation with graph cuts using spectral gradient and multidimensional Gaussian mixture model

NASA Astrophysics Data System (ADS)

Lecoeur, Jérémy; Ferré, Jean-Christophe; Collins, D. Louis; Morrisey, Sean P.; Barillot, Christian

2009-02-01

A new segmentation framework is presented taking advantage of multimodal image signature of the different brain tissues (healthy and/or pathological). This is achieved by merging three different modalities of gray-level MRI sequences into a single RGB-like MRI, hence creating a unique 3-dimensional signature for each tissue by utilising the complementary information of each MRI sequence. Using the scale-space spectral gradient operator, we can obtain a spatial gradient robust to intensity inhomogeneity. Even though it is based on psycho-visual color theory, it can be very efficiently applied to the RGB colored images. More over, it is not influenced by the channel assigment of each MRI. Its optimisation by the graph cuts paradigm provides a powerful and accurate tool to segment either healthy or pathological tissues in a short time (average time about ninety seconds for a brain-tissues classification). As it is a semi-automatic method, we run experiments to quantify the amount of seeds needed to perform a correct segmentation (dice similarity score above 0.85). Depending on the different sets of MRI sequences used, this amount of seeds (expressed as a relative number in pourcentage of the number of voxels of the ground truth) is between 6 to 16%. We tested this algorithm on brainweb for validation purpose (healthy tissue classification and MS lesions segmentation) and also on clinical data for tumours and MS lesions dectection and tissues classification.

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging.

PubMed

Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong

2017-09-01

Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF

PubMed Central

Li, Zeju; Shi, Zhifeng; Guo, Yi; Chen, Liang; Mao, Ying

2017-01-01

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas. PMID:29065666

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF.

PubMed

Li, Zeju; Wang, Yuanyuan; Yu, Jinhua; Shi, Zhifeng; Guo, Yi; Chen, Liang; Mao, Ying

2017-01-01

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.

Scale-based fuzzy connectivity: a novel image segmentation methodology and its validation

NASA Astrophysics Data System (ADS)

Saha, Punam K.; Udupa, Jayaram K.

1999-05-01

This paper extends a previously reported theory and algorithms for fuzzy connected object definition. It introduces `object scale' for determining the neighborhood size for defining affinity, the degree of local hanging togetherness between image elements. Object scale allows us to use a varying neighborhood size in different parts of the image. This paper argues that scale-based fuzzy connectivity is natural in object definition and demonstrates that this leads to a more effective object segmentation than without using scale in fuzzy concentrations. Affinity is described as consisting of a homogeneity-based and an object-feature- based component. Families of non scale-based and scale-based affinity relations are constructed. An effective method for giving a rough estimate of scale at different locations in the image is presented. The original theoretical and algorithmic framework remains more-or-less the same but considerably improved segmentations result. A quantitative statistical comparison between the non scale-based and the scale-based methods was made based on phantom images generated from patient MR brain studies by first segmenting the objects, and then by adding noise and blurring, and background component. Both the statistical and the subjective tests clearly indicate the superiority of scale- based method in capturing details and in robustness to noise.

Development of Realistic Striatal Digital Brain (SDB) Phantom for 123I-FP-CIT SPECT and Effect on Ventricle in the Brain for Semi-quantitative Index of Specific Binding Ratio.

PubMed

Furuta, Akihiro; Onishi, Hideo; Nakamoto, Kenta

This study aimed at developing the realistic striatal digital brain (SDB) phantom and to assess specific binding ratio (SBR) for ventricular effect in the 123 I-FP-CIT SPECT imaging. SDB phantom was constructed in to four segments (striatum, ventricle, brain parenchyma, and skull bone) using Percentile method and other image processing in the T2-weighted MR images. The reference image was converted into 128×128 matrixes to align MR images with SPECT images. The process image was reconstructed with projection data sets generated from reference images additive blurring, attenuation, scatter, and statically noise. The SDB phantom was evaluated to find the accuracy of calculated SBR and to find the effect of SBR with/without ventricular counts with the reference and process images. We developed and investigated the utility of the SDB phantom in the 123 I-FP-CIT SPECT clinical study. The true value of SBR was just marched to calculate SBR from reference and process images. The SBR was underestimated 58.0% with ventricular counts in reference image, however, was underestimated 162% with ventricular counts in process images. The SDB phantom provides an extremely convenient tool for discovering basic properties of 123 I-FP-CIT SPECT clinical study image. It was suggested that the SBR was susceptible to ventricle.

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

PubMed

Souza, Roberto; Lucena, Oeslle; Garrafa, Julia; Gobbi, David; Saluzzi, Marina; Appenzeller, Simone; Rittner, Letícia; Frayne, Richard; Lotufo, Roberto

2018-04-15

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p-value<0.001) and magnetic field strength (p-value<0.001) have statistically significant impacts on skull stripping results. Copyright © 2017 Elsevier Inc. All rights reserved.

Delineation and segmentation of cerebral tumors by mapping blood-brain barrier disruption with dynamic contrast-enhanced CT and tracer kinetics modeling-a feasibility study.

PubMed

Bisdas, S; Yang, X; Lim, C C T; Vogl, T J; Koh, T S

2008-01-01

Dynamic contrast-enhanced (DCE) imaging is a promising approach for in vivo assessment of tissue microcirculation. Twenty patients with clinical and routine computed tomography (CT) evidence of intracerebral neoplasm were examined with DCE-CT imaging. Using a distributed-parameter model for tracer kinetics modeling of DCE-CT data, voxel-level maps of cerebral blood flow (F), intravascular blood volume (vi) and intravascular mean transit time (t1) were generated. Permeability-surface area product (PS), extravascular extracellular blood volume (ve) and extraction ratio (E) maps were also calculated to reveal pathologic locations of tracer extravasation, which are indicative of disruptions in the blood-brain barrier (BBB). All maps were visually assessed for quality of tumor delineation and measurement of tumor extent by two radiologists. Kappa (kappa) coefficients and their 95% confidence intervals (CI) were calculated to determine the interobserver agreement for each DCE-CT map. There was a substantial agreement for the tumor delineation quality in the F, ve and t1 maps. The agreement for the quality of the tumor delineation was excellent for the vi, PS and E maps. Concerning the measurement of tumor extent, excellent and nearly excellent agreement was achieved only for E and PS maps, respectively. According to these results, we performed a segmentation of the cerebral tumors on the base of the E maps. The interobserver agreement for the tumor extent quantification based on manual segmentation of tumor in the E maps vs. the computer-assisted segmentation was excellent (kappa = 0.96, CI: 0.93-0.99). The interobserver agreement for the tumor extent quantification based on computer segmentation in the mean images and the E maps was substantial (kappa = 0.52, CI: 0.42-0.59). This study illustrates the diagnostic usefulness of parametric maps associated with BBB disruption on a physiology-based approach and highlights the feasibility for automatic segmentation of cerebral tumors.

Phase congruency map driven brain tumour segmentation

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

2015-03-01

Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating multi-modal Magnetic Resonance Images (MRI). The use of Gadolinium-based contrast agents is only relevant at later stages of the disease where it highlights the enhancing rim of the tumour. Currently, there is no single accepted method that can be used as a reference. There are three main challenges with such images: to decide whether there is tumour present and is so localize it; to construct a mask that separates healthy and diseased tissue; and to differentiate between the tumour core and the surrounding oedema. This paper presents two contributions. First, we develop tumour seed selection based on multiscale multi-modal texture feature vectors. Second, we develop a method based on a local phase congruency based feature map to drive level-set segmentation. The segmentations achieved with our method are more accurate than previously presented methods, particularly for challenging low grade tumours.

Generalized expectation-maximization segmentation of brain MR images

NASA Astrophysics Data System (ADS)

Devalkeneer, Arnaud A.; Robe, Pierre A.; Verly, Jacques G.; Phillips, Christophe L. M.

2006-03-01

Manual segmentation of medical images is unpractical because it is time consuming, not reproducible, and prone to human error. It is also very difficult to take into account the 3D nature of the images. Thus, semi- or fully-automatic methods are of great interest. Current segmentation algorithms based on an Expectation- Maximization (EM) procedure present some limitations. The algorithm by Ashburner et al., 2005, does not allow multichannel inputs, e.g. two MR images of different contrast, and does not use spatial constraints between adjacent voxels, e.g. Markov random field (MRF) constraints. The solution of Van Leemput et al., 1999, employs a simplified model (mixture coefficients are not estimated and only one Gaussian is used by tissue class, with three for the image background). We have thus implemented an algorithm that combines the features of these two approaches: multichannel inputs, intensity bias correction, multi-Gaussian histogram model, and Markov random field (MRF) constraints. Our proposed method classifies tissues in three iterative main stages by way of a Generalized-EM (GEM) algorithm: (1) estimation of the Gaussian parameters modeling the histogram of the images, (2) correction of image intensity non-uniformity, and (3) modification of prior classification knowledge by MRF techniques. The goal of the GEM algorithm is to maximize the log-likelihood across the classes and voxels. Our segmentation algorithm was validated on synthetic data (with the Dice metric criterion) and real data (by a neurosurgeon) and compared to the original algorithms by Ashburner et al. and Van Leemput et al. Our combined approach leads to more robust and accurate segmentation.

Automated segmentation of MS lesions in FLAIR, DIR and T2-w MR images via an information theoretic approach

NASA Astrophysics Data System (ADS)

Hill, Jason E.; Matlock, Kevin; Pal, Ranadip; Nutter, Brian; Mitra, Sunanda

2016-03-01

Magnetic Resonance Imaging (MRI) is a vital tool in the diagnosis and characterization of multiple sclerosis (MS). MS lesions can be imaged with relatively high contrast using either Fluid Attenuated Inversion Recovery (FLAIR) or Double Inversion Recovery (DIR). Automated segmentation and accurate tracking of MS lesions from MRI remains a challenging problem. Here, an information theoretic approach to cluster the voxels in pseudo-colorized multispectral MR data (FLAIR, DIR, T2-weighted) is utilized to automatically segment MS lesions of various sizes and noise levels. The Improved Jump Method (IJM) clustering, assisted by edge suppression, is applied to the segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and MS lesions, if present, into a subset of slices determined to be the best MS lesion candidates via Otsu's method. From this preliminary clustering, the modal data values for the tissues can be determined. A Euclidean distance is then used to estimate the fuzzy memberships of each brain voxel for all tissue types and their 50/50 partial volumes. From these estimates, binary discrete and fuzzy MS lesion masks are constructed. Validation is provided by using three synthetic MS lesions brains (mild, moderate and severe) with labeled ground truths. The MS lesions of mild, moderate and severe designations were detected with a sensitivity of 83.2%, and 88.5%, and 94.5%, and with the corresponding Dice similarity coefficient (DSC) of 0.7098, 0.8739, and 0.8266, respectively. The effect of MRI noise is also examined by simulated noise and the application of a bilateral filter in preprocessing.

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

Segmentation of High Angular Resolution Diffusion MRI using Sparse Riemannian Manifold Clustering

PubMed Central

Wright, Margaret J.; Thompson, Paul M.; Vidal, René

2015-01-01

We address the problem of segmenting high angular resolution diffusion imaging (HARDI) data into multiple regions (or fiber tracts) with distinct diffusion properties. We use the orientation distribution function (ODF) to represent HARDI data and cast the problem as a clustering problem in the space of ODFs. Our approach integrates tools from sparse representation theory and Riemannian geometry into a graph theoretic segmentation framework. By exploiting the Riemannian properties of the space of ODFs, we learn a sparse representation for each ODF and infer the segmentation by applying spectral clustering to a similarity matrix built from these representations. In cases where regions with similar (resp. distinct) diffusion properties belong to different (resp. same) fiber tracts, we obtain the segmentation by incorporating spatial and user-specified pairwise relationships into the formulation. Experiments on synthetic data evaluate the sensitivity of our method to image noise and the presence of complex fiber configurations, and show its superior performance compared to alternative segmentation methods. Experiments on phantom and real data demonstrate the accuracy of the proposed method in segmenting simulated fibers, as well as white matter fiber tracts of clinical importance in the human brain. PMID:24108748

SU-F-303-12: Implementation of MR-Only Simulation for Brain Cancer: A Virtual Clinical Trial

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

Glide-Hurst, C; Zheng, W; Kim, J

2015-06-15

Purpose: To perform a retrospective virtual clinical trial using an MR-only workflow for a variety of brain cancer cases by incorporating novel imaging sequences, tissue segmentation using phase images, and an innovative synthetic CT (synCT) solution. Methods: Ten patients (16 lesions) were evaluated using a 1.0T MR-SIM including UTE-DIXON imaging (TE = 0.144/3.4/6.9ms). Bone-enhanced images were generated from DIXON-water/fat and inverted UTE. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating intersection and Dice similarity coefficients (DSC) using CT-SIM as ground truth. SynCTs were generated using voxel-based weighted summation incorporating T2, FLAIR, UTE1,more » and bone-enhanced images. Mean absolute error (MAE) characterized HU differences between synCT and CT-SIM. Dose was recalculated on synCTs; differences were quantified using planar gamma analysis (2%/2 mm dose difference/distance to agreement) at isocenter. Digitally reconstructed radiographs (DRRs) were compared. Results: On average, air maps intersected 80.8 ±5.5% (range: 71.8–88.8%) between MR-SIM and CT-SIM yielding DSCs of 0.78 ± 0.04 (range: 0.70–0.83). Whole-brain MAE between synCT and CT-SIM was 160.7±8.8 HU, with the largest uncertainty arising from bone (MAE = 423.3±33.2 HU). Gamma analysis revealed pass rates of 99.4 ± 0.04% between synCT and CT-SIM for the cohort. Dose volume histogram analysis revealed that synCT tended to yield slightly higher doses. Organs at risk such as the chiasm and optic nerves were most sensitive due to their proximities to air/bone interfaces. DRRs generated via synCT and CT-SIM were within clinical tolerances. Conclusion: Our approach for MR-only simulation for brain cancer treatment planning yielded clinically acceptable results relative to the CT-based benchmark. While slight dose differences were observed, reoptimization of treatment plans and improved image registration can address this limitation. Future work will incorporate automated registration between setup images (cone-beam CT and kilovoltage images) for synCT and CT-SIM. Submitting institution holds research agreements with Philips HealthCare, Best, Netherlands and Varian Medical Systems, Palo Alto, CA. Research partially sponsored via an Internal Mentored Research Grant.« less

Automatic detection of the hippocampal region associated with Alzheimer's disease from microscopic images of mice brain

NASA Astrophysics Data System (ADS)

Albaidhani, Tahseen; Hawkes, Cheryl; Jassim, Sabah; Al-Assam, Hisham

2016-05-01

The hippocampus is the region of the brain that is primarily associated with memory and spatial navigation. It is one of the first brain regions to be damaged when a person suffers from Alzheimer's disease. Recent research in this field has focussed on the assessment of damage to different blood vessels within the hippocampal region from a high throughput brain microscopic images. The ultimate aim of our research is the creation of an automatic system to count and classify different blood vessels such as capillaries, veins, and arteries in the hippocampus region. This work should provide biologists with efficient and accurate tools in their investigation of the causes of Alzheimer's disease. Locating the boundary of the Region of Interest in the hippocampus from microscopic images of mice brain is the first essential stage towards developing such a system. This task benefits from the variation in colour channels and texture between the two sides of the hippocampus and the boundary region. Accordingly, the developed initial step of our research to locating the hippocampus edge uses a colour-based segmentation of the brain image followed by Hough transforms on the colour channel that isolate the hippocampus region. The output is then used to split the brain image into two sides of the detected section of the boundary: the inside region and the outside region. Experimental results on a sufficiently number of microscopic images demonstrate the effectiveness of the developed solution.

Brain tumor locating in 3D MR volume using symmetry

NASA Astrophysics Data System (ADS)

Dvorak, Pavel; Bartusek, Karel

2014-03-01

This work deals with the automatic determination of a brain tumor location in 3D magnetic resonance volumes. The aim of this work is not the precise segmentation of the tumor and its parts but only the detection of its location. This work is the first step in the tumor segmentation process, an important topic in neuro-image processing. The algorithm expects 3D magnetic resonance volumes of brain containing a tumor. The detection is based on locating the area that breaks the left-right symmetry of the brain. This is done by multi-resolution comparing of corresponding regions in left and right hemisphere. The output of the computation is the probabilistic map of the tumor location. The created algorithm was tested on 80 volumes from publicly available BRATS databases containing 3D brain volumes afflicted by a brain tumor. These pathological structures had various sizes and shapes and were located in various parts of the brain. The locating performance of the algorithm was 85% for T1-weighted volumes, 91% for T1-weighted contrast enhanced volumes, 96% for FLAIR and T2-wieghted volumes and 95% for their combinations.

Hippocampus segmentation using locally weighted prior based level set

NASA Astrophysics Data System (ADS)

Achuthan, Anusha; Rajeswari, Mandava

2015-12-01

Segmentation of hippocampus in the brain is one of a major challenge in medical image segmentation due to its' imaging characteristics, with almost similar intensity between another adjacent gray matter structure, such as amygdala. The intensity similarity has causes the hippocampus to have weak or fuzzy boundaries. With this main challenge being demonstrated by hippocampus, a segmentation method that relies on image information alone may not produce accurate segmentation results. Therefore, it is needed an assimilation of prior information such as shape and spatial information into existing segmentation method to produce the expected segmentation. Previous studies has widely integrated prior information into segmentation methods. However, the prior information has been utilized through a global manner integration, and this does not reflect the real scenario during clinical delineation. Therefore, in this paper, a locally integrated prior information into a level set model is presented. This work utilizes a mean shape model to provide automatic initialization for level set evolution, and has been integrated as prior information into the level set model. The local integration of edge based information and prior information has been implemented through an edge weighting map that decides at voxel level which information need to be observed during a level set evolution. The edge weighting map shows which corresponding voxels having sufficient edge information. Experiments shows that the proposed integration of prior information locally into a conventional edge-based level set model, known as geodesic active contour has shown improvement of 9% in averaged Dice coefficient.

Localized Statistics for DW-MRI Fiber Bundle Segmentation

PubMed Central

Lankton, Shawn; Melonakos, John; Malcolm, James; Dambreville, Samuel; Tannenbaum, Allen

2013-01-01

We describe a method for segmenting neural fiber bundles in diffusion-weighted magnetic resonance images (DWMRI). As these bundles traverse the brain to connect regions, their local orientation of diffusion changes drastically, hence a constant global model is inaccurate. We propose a method to compute localized statistics on orientation information and use it to drive a variational active contour segmentation that accurately models the non-homogeneous orientation information present along the bundle. Initialized from a single fiber path, the proposed method proceeds to capture the entire bundle. We demonstrate results using the technique to segment the cingulum bundle and describe several extensions making the technique applicable to a wide range of tissues. PMID:23652079

A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction.

PubMed

Ballanger, Bénédicte; Tremblay, Léon; Sgambato-Faure, Véronique; Beaudoin-Gobert, Maude; Lavenne, Franck; Le Bars, Didier; Costes, Nicolas

2013-08-15

MRI templates and digital atlases are needed for automated and reproducible quantitative analysis of non-human primate PET studies. Segmenting brain images via multiple atlases outperforms single-atlas labelling in humans. We present a set of atlases manually delineated on brain MRI scans of the monkey Macaca fascicularis. We use this multi-atlas dataset to evaluate two automated methods in terms of accuracy, robustness and reliability in segmenting brain structures on MRI and extracting regional PET measures. Twelve individual Macaca fascicularis high-resolution 3DT1 MR images were acquired. Four individual atlases were created by manually drawing 42 anatomical structures, including cortical and sub-cortical structures, white matter regions, and ventricles. To create the MRI template, we first chose one MRI to define a reference space, and then performed a two-step iterative procedure: affine registration of individual MRIs to the reference MRI, followed by averaging of the twelve resampled MRIs. Automated segmentation in native space was obtained in two ways: 1) Maximum probability atlases were created by decision fusion of two to four individual atlases in the reference space, and transformation back into the individual native space (MAXPROB)(.) 2) One to four individual atlases were registered directly to the individual native space, and combined by decision fusion (PROPAG). Accuracy was evaluated by computing the Dice similarity index and the volume difference. The robustness and reproducibility of PET regional measurements obtained via automated segmentation was evaluated on four co-registered MRI/PET datasets, which included test-retest data. Dice indices were always over 0.7 and reached maximal values of 0.9 for PROPAG with all four individual atlases. There was no significant mean volume bias. The standard deviation of the bias decreased significantly when increasing the number of individual atlases. MAXPROB performed better when increasing the number of atlases used. When all four atlases were used for the MAXPROB creation, the accuracy of morphometric segmentation approached that of the PROPAG method. PET measures extracted either via automatic methods or via the manually defined regions were strongly correlated, with no significant regional differences between methods. Intra-class correlation coefficients for test-retest data were over 0.87. Compared to single atlas extractions, multi-atlas methods improve the accuracy of region definition. They also perform comparably to manually defined regions for PET quantification. Multiple atlases of Macaca fascicularis brains are now available and allow reproducible and simplified analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

Training models of anatomic shape variability

PubMed Central

Merck, Derek; Tracton, Gregg; Saboo, Rohit; Levy, Joshua; Chaney, Edward; Pizer, Stephen; Joshi, Sarang

2008-01-01

Learning probability distributions of the shape of anatomic structures requires fitting shape representations to human expert segmentations from training sets of medical images. The quality of statistical segmentation and registration methods is directly related to the quality of this initial shape fitting, yet the subject is largely overlooked or described in an ad hoc way. This article presents a set of general principles to guide such training. Our novel method is to jointly estimate both the best geometric model for any given image and the shape distribution for the entire population of training images by iteratively relaxing purely geometric constraints in favor of the converging shape probabilities as the fitted objects converge to their target segmentations. The geometric constraints are carefully crafted both to obtain legal, nonself-interpenetrating shapes and to impose the model-to-model correspondences required for useful statistical analysis. The paper closes with example applications of the method to synthetic and real patient CT image sets, including same patient male pelvis and head and neck images, and cross patient kidney and brain images. Finally, we outline how this shape training serves as the basis for our approach to IGRT∕ART. PMID:18777919

A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity.

PubMed

Xie, Mei; Gao, Jingjing; Zhu, Chongjin; Zhou, Yan

2015-01-01

Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulation. In this paper, we propose an interleaved method joining a modified MRF classification and bias field estimation in an energy minimization framework, whose initial estimation is based on k-means algorithm in view of prior information on MRI. The proposed method has a salient advantage of overcoming the misclassifications from the non-interleaved MRF classification for the MR image with intensity inhomogeneity. In contrast to other baseline methods, experimental results also have demonstrated the effectiveness and advantages of our algorithm via its applications in the real and the synthetic MR images.

Multislice 1H magnetic resonance spectroscopic imaging: assessment of epilepsy, Alzheimer's disease, and amyotrophic lateral sclerosis

NASA Astrophysics Data System (ADS)

Weiner, Michael W.; Maudsley, Andrew A.; Schuff, Norbert; Soher, Brian J.; Vermathen, Peter P.; Fein, George; Laxer, Kenneth D.

1998-07-01

Proton magnetic resonance spectroscopic imaging (1H MRSI) with volume pre-selection (i.e. by PRESS) or multislice 1H MRSI was used to investigate changes in brain metabolites in Alzheimer's disease, epilepsy, and amyotrophic lateral sclerosis. Examples of results from several ongoing clinical studies are provided. Multislice 1H MRSI of the human brain, without volume pre-selection offers considerable advantages over previously available techniques. Furthermore, MRI tissue segmentation and completely automated spectra curve fitting greatly facilitate quantitative data analysis. Future efforts will be devoted to obtaining full brain coverage and data acquisition at short spin echo times (TE less than 30 ms) for the detection of metabolites with short T2 relaxation times.

Cerebrovascular plaque segmentation using object class uncertainty snake in MR images

NASA Astrophysics Data System (ADS)

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

2005-04-01

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

An SPM8-based Approach for Attenuation Correction Combining Segmentation and Non-rigid Template Formation: Application to Simultaneous PET/MR Brain Imaging

PubMed Central

Izquierdo-Garcia, David; Hansen, Adam E.; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T.; Chonde, Daniel B.; Catana, Ciprian

2014-01-01

We present an approach for head MR-based attenuation correction (MR-AC) based on the Statistical Parametric Mapping (SPM8) software that combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (µ-maps) from MR data in integrated PET/MR scanners. Methods Coregistered anatomical MR and CT images acquired in 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray and white matter, cerebro-spinal fluid, bone and soft tissue, and air), which were then non-rigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomical MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients (LACs) to be used for AC of PET data. The method was validated on sixteen new subjects with brain tumors (N=12) or mild cognitive impairment (N=4) who underwent CT and PET/MR scans. The µ-maps and corresponding reconstructed PET images were compared to those obtained using the gold standard CT-based approach and the Dixon-based method available on the Siemens Biograph mMR scanner. Relative change (RC) images were generated in each case and voxel- and region of interest (ROI)-based analyses were performed. Results The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain LACs (RC=1.38%±4.52%) compared to the gold standard. Similar results (RC=1.86±4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and ROI-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87±5.0% and 2.74±2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0±10.25% and 9.38±4.97%, respectively). Areas closer to skull showed the largest improvement. Conclusion We have presented an SPM8-based approach for deriving the head µ-map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and only requires the morphological data acquired with a single MR sequence. The method is very accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. PMID:25278515