201
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Schoemaker D, Mascret C, Collins DL, Yu E, Gauthier S, Pruessner JC. Recollection and familiarity in aging individuals: Gaining insight into relationships with medial temporal lobe structural integrity. Hippocampus 2017; 27:692-701. [PMID: 28281326 DOI: 10.1002/hipo.22725] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Revised: 02/21/2017] [Accepted: 02/23/2017] [Indexed: 11/08/2022]
Abstract
Dual-process theories posit that two separate processes are involved in recognition, namely recollection and familiarity. Studies investigating the neuroanatomical substrates of these two processes have frequently revealed that, while recollection is functionally linked with the hippocampus, familiarity appears to be associated with perirhinal and/or entorhinal cortices integrity. Interestingly these regions are known to be sensitive to normal and neuropathological aging processes. The objective of this study was to examine the effects of aging on recollection and familiarity performance, as well as to investigate associations with the rate of false alarms. In older individuals, we further aimed to explore relationships between these recognition variables and structural integrity of the hippocampus and the entorhinal and perirhinal cortices. Younger (N = 56) and older (N = 59) adults were tested on a computerized recollection and familiarity task. In a separate session, older adults (N = 56) underwent a structural MRI. Hippocampal, entorhinal and perihinal cortices volumes were automatically segmented and then manually corrected to ensure validity of the volumetric assessment. Regional volumes were normalized for total intracranial volume. While the overall recognition performance did not significantly differ across groups, our results reveal a decrease in recollection, together with an increase in familiarity in older adults. The increase reliance on familiarity was significantly and positively associated with the rate of false alarms. In the older adult sample, significant positive associations were found between recollection estimates and normalized hippocampal volumes. The normalized total hippocampal volume accounted for 25% of the variance in recollection performance. No correlation was found between any recognition variables and perirhinal or entorhinal cortices volumes. Overall, our results suggest that the age-related impairment in recollection is linked with reduced hippocampal structural integrity.
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Affiliation(s)
- Dorothee Schoemaker
- McGill University Research Center for Studies in Aging, Montreal, Quebec, Canada
| | - Charlotte Mascret
- McGill University Research Center for Studies in Aging, Montreal, Quebec, Canada
| | - D Louis Collins
- Department of Neurology/Neurosurgery, McGill University, Montreal, Quebec, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Elsa Yu
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Serge Gauthier
- McGill University Research Center for Studies in Aging, Montreal, Quebec, Canada
| | - Jens C Pruessner
- McGill University Research Center for Studies in Aging, Montreal, Quebec, Canada.,Department of Neurology/Neurosurgery, McGill University, Montreal, Quebec, Canada.,Department of Psychology, University of Constance, Constance, Germany
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202
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Zandifar A, Fonov V, Coupé P, Pruessner J, Collins DL. A comparison of accurate automatic hippocampal segmentation methods. Neuroimage 2017; 155:383-393. [PMID: 28404458 DOI: 10.1016/j.neuroimage.2017.04.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 04/06/2017] [Accepted: 04/07/2017] [Indexed: 01/26/2023] Open
Abstract
The hippocampus is one of the first brain structures affected by Alzheimer's disease (AD). While many automatic methods for hippocampal segmentation exist, few studies have compared them on the same data. In this study, we compare four fully automated hippocampal segmentation methods in terms of their conformity with manual segmentation and their ability to be used as an AD biomarker in clinical settings. We also apply error correction to the four automatic segmentation methods, and complete a comprehensive validation to investigate differences between the methods. The effect size and classification performance is measured for AD versus normal control (NC) groups and for stable mild cognitive impairment (sMCI) versus progressive mild cognitive impairment (pMCI) groups. Our study shows that the nonlinear patch-based segmentation method with error correction is the most accurate automatic segmentation method and yields the most conformity with manual segmentation (κ=0.894). The largest effect size between AD versus NC and sMCI versus pMCI is produced by FreeSurfer with error correction. We further show that, using only hippocampal volume, age, and sex as features, the area under the receiver operating characteristic curve reaches up to 0.8813 for AD versus NC and 0.6451 for sMCI versus pMCI. However, the automatic segmentation methods are not significantly different in their performance.
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Affiliation(s)
- Azar Zandifar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Vladimir Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Pierrick Coupé
- Univ. Bordeaux, LaBRI, UMR 5800, PICTURA, F-33400, Talence, France
| | - Jens Pruessner
- McGill University Research Centre for Studies in Aging, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada.
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203
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Hippocampus Segmentation Based on Local Linear Mapping. Sci Rep 2017; 7:45501. [PMID: 28368016 PMCID: PMC5377362 DOI: 10.1038/srep45501] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 03/01/2017] [Indexed: 01/18/2023] Open
Abstract
We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.
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204
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Mehta R, Majumdar A, Sivaswamy J. BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures. J Med Imaging (Bellingham) 2017; 4:024003. [PMID: 28439524 PMCID: PMC5397775 DOI: 10.1117/1.jmi.4.2.024003] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Accepted: 03/28/2017] [Indexed: 11/14/2022] Open
Abstract
Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is [Formula: see text] and [Formula: see text] for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.
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Affiliation(s)
- Raghav Mehta
- Centre for Visual Information Technology (CVIT), International Institute of Information Technology - Hyderabad (IIIT-H), Hyderabad, India
| | - Aabhas Majumdar
- Centre for Visual Information Technology (CVIT), International Institute of Information Technology - Hyderabad (IIIT-H), Hyderabad, India
| | - Jayanthi Sivaswamy
- Centre for Visual Information Technology (CVIT), International Institute of Information Technology - Hyderabad (IIIT-H), Hyderabad, India
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205
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Zhang L, Wang Q, Gao Y, Wu G, Shen D. Concatenated Spatially-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images. Neurocomputing 2017; 229:3-12. [PMID: 28133417 PMCID: PMC5268165 DOI: 10.1016/j.neucom.2016.05.082] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.
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Affiliation(s)
- Lichi Zhang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University
| | - Qian Wang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University
| | - Yaozong Gao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill; Department of Computer Science, University of North Carolina at Chapel Hill
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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206
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Dong P, Wang L, Lin W, Shen D, Wu G. Scalable Joint Segmentation and Registration Framework for Infant Brain Images. Neurocomputing 2017; 229:54-62. [PMID: 29416227 PMCID: PMC5798494 DOI: 10.1016/j.neucom.2016.05.107] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.
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Affiliation(s)
- Pei Dong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
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207
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Zhang H, Zeng D, Zhang H, Wang J, Liang Z, Ma J. Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review. Med Phys 2017; 44:1168-1185. [PMID: 28303644 PMCID: PMC5381744 DOI: 10.1002/mp.12097] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 09/12/2016] [Accepted: 12/13/2016] [Indexed: 02/03/2023] Open
Abstract
Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail.
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Affiliation(s)
- Hao Zhang
- Departments of Radiology and Biomedical EngineeringStony Brook UniversityStony BrookNY11794USA
| | - Dong Zeng
- School of Biomedical EngineeringSouthern Medical UniversityGuangzhou510515China
- Guangdong Provincial Key Laboratory of Medical Image ProcessingSouthern Medical UniversityGuangzhou510515China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection TechnologyGuangzhou510515China
| | - Hua Zhang
- School of Biomedical EngineeringSouthern Medical UniversityGuangzhou510515China
- Guangdong Provincial Key Laboratory of Medical Image ProcessingSouthern Medical UniversityGuangzhou510515China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection TechnologyGuangzhou510515China
| | - Jing Wang
- Department of Radiation OncologyUniversity of Texas Southwestern Medical CenterDallasTX75390USA
| | - Zhengrong Liang
- Departments of Radiology and Biomedical EngineeringStony Brook UniversityStony BrookNY11794USA
| | - Jianhua Ma
- School of Biomedical EngineeringSouthern Medical UniversityGuangzhou510515China
- Guangdong Provincial Key Laboratory of Medical Image ProcessingSouthern Medical UniversityGuangzhou510515China
- Guangzhou Key Laboratory of Medical Radiation Imaging and Detection TechnologyGuangzhou510515China
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208
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Zu C, Wang Z, Zhang D, Liang P, Shi Y, Shen D, Wu G. Robust multi-atlas label propagation by deep sparse representation. PATTERN RECOGNITION 2017; 63:511-517. [PMID: 27942077 PMCID: PMC5144541 DOI: 10.1016/j.patcog.2016.09.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer (label-specific dictionaries) consists of groups of representative atlas patches and the subsequent layers (residual dictionaries) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared to other counterpart label fusion methods.
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Affiliation(s)
- Chen Zu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Zhengxia Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Peipeng Liang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yonghong Shi
- Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, China
- Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention, Shanghai 200032, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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209
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Wachinger C, Reuter M, Klein T. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy. Neuroimage 2017; 170:434-445. [PMID: 28223187 DOI: 10.1016/j.neuroimage.2017.02.035] [Citation(s) in RCA: 178] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Revised: 02/13/2017] [Accepted: 02/13/2017] [Indexed: 10/20/2022] Open
Abstract
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.
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Affiliation(s)
- Christian Wachinger
- Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilian-University, Waltherstr. 23, 81369 München, Munich, Germany.
| | - Martin Reuter
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; German Centre for Neurodegenerative Diseases (DZNE), Department of Image Analysis, Bonn, Germany
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210
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Ma C, Luo G, Wang K. A Combined Random Forests and Active Contour Model Approach for Fully Automatic Segmentation of the Left Atrium in Volumetric MRI. BIOMED RESEARCH INTERNATIONAL 2017; 2017:8381094. [PMID: 28316992 PMCID: PMC5337796 DOI: 10.1155/2017/8381094] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 01/08/2017] [Accepted: 01/23/2017] [Indexed: 11/30/2022]
Abstract
Segmentation of the left atrium (LA) from cardiac magnetic resonance imaging (MRI) datasets is of great importance for image guided atrial fibrillation ablation, LA fibrosis quantification, and cardiac biophysical modelling. However, automated LA segmentation from cardiac MRI is challenging due to limited image resolution, considerable variability in anatomical structures across subjects, and dynamic motion of the heart. In this work, we propose a combined random forests (RFs) and active contour model (ACM) approach for fully automatic segmentation of the LA from cardiac volumetric MRI. Specifically, we employ the RFs within an autocontext scheme to effectively integrate contextual and appearance information from multisource images together for LA shape inferring. The inferred shape is then incorporated into a volume-scalable ACM for further improving the segmentation accuracy. We validated the proposed method on the cardiac volumetric MRI datasets from the STACOM 2013 and HVSMR 2016 databases and showed that it outperforms other latest automated LA segmentation methods. Validation metrics, average Dice coefficient (DC) and average surface-to-surface distance (S2S), were computed as 0.9227 ± 0.0598 and 1.14 ± 1.205 mm, versus those of 0.6222-0.878 and 1.34-8.72 mm, obtained by other methods, respectively.
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Affiliation(s)
- Chao Ma
- Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Gongning Luo
- Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Kuanquan Wang
- Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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211
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Chen M, Carass A, Jog A, Lee J, Roy S, Prince JL. Cross contrast multi-channel image registration using image synthesis for MR brain images. Med Image Anal 2017; 36:2-14. [PMID: 27816859 PMCID: PMC5239759 DOI: 10.1016/j.media.2016.10.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Revised: 10/13/2016] [Accepted: 10/17/2016] [Indexed: 11/21/2022]
Abstract
Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information.
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Affiliation(s)
- Min Chen
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA.
| | - Amod Jog
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA.
| | - Junghoon Lee
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
| | - Snehashis Roy
- CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA.
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA; Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
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212
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Romero JE, Coupé P, Giraud R, Ta VT, Fonov V, Park MTM, Chakravarty MM, Voineskos AN, Manjón JV. CERES: A new cerebellum lobule segmentation method. Neuroimage 2017; 147:916-924. [DOI: 10.1016/j.neuroimage.2016.11.003] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 11/04/2016] [Indexed: 01/18/2023] Open
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213
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Increased cortical capillary transit time heterogeneity in Alzheimer's disease: a DSC-MRI perfusion study. Neurobiol Aging 2017; 50:107-118. [DOI: 10.1016/j.neurobiolaging.2016.11.004] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 10/17/2016] [Accepted: 11/11/2016] [Indexed: 01/18/2023]
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214
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Song Y, Wu G, Bahrami K, Sun Q, Shen D. Progressive multi-atlas label fusion by dictionary evolution. Med Image Anal 2017; 36:162-171. [PMID: 27914302 PMCID: PMC5239730 DOI: 10.1016/j.media.2016.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 10/08/2016] [Accepted: 11/18/2016] [Indexed: 10/20/2022]
Abstract
Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain). However, due to the generally large gap between the patch appearance in the image domain and the patch structure in the label domain, the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer static dictionary to the multi-layer dynamic dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer static dictionary.
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Affiliation(s)
- Yantao Song
- School of Computer Science & Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Khosro Bahrami
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Quansen Sun
- School of Computer Science & Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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Yang X, Liu T, Dong X, Tang X, Elder E, Curran WJ, Dhabaan A. A Patch-based CBCT Scatter Artifact Correction Using Prior CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10132:1013229. [PMID: 31564764 PMCID: PMC6764528 DOI: 10.1117/12.2253935] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
We have developed a novel patch-based cone beam CT (CBCT) artifact correction method based on prior CT images. First, we used the image registration to align the planning CT with the CBCT to reduce the geometry difference between the two images. Then, we brought the planning CT-based prior information into the Bayesian deconvolution framework to perform the CBCT scatter artifact correction based on patch-wise nonlocal mean strategy. We evaluated the proposed correction method using a Catphan phantom with multiple inserts based on contrast-to-noise ratios (CNR) and signal-to-noise ratios (SNR), and the image spatial non-uniformity (ISN). All values of CNR SNR and ISN in the corrected CBCT image were much closer to those in the planning CT images. The results demonstrated that the proposed CT-guided correction method could significantly reduce scatter artifacts and improve the image quality. This method has great potential to correct CBCT images allowing its use in adaptive radiotherapy.
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Affiliation(s)
- Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Xue Dong
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Xiangyang Tang
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Eric Elder
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Anees Dhabaan
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
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216
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Platero C, Tobar MC. Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal Segmentation in Alzheimer’s Disease. Neuroinformatics 2017; 15:165-183. [DOI: 10.1007/s12021-017-9323-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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217
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Serag A, Wilkinson AG, Telford EJ, Pataky R, Sparrow SA, Anblagan D, Macnaught G, Semple SI, Boardman JP. SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests. Front Neuroinform 2017; 11:2. [PMID: 28163680 PMCID: PMC5247463 DOI: 10.3389/fninf.2017.00002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 01/05/2017] [Indexed: 11/29/2022] Open
Abstract
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.
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Affiliation(s)
- Ahmed Serag
- MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK
| | | | - Emma J Telford
- MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK
| | - Rozalia Pataky
- MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK
| | - Sarah A Sparrow
- MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK
| | - Devasuda Anblagan
- MRC Centre for Reproductive Health, University of EdinburghEdinburgh, UK; Centre for Clinical Brain Sciences, University of EdinburghEdinburgh, UK
| | - Gillian Macnaught
- Clinical Research Imaging Centre, University of Edinburgh Edinburgh, UK
| | - Scott I Semple
- Clinical Research Imaging Centre, University of EdinburghEdinburgh, UK; Centre for Cardiovascular Science, University of EdinburghEdinburgh, UK
| | - James P Boardman
- MRC Centre for Reproductive Health, University of EdinburghEdinburgh, UK; Centre for Clinical Brain Sciences, University of EdinburghEdinburgh, UK
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218
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Næss-Schmidt ET, Blicher JU, Eskildsen SF, Tietze A, Hansen B, Stubbs PW, Jespersen S, Østergaard L, Nielsen JF. Microstructural changes in the thalamus after mild traumatic brain injury: A longitudinal diffusion and mean kurtosis tensor MRI study. Brain Inj 2017; 31:230-236. [DOI: 10.1080/02699052.2016.1229034] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Erhard Trillingsgaard Næss-Schmidt
- Hammel Neurorehabilitation Centre and University Research Clinic, Aarhus, Denmark
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus, Denmark
| | - Jakob Udby Blicher
- Hammel Neurorehabilitation Centre and University Research Clinic, Aarhus, Denmark
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus, Denmark
| | - Simon Fristed Eskildsen
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus, Denmark
| | - Anna Tietze
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus, Denmark
- Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark
| | - Brian Hansen
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus, Denmark
| | - Peter William Stubbs
- Hammel Neurorehabilitation Centre and University Research Clinic, Aarhus, Denmark
- Neuroscience Research Australia, Randwick, New South Wales, Australia
| | - Sune Jespersen
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience, Institute of Clinical Medicine, Aarhus, Denmark
- Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark
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219
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Qiu W, Chen Y, Kishimoto J, de Ribaupierre S, Chiu B, Fenster A, Yuan J. Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images. Med Image Anal 2017; 35:181-191. [DOI: 10.1016/j.media.2016.06.038] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 06/28/2016] [Accepted: 06/30/2016] [Indexed: 01/26/2023]
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220
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Oktay O, Bai W, Guerrero R, Rajchl M, de Marvao A, O'Regan DP, Cook SA, Heinrich MP, Glocker B, Rueckert D. Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:332-342. [PMID: 28055830 DOI: 10.1109/tmi.2016.2597270] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D high-resolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-the-art landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy.
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221
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Abstract
Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer's disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.
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Affiliation(s)
- Hancan Zhu
- School of Mathematics Physics and Information, Shaoxing University, Shaoxing, 312000, China
| | - Hewei Cheng
- Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Xuesong Yang
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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222
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Sørensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, Nielsen M. Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NEUROIMAGE-CLINICAL 2016; 13:470-482. [PMID: 28119818 PMCID: PMC5237821 DOI: 10.1016/j.nicl.2016.11.025] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2016] [Revised: 10/21/2016] [Accepted: 11/26/2016] [Indexed: 01/01/2023]
Abstract
This paper presents a brain T1-weighted structural magnetic resonance imaging (MRI) biomarker that combines several individual MRI biomarkers (cortical thickness measurements, volumetric measurements, hippocampal shape, and hippocampal texture). The method was developed, trained, and evaluated using two publicly available reference datasets: a standardized dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the imaging arm of the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL). In addition, the method was evaluated by participation in the Computer-Aided Diagnosis of Dementia (CADDementia) challenge. Cross-validation using ADNI and AIBL data resulted in a multi-class classification accuracy of 62.7% for the discrimination of healthy normal controls (NC), subjects with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD). This performance generalized to the CADDementia challenge where the method, trained using the ADNI and AIBL data, achieved a classification accuracy 63.0%. The obtained classification accuracy resulted in a first place in the challenge, and the method was significantly better (McNemar's test) than the bottom 24 methods out of the total of 29 methods contributed by 15 different teams in the challenge. The method was further investigated with learning curve and feature selection experiments using ADNI and AIBL data. The learning curve experiments suggested that neither more training data nor a more complex classifier would have improved the obtained results. The feature selection experiment showed that both common and uncommon individual MRI biomarkers contributed to the performance; hippocampal volume, ventricular volume, hippocampal texture, and parietal lobe thickness were the most important. This study highlights the need for both subtle, localized measurements and global measurements in order to discriminate NC, MCI, and AD simultaneously based on a single structural MRI scan. It is likely that additional non-structural MRI features are needed to further improve the obtained performance, especially to improve the discrimination between NC and MCI. The algorithm that won the CADDementia challenge is described and analyzed. Evaluation on data from ADNI, AIBL and the CADDementia challenge. Hippocampal texture is shown to be an important feature in the algorithm. Structural MRI intensity variations may include so far unused information. It is conjectured that additional features are needed in order to improve diagnostic performance.
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Affiliation(s)
- Lauge Sørensen
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Christian Igel
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark
| | - Akshay Pai
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Ioana Balas
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark
| | | | - Martin Lillholm
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Copenhagen Ø DK-2100, Denmark; Biomediq A/S, Copenhagen Ø DK-2100, Denmark
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223
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Atehortúa A, Zuluaga MA, García JD, Romero E. Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis. Med Phys 2016; 43:6270. [PMID: 27908177 DOI: 10.1118/1.4966133] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
PURPOSE Accurate measurement of the right ventricle (RV) volume is important for the assessment of the ventricular function and a biomarker of the progression of any cardiovascular disease. However, the high RV variability makes difficult a proper delineation of the myocardium wall. This paper introduces a new automatic method for segmenting the RV volume from short axis cardiac magnetic resonance (MR) images by a salient analysis of temporal and spatial observations. METHODS The RV volume estimation starts by localizing the heart as the region with the most coherent motion during the cardiac cycle. Afterward, the ventricular chambers are identified at the basal level using the isodata algorithm, the right ventricle extracted, and its centroid computed. A series of radial intensity profiles, traced from this centroid, is used to search a salient intensity pattern that models the inner-outer myocardium boundary. This process is iteratively applied toward the apex, using the segmentation of the previous slice as a regularizer. The consecutive 2D segmentations are added together to obtain the final RV endocardium volume that serves to estimate also the epicardium. RESULTS Experiments performed with a public dataset, provided by the RV segmentation challenge in cardiac MRI, demonstrated that this method is highly competitive with respect to the state of the art, obtaining a Dice score of 0.87, and a Hausdorff distance of 7.26 mm while a whole volume was segmented in about 3 s. CONCLUSIONS The proposed method provides an useful delineation of the RV shape using only the spatial and temporal information of the cine MR images. This methodology may be used by the expert to achieve cardiac indicators of the right ventricle function.
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Affiliation(s)
| | - Maria A Zuluaga
- Universidad Nacional de Colombia, Bogotá 111321, Colombia and Translational Imaging Group, Centre for Medical Image Computing, University College London, NW1 2PS, United Kingdom
| | - Juan D García
- Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Eduardo Romero
- Universidad Nacional de Colombia, Bogotá 111321, Colombia
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224
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Cordier N, Delingette H, Le M, Ayache N. Extended Modality Propagation: Image Synthesis of Pathological Cases. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2598-2608. [PMID: 27411217 DOI: 10.1109/tmi.2016.2589760] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper describes a novel generative model for the synthesis of multi-modal medical images of pathological cases based on a single label map. Our model builds upon i) a generative model commonly used for label fusion and multi-atlas patch-based segmentation of healthy anatomical structures, ii) the Modality Propagation iterative strategy used for a spatially-coherent synthesis of subject-specific scans of desired image modalities. The expression Extended Modality Propagation is coined to refer to the extension of Modality Propagation to the synthesis of images of pathological cases. Moreover, image synthesis uncertainty is estimated. An application to Magnetic Resonance Imaging synthesis of glioma-bearing brains is i) validated on the training dataset of a Multimodal Brain Tumor Image Segmentation challenge, ii) compared to the state-of-the-art in glioma image synthesis, and iii) illustrated using the output of two different tumor growth models. Such a generative model allows the generation of a large dataset of synthetic cases, which could prove useful for the training, validation, or benchmarking of image processing algorithms.
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225
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Ma G, Gao Y, Wu G, Wu L, Shen D. Nonlocal atlas-guided multi-channel forest learning for human brain labeling. Med Phys 2016; 43:1003-19. [PMID: 26843260 DOI: 10.1118/1.4940399] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). METHODS In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. RESULTS The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. CONCLUSIONS The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature.
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Affiliation(s)
- Guangkai Ma
- Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China and Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Yaozong Gao
- Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Guorong Wu
- Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Ligang Wu
- Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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226
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Planche V, Ruet A, Coupé P, Lamargue-Hamel D, Deloire M, Pereira B, Manjon JV, Munsch F, Moscufo N, Meier DS, Guttmann CR, Dousset V, Brochet B, Tourdias T. Hippocampal microstructural damage correlates with memory impairment in clinically isolated syndrome suggestive of multiple sclerosis. Mult Scler 2016; 23:1214-1224. [PMID: 27780913 DOI: 10.1177/1352458516675750] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE We investigated whether diffusion tensor imaging (DTI) could reveal early hippocampal damage and clinically relevant correlates of memory impairment in persons with clinically isolated syndrome (CIS) suggestive of multiple sclerosis (MS). METHODS A total of 37 persons with CIS, 32 with MS and 36 controls prospectively included from 2011 to 2014 were tested for cognitive performances and scanned with 3T-magnetic resonance imaging (MRI) to assess volumetric and DTI changes within the hippocampus, whole brain volume and T2-lesion load. RESULTS While there was no hippocampal atrophy in the CIS group, hippocampal fractional anisotropy (FA) was significantly decreased compared to controls. Decrease in hippocampal FA together with increased mean diffusivity (MD) was even more prominent in MS patients. In CIS, hippocampal MD was correlated with episodic verbal memory performance ( r = -0.57, p = 0.0002 and odds ratio (OR) = 0.058, 95% confidence interval (CI) = 0.0057-0.59, p = 0.016 adjusted for age, gender, depression and T2-lesion load), but not with cognitive tasks unrelated to hippocampal functions. Hippocampal MD was the only variable discriminating memory-impaired from memory-preserved persons with CIS (area under the curve (AUC) = 0.77, sensitivity = 90.0%, specificity = 70.3%, positive predictive value (PPV) = 52.9%, negative predictive value (NPV) = 95.0%). CONCLUSION DTI alterations within the hippocampus might reflect early neurodegenerative processes that are correlated with episodic memory performance, discriminating persons with CIS according to their memory status.
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Affiliation(s)
- Vincent Planche
- Universite de Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/Centre Hospitalier Universitaire de Clermont-Ferrand, Clermont-Ferrand, France
| | - Aurélie Ruet
- Universite de Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique (LaBRI), Talence, France
| | - Delphine Lamargue-Hamel
- Universite de Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France
| | - Mathilde Deloire
- Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France
| | - Bruno Pereira
- Centre Hospitalier Universitaire de Clermont-Ferrand, Clermont-Ferrand, France
| | - José V Manjon
- Universitat Politècnica de València, Valencia, Spain
| | - Fanny Munsch
- Universite de Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France
| | - Nicola Moscufo
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Dominik S Meier
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Charles Rg Guttmann
- Universite de Bordeaux, Bordeaux, France/Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Vincent Dousset
- Universite de Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France
| | - Bruno Brochet
- Universite de Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France
| | - Thomas Tourdias
- Universite de Bordeaux, Bordeaux, France/Inserm U1215, Neurocentre Magendie, Bordeaux, France/Centre Hospitalier Universitaire (CHU) de Bordeaux, Bordeaux, France
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227
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Huo Y, Asman AJ, Plassard AJ, Landman BA. Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion. Hum Brain Mapp 2016; 38:599-616. [PMID: 27726243 DOI: 10.1002/hbm.23432] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 08/02/2016] [Accepted: 10/01/2016] [Indexed: 01/09/2023] Open
Abstract
Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of brain tissues. None of the three provide explicit skull/CSF boundary (SCB) since it is challenging to distinguish these dark structures in a T1-weighted image. However, explicit SCB not only leads to a natural way of obtaining TICV (i.e., counting voxels inside the skull) but also allows sub-definition of TICV, for example, the posterior fossa volume (PFV). In this article, they proposed to use multi-atlas label fusion to obtain TICV and PFV simultaneously. The main contributions are: (1) TICV and PFV are simultaneously obtained with explicit SCB from a single T1-weighted image. (2) TICV and PFV labels are added to the widely used BrainCOLOR atlases. (3) Detailed mathematical derivation of non-local spatial STAPLE (NLSS) label fusion is presented. As the skull is clearly distinguished in CT images, we use a semi-manual procedure to obtain atlases with TICV and PFV labels using 20 subjects who both have a MR and CT scan. The proposed method provides simultaneous TICV and PFV estimation while achieving more accurate TICV estimation compared with FreeSurfer, FSL, SPM12, and the previously proposed STAPLE based approach. The newly developed TICV and PFV labels for the OASIS BrainCOLOR atlases provide acceptable performance, which enables simultaneous TICV and PFV estimation during whole brain segmentation. The NLSS method and the new atlases have been made freely available. Hum Brain Mapp 38:599-616, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Yuankai Huo
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee
| | - Andrew J Asman
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee
| | | | - Bennett A Landman
- Electrical Engineering, Vanderbilt University, Nashville, Tennessee.,Computer Science, Vanderbilt University, Nashville, Tennessee.,Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.,Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee.,Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee
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228
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Hosseini MP, Nazem-Zadeh MR, Pompili D, Jafari-Khouzani K, Elisevich K, Soltanian-Zadeh H. Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients. Med Phys 2016; 43:538. [PMID: 26745947 DOI: 10.1118/1.4938411] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. METHODS A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. RESULTS Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland-Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others. CONCLUSIONS The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.
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Affiliation(s)
- Mohammad-Parsa Hosseini
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854 and Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202
| | - Mohammad-Reza Nazem-Zadeh
- Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202
| | - Dario Pompili
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854
| | - Kourosh Jafari-Khouzani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02129
| | - Kost Elisevich
- Department of Clinical Neuroscience, Spectrum Health System, Grand Rapids, Michigan 49503 and Division of Neurosurgery, College of Human Medicine, Michigan State University, Grand Rapids, Michigan 49503
| | - Hamid Soltanian-Zadeh
- Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202; Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, Iran; and School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran 1954856316, Iran
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Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning. PATCH-BASED TECHNIQUES IN MEDICAL IMAGING : SECOND INTERNATIONAL WORKSHOP, PATCH-MI 2016, HELD IN CONJUNCTION WITH MICCAI 2016, ATHENS, GREECE, OCTOBER 17, 2016 : PROCEEDINGS. PATCH-MI (WORKSHOP) (2ND : 2016 : ATHENS, GREECE) 2016; 9993:1-8. [PMID: 30246179 DOI: 10.1007/978-3-319-47118-1_1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Accurate segmentation of infant hippocampus from Magnetic Resonance (MR) images is one of the key steps for the investigation of early brain development and neurological disorders. Since the manual delineation of anatomical structures is time-consuming and irreproducible, a number of automatic segmentation methods have been proposed, such as multi-atlas patch-based label fusion methods. However, the hippocampus during the first year of life undergoes dynamic appearance, tissue contrast and structural changes, which pose substantial challenges to the existing label fusion methods. In addition, most of the existing label fusion methods generally segment target images at each time-point independently, which is likely to result in inconsistent hippocampus segmentation results along different time-points. In this paper, we treat a longitudinal image sequence as a whole, and propose a spatial-temporal hypergraph based model to jointly segment infant hippocampi from all time-points. Specifically, in building the spatial-temporal hypergraph, (1) the atlas-to-target relationship and (2) the spatial/temporal neighborhood information within the target image sequence are encoded as two categories of hyperedges. Then, the infant hippocampus segmentation from the whole image sequence is formulated as a semi-supervised label propagation model using the proposed hypergraph. We evaluate our method in segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that, by leveraging spatial-temporal information, our method achieves better performance in both segmentation accuracy and consistency over the state-of-the-art multi-atlas label fusion methods.
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Kim M, Wu G, Rekik I, Shen D. Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2016; 10019:69-76. [PMID: 28603790 PMCID: PMC5464755 DOI: 10.1007/978-3-319-47157-0_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The growing collection of longitudinal images for brain disease diagnosis necessitates the development of advanced longitudinal registration and anatomical labeling methods that can respect temporal consistency between images. However, the characteristics of such longitudinal images and how they lodge into the image manifold are often neglected in existing labeling methods. Indeed, most of them independently align atlases to each target time-point image for propagating the pre-defined atlas labels to the subject domain. In this paper, we present a dual-layer groupwise registration method to consistently label anatomical regions of interest in brain images across different time-points using a multi-atlases-based labeling framework. Our framework can best enhance the labeling of longitudinal images through: (1) using the group mean of the longitudinal images of each subject (i.e., subject-mean) as a bridge between atlases and the longitudinal subject scans to align atlases to all time-point images jointly; and (2) using inter-atlas relationship in their nesting manifold to better register each atlas image to the subject-mean. These steps yield to a more consistent (from the joint alignment of atlases with all time-point images) and more accurate (from the manifold-guided registration between each atlases and the subject-mean image) registration, thereby eventually improving the consistency and accuracy for the subsequent labeling step. We have tested our dual-layer groupwise registration method to label two challenging longitudinal brain datasets (i.e., healthy infants and Alzheimer's disease subjects). Our experimental results have showed that our method achieves higher labeling accuracy while keeping the labeling consistency over time, when compared to the traditional registration scheme (without our proposed contributions). Moreover, the proposed framework can flexibly integrate with the existing label fusion methods, such as sparse-patch based methods, to improve the labeling accuracy of longitudinal datasets.
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Affiliation(s)
- Minjeong Kim
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Isrem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
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Wu Z, Park SH, Guo Y, Gao Y, Shen D. Regression Guided Deformable Models for Segmentation of Multiple Brain ROIs. MACHINE LEARNING IN MEDICAL IMAGING. MLMI (WORKSHOP) 2016; 10019:237-245. [PMID: 28603792 PMCID: PMC5464596 DOI: 10.1007/978-3-319-47157-0_29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper proposes a novel method of using regression-guided deformable models for brain regions of interest (ROIs) segmentation. Different from conventional deformable segmentation, which often deforms shape model locally and thus sensitive to initialization, we propose to learn a regressor to explicitly guide the shape deformation, thus eventually improves the performance of ROI segmentation. The regressor is learned via two steps, (1) a joint classification and regression random forest (CRRF) and (2) an auto-context model. The CRRF predicts each voxel's deformation to the nearest point on the ROI boundary as well as each voxel's class label (e.g., ROI versus background). The auto-context model further refines all voxel's deformations (i.e., deformation field) and class labels (i.e., label maps) by considering the neighboring structures. Compared to the conventional random forest regressor, the proposed regressor provides more accurate deformation field estimation and thus more robust in guiding deformation of the shape model. Validated in segmentation of 14 midbrain ROIs from the IXI dataset, our method outperforms the state-of-art multi-atlas label fusion and classification methods, and also significantly reduces the computation cost.
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Affiliation(s)
- Zhengwang Wu
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
| | - Sang Hyun Park
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
| | - Yanrong Guo
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
| | - Yaozong Gao
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
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Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning. PATCH-BASED TECHNIQUES IN MEDICAL IMAGING : SECOND INTERNATIONAL WORKSHOP, PATCH-MI 2016, HELD IN CONJUNCTION WITH MICCAI 2016, ATHENS, GREECE, OCTOBER 17, 2016 : PROCEEDINGS. PATCH-MI (WORKSHOP) (2ND : 2016 : ATHENS, GREECE) 2016. [PMID: 29594262 DOI: 10.1007/978-3-319-47118-1_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
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.
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A review on brain structures segmentation in magnetic resonance imaging. Artif Intell Med 2016; 73:45-69. [DOI: 10.1016/j.artmed.2016.09.001] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 07/27/2016] [Accepted: 09/05/2016] [Indexed: 11/18/2022]
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Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction. ACTA ACUST UNITED AC 2016; 9968:146-156. [PMID: 28367541 DOI: 10.1007/978-3-319-46630-9_15] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize T2-w whole head (including brain, skull, eyes etc) images from T1-w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous B0 magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T2-w image. We show that our synthetic T2-w images can be used as a template in absence of a real T2-w image. Our patch based method requires multiple atlases with T1 and T2 to be registeLowRes to a given target T1. Then for every patch on the target, multiple similar looking matching patches are found on the atlas T1 images and corresponding patches on the atlas T2 images are combined to generate a synthetic T2 of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T2 images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.
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Hierarchical Multi-Atlas Segmentation Using Label-Specific Embeddings, Target-Specific Templates and Patch Refinement. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-47118-1_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
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Puonti O, Iglesias JE, Van Leemput K. Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. Neuroimage 2016; 143:235-249. [PMID: 27612647 DOI: 10.1016/j.neuroimage.2016.09.011] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 09/02/2016] [Accepted: 09/05/2016] [Indexed: 12/18/2022] Open
Abstract
Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data.
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Affiliation(s)
- Oula Puonti
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark.
| | - Juan Eugenio Iglesias
- Basque Center on Cognition, Brain and Language (BCBL), Paseo Mikeletegi, 20009 San Sebastian - Donostia, Gipuzkoa, Spain; Department of Medical Physics and Biomedical Engineering, University College London, Gower St, London WC1E 6BT, United Kingdom
| | - Koen Van Leemput
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby, Denmark; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Charlestown, MA 02129, USA
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A fast approach for hippocampal segmentation from T1-MRI for predicting progression in Alzheimer's disease from elderly controls. J Neurosci Methods 2016; 270:61-75. [DOI: 10.1016/j.jneumeth.2016.06.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 06/14/2016] [Accepted: 06/15/2016] [Indexed: 01/08/2023]
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241
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Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev 2016; 30:25-48. [PMID: 26827786 DOI: 10.1016/j.arr.2016.01.002] [Citation(s) in RCA: 445] [Impact Index Per Article: 55.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 01/22/2023]
Abstract
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
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Affiliation(s)
- Lorenzo Pini
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Martina Bocchetta
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Daniele Altomare
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Bosco
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Enrica Cavedo
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Hôpital de la Pitié-Salpétrière Paris & CATI Multicenter Neuroimaging Platform, France
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Moira Marizzoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.
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Manjón JV, Coupé P. volBrain: An Online MRI Brain Volumetry System. Front Neuroinform 2016; 10:30. [PMID: 27512372 PMCID: PMC4961698 DOI: 10.3389/fninf.2016.00030] [Citation(s) in RCA: 317] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Accepted: 07/11/2016] [Indexed: 01/18/2023] Open
Abstract
The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neurological diseases. This field has recently experienced many advances with successful techniques based on non-linear warping and label fusion. In this work we present a novel and fully automatic pipeline for volumetric brain analysis based on multi-atlas label fusion technology that is able to provide accurate volumetric information at different levels of detail in a short time. This method is available through the volBrain online web interface (http://volbrain.upv.es), which is publically and freely accessible to the scientific community. Our new framework has been compared with current state-of-the-art methods showing very competitive results.
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Affiliation(s)
- José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València Valencia, Spain
| | - Pierrick Coupé
- Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche ScientifiqueTalence, France; Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, University BordeauxTalence, France
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Bhagwat N, Pipitone J, Winterburn JL, Guo T, Duerden EG, Voineskos AN, Lepage M, Miller SP, Pruessner JC, Chakravarty MM. Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion. Front Neurosci 2016; 10:325. [PMID: 27486386 PMCID: PMC4949270 DOI: 10.3389/fnins.2016.00325] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 06/28/2016] [Indexed: 01/08/2023] Open
Abstract
Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method—Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)—that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.
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Affiliation(s)
- Nikhil Bhagwat
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada
| | - Jon Pipitone
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health Toronto, ON, Canada
| | - Julie L Winterburn
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada
| | - Ting Guo
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Emma G Duerden
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada; Department of Psychiatry, University of TorontoToronto, ON, Canada
| | - Martin Lepage
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada
| | - Steven P Miller
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Jens C Pruessner
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; McGill Centre for Studies in AgingMontreal, QC, Canada
| | - M Mallar Chakravarty
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada; Biological and Biomedical Engineering, McGill UniversityMontreal, QC, Canada
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Liao S, Zhan Y, Dong Z, Yan R, Gong L, Zhou XS, Salganicoff M, Fei J. Automatic Lumbar Spondylolisthesis Measurement in CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1658-1669. [PMID: 26849859 DOI: 10.1109/tmi.2016.2523452] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Lumbar spondylolisthesis is one of the most common spinal diseases. It is caused by the anterior shift of a lumbar vertebrae relative to subjacent vertebrae. In current clinical practices, staging of spondylolisthesis is often conducted in a qualitative way. Although meyerding grading opens the door to stage spondylolisthesis in a more quantitative way, it relies on the manual measurement, which is time consuming and irreproducible. Thus, an automatic measurement algorithm becomes desirable for spondylolisthesis diagnosis and staging. However, there are two challenges. 1) Accurate detection of the most anterior and posterior points on the superior and inferior surfaces of each lumbar vertebrae. Due to the small size of the vertebrae, slight errors of detection may lead to significant measurement errors, hence, wrong disease stages. 2) Automatic localize and label each lumbar vertebrae is required to provide the semantic meaning of the measurement. It is difficult since different lumbar vertebraes have high similarity of both shape and image appearance. To resolve these challenges, a new auto measurement framework is proposed with two major contributions: First, a learning based spine labeling method that integrates both the image appearance and spine geometry information is designed to detect lumbar vertebrae. Second, a hierarchical method using both the population information from atlases and domain-specific information in the target image is proposed for most anterior and posterior points positioning. Validated on 258 CT spondylolisthesis patients, our method shows very similar results to manual measurements by radiologists and significantly increases the measurement efficiency.
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Adeli E, Lalush DS. Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:3303-3315. [PMID: 27187957 PMCID: PMC5106345 DOI: 10.1109/tip.2016.2567072] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Positron emission tomography (PET) images are widely used in many clinical applications, such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both S-PET and low-dose PET data into a common space and then performing patch-based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level canonical correlation analysis scheme to solve this problem. In particular, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. In addition, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain data sets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.
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247
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Bao S, Chung ACS. Multi-scale structured CNN with label consistency for brain MR image segmentation. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1182072] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Siqi Bao
- Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong,
| | - Albert C. S. Chung
- Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong,
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Næss-Schmidt E, Tietze A, Blicher JU, Petersen M, Mikkelsen IK, Coupé P, Manjón JV, Eskildsen SF. Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification. Int J Comput Assist Radiol Surg 2016; 11:1979-1991. [PMID: 27325140 DOI: 10.1007/s11548-016-1433-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 05/27/2016] [Indexed: 01/18/2023]
Abstract
PURPOSE In both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation tools and their impact on diffusion quantification, emphasizing the importance of using recently developed segmentation algorithms and imaging techniques. METHODS Four publicly available, automatic segmentation methods (volBrain, FSL, FreeSurfer and SPM) are compared to manual segmentation of the thalamus and hippocampus imaged with a recently proposed T1-weighted MRI sequence (MP2RAGE). We evaluate morphometric accuracy on 22 healthy subjects and impact on diffusivity measurements obtained from aligned diffusion-weighted images on a subset of 10 subjects. RESULTS Compared to manual segmentation, the highest Dice similarity index of the thalamus is obtained with volBrain using a local library ([Formula: see text], [Formula: see text]) followed by volBrain using an external library ([Formula: see text], [Formula: see text]), FSL ([Formula: see text], [Formula: see text]), FreeSurfer ([Formula: see text], [Formula: see text]) and SPM ([Formula: see text], [Formula: see text]). The same order is found for hippocampus with volBrain local ([Formula: see text], [Formula: see text]), volBrain external ([Formula: see text], [Formula: see text]), FSL ([Formula: see text], [Formula: see text]), FreeSurfer ([Formula: see text], [Formula: see text]) and SPM ([Formula: see text], [Formula: see text]). For diffusivity measurements, volBrain provides values closest to those obtained from manual segmentations. volBrain is the only method where FA values do not differ significantly from manual segmentation of the thalamus. CONCLUSIONS Overall we find that volBrain is superior in thalamus and hippocampus segmentation compared to FSL, FreeSurfer and SPM. Furthermore, the choice of segmentation technique and training library affects quantitative results from diffusivity measures in thalamus and hippocampus.
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Affiliation(s)
- Erhard Næss-Schmidt
- Hammel Neurorehabilitation Centre and University Research Clinic, Aarhus University, Voldbyvej 15, 8460, Hammel, Denmark. .,Hammel Neurorehabilitation Centre and University Research Clinic, Voldbyvej 15, 8460, Hammel, Denmark.
| | - Anna Tietze
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark.,Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark
| | - Jakob Udby Blicher
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark
| | - Mikkel Petersen
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark
| | - Irene K Mikkelsen
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark
| | - Pierrick Coupé
- Laboratoire Bordelais de Recherche en Informatique, Unité Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, 351, cours de la Libération, 33405, Talence cedex, France
| | - José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain
| | - Simon Fristed Eskildsen
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University, Aarhus, Denmark
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249
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Lampert TA, Stumpf A, Gancarski P. An Empirical Study Into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:2557-2572. [PMID: 27019487 DOI: 10.1109/tip.2016.2544703] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Although agreement between the annotators who mark feature locations within images has been studied in the past from a statistical viewpoint, little work has attempted to quantify the extent to which this phenomenon affects the evaluation of foreground-background segmentation algorithms. Many researchers utilize ground truth (GT) in experimentation and more often than not this GT is derived from one annotator's opinion. How does the difference in opinion affects an algorithm's evaluation? A methodology is applied to four image-processing problems to quantify the interannotator variance and to offer insight into the mechanisms behind agreement and the use of GT. It is found that when detecting linear structures, annotator agreement is very low. The agreement in a structure's position can be partially explained through basic image properties. Automatic segmentation algorithms are compared with annotator agreement and it is found that there is a clear relation between the two. Several GT estimation methods are used to infer a number of algorithm performances. It is found that the rank of a detector is highly dependent upon the method used to form the GT, and that although STAPLE and LSML appear to represent the mean of the performance measured using individual annotations, when there are few annotations, or there is a large variance in them, these estimates tend to degrade. Furthermore, one of the most commonly adopted combination methods-consensus voting-accentuates more obvious features, resulting in an overestimation of performance. It is concluded that in some data sets, it is not possible to confidently infer an algorithm ranking when evaluating upon one GT.
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250
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Wu Y, Yang W, Lu L, Lu Z, Zhong L, Huang M, Feng Y, Feng Q, Chen W. Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction. J Nucl Med 2016; 57:1635-1641. [PMID: 27230932 DOI: 10.2967/jnumed.115.163121] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Accepted: 04/16/2016] [Indexed: 11/16/2022] Open
Affiliation(s)
- Yao Wu
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wei Yang
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Lijun Lu
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhentai Lu
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Liming Zhong
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Meiyan Huang
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yanqiu Feng
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianjin Feng
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wufan Chen
- Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China
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