A generative probability model of joint label fusion for multi-atlas based brain segmentation |
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| Affiliation: | 1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA;2. Department of Computer Science, University of North Carolina at Chapel Hill, USA;3. Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;4. Department of Computer Science and Engineering, University of Texas, Arlington, USA;1. Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;2. Department of Computer Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;3. Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;1. Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;2. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States;3. Digital Medical Research Center, Shanghai Key Lab of MICCAI, School of Basic Medical Sciences, Fudan University, Shanghai, China;4. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;1. Centre for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China;2. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA;3. Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China;4. Guangzhou Psychiatry Hospital, Guangzhou, China;5. Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;6. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea;1. School of Computer, National University of Defense Technology, Changsha 410073, China;2. School of Computer Science and Software Engineering, University of Wollongong, NSW 2522, Australia;3. Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia;4. The Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA;5. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea;1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA;2. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea |
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| Abstract: | Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling has been widely investigated to alleviate the possible mis-alignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these limitations, we propose a generative probability model to describe the procedure of label fusion in a multi-atlas scenario, for the goal of labeling each point in the target image by the best representative atlas patches that also have the largest labeling unanimity in labeling the underlying point correctly. Specifically, sparsity constraint is imposed upon label fusion weights, in order to select a small number of atlas patches that best represent the underlying target patch, thus reducing the risks of including the misleading atlas patches. The labeling unanimity among atlas patches is achieved by exploring their dependencies, where we model these dependencies as the joint probability of each pair of atlas patches in correctly predicting the labels, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. The patch dependencies will be further recursively updated based on the latest labeling results to correct the possible labeling errors, which falls to the Expectation Maximization (EM) framework. To demonstrate the labeling performance, we have comprehensively evaluated our patch-based labeling method on the whole brain parcellation and hippocampus segmentation. Promising labeling results have been achieved with comparison to the conventional patch-based labeling method, indicating the potential application of the proposed method in the future clinical studies. |
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| Keywords: | Patch-based labeling Generative probability model Multi-atlas based segmentation Sparse representation |
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