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|a Sabuncu, Mert R.
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Sabuncu, Mert R.
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|a Yeo, B. T. Thomas
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|a Van Leemput, Koen
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|a Fischl, Bruce
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|a Golland, Polina
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|a Yeo, B. T. Thomas
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|a Van Leemput, Koen
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|a Fischl, Bruce
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|a Golland, Polina
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|a Nonparametric mixture models for supervised image parcellation
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|c 2012-10-16T13:41:07Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/74007
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|a We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.
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|a National Alliance for Medical Image Computing (U.S.) (NIH NIBIB NAMIC U54-EB005149)
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|a NIH NCRR NAC P41-RR13218
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|a NIH NCRR mBIRN U24-RR021382
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|a NIH NINDS R01-NS051826
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|a National Science Foundation (U.S.) (CAREER grant 0642971)
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|a National Center for Research Resources (U.S.) (P41-RR14075)
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|a National Center for Research Resources (U.S.) (R01 RR16594-01A1)
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|a National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01 EB001550)
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|a National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)
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|a National Institute of Neurological Disorders and Stroke (U.S.) (R01 NS052585-01)
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|a en_US
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|a Article
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|t Proceedings of the Medical Image Computing and Computer Assisted Intervention (MICCAI)
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