A generative model for image segmentation based on label fusion
We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/64791 https://orcid.org/0000-0002-5002-1227 https://orcid.org/0000-0003-2516-731X |
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| author | Sabuncu, Mert R. Yeo, Boon Thye Thomas Fischl, Bruce Van Leemput, Koen Golland, Polina |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Sabuncu, Mert R. Yeo, Boon Thye Thomas Fischl, Bruce Van Leemput, Koen Golland, Polina |
| author_sort | Sabuncu, Mert R. |
| collection | MIT |
| description | We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans - with manually segmented white matter, cerebral cortex, ventricles and subcortical structures - to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease. |
| first_indexed | 2024-09-23T16:34:44Z |
| format | Article |
| id | mit-1721.1/64791 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:34:44Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/647912022-10-02T08:19:23Z A generative model for image segmentation based on label fusion Sabuncu, Mert R. Yeo, Boon Thye Thomas Fischl, Bruce Van Leemput, Koen Golland, Polina Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Golland, Polina Golland, Polina Van Leemput, Koen Sabuncu, Mert R. Yeo, Boon Thye Thomas Fischl, Bruce We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans - with manually segmented white matter, cerebral cortex, ventricles and subcortical structures - to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease. National Alliance for Medical Image Computing (U.S.) (NIH NIBIB NAMIC U54-EB005149) National Alliance for Medical Image Computing (U.S.) (NAC NIH NCRR NAC P41-RR13218) National Institutes of Health (U.S.) (mBIRN NIH NCRR mBIRN U24-RR021382) National Institutes of Health (U.S.) (NIH NINDS R01-NS051826 grant) National Science Foundation (U.S.) (NSF CAREER 0642971 grant) National Center for Research Resources (U.S.) (P41-RR14075, R01 RR16594-01A1) National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01 EB001550, R01EB006758) National Institute of Neurological Disorders and Stroke (U.S.) (R01 NS052585-01) Mind Research Institute Ellison Medical Foundation (Autism and Dyslexia Project) Singapore. Agency for Science, Technology and Research Academy of Finland (grant number 133611) 2011-07-13T18:13:40Z 2011-07-13T18:13:40Z 2010-09 2010-04 Article http://purl.org/eprint/type/ConferencePaper 0278-0062 INSPEC Accession Number: 11534908 http://hdl.handle.net/1721.1/64791 Sabuncu, M.R. et al. “A Generative Model for Image Segmentation Based on Label Fusion.” Medical Imaging, IEEE Transactions On 29.10 (2010) : 1714-1729.© 2010 IEEE. https://orcid.org/0000-0002-5002-1227 https://orcid.org/0000-0003-2516-731X en_US http://dx.doi.org/ 10.1109/TMI.2010.2050897 IEEE Transactions on Medical Imaging, 2010 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers MIT web domain |
| spellingShingle | Sabuncu, Mert R. Yeo, Boon Thye Thomas Fischl, Bruce Van Leemput, Koen Golland, Polina A generative model for image segmentation based on label fusion |
| title | A generative model for image segmentation based on label fusion |
| title_full | A generative model for image segmentation based on label fusion |
| title_fullStr | A generative model for image segmentation based on label fusion |
| title_full_unstemmed | A generative model for image segmentation based on label fusion |
| title_short | A generative model for image segmentation based on label fusion |
| title_sort | generative model for image segmentation based on label fusion |
| url | http://hdl.handle.net/1721.1/64791 https://orcid.org/0000-0002-5002-1227 https://orcid.org/0000-0003-2516-731X |
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