Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that lev...
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| Format: | Article |
| Language: | English |
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Springer International Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/129557 |
| _version_ | 1811088195297738752 |
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| author | Dalca, Adrian Vasile Golland, Polina Iglesias Gonzalez, Juan Eugenio |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Dalca, Adrian Vasile Golland, Polina Iglesias Gonzalez, Juan Eugenio |
| author_sort | Dalca, Adrian Vasile |
| collection | MIT |
| description | Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning segmentation tools that are computationally efficient at test time. However, most of these strategies rely on supervised learning from manually annotated images and are therefore sensitive to the intensity profiles in the training dataset. A deep learning-based segmentation model for a new image dataset (e.g., of different contrast), usually requires a new labeled training dataset, which can be prohibitively expensive, or suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines conventional probabilistic atlas-based segmentation with deep learning, enabling training of a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 s at test time on a GPU. |
| first_indexed | 2024-09-23T13:57:46Z |
| format | Article |
| id | mit-1721.1/129557 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:57:46Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1295572022-09-28T17:26:24Z Unsupervised Deep Learning for Bayesian Brain MRI Segmentation Dalca, Adrian Vasile Golland, Polina Iglesias Gonzalez, Juan Eugenio Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning segmentation tools that are computationally efficient at test time. However, most of these strategies rely on supervised learning from manually annotated images and are therefore sensitive to the intensity profiles in the training dataset. A deep learning-based segmentation model for a new image dataset (e.g., of different contrast), usually requires a new labeled training dataset, which can be prohibitively expensive, or suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines conventional probabilistic atlas-based segmentation with deep learning, enabling training of a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 s at test time on a GPU. European Research Council (Starting Grant 677697) BRAIN Initiative. Cell Census Network (Grant U01-MH117023) National Institutes of Health (U.S.) (Grants R21-AG050122, P41-EB015902, 5U01-MH093765, R01LM012719, R01AG053949, and 1R21AG050122) National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grants P41-EB015896, 1R01-EB023281, R01-EB006758, R21-EB018907, R01-EB019956) National Institute on Aging (Grants 5R01-AG008122, R01-AG016495) National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Grant 1R21-DK-108277-01) National Institute of Neurological Diseases and Stroke (Grants R01-NS0525851, R21-NS072652, R01-NS070963, R01-NS083534, 5U01-NS086625, 5U24-NS10059103) National Science Foundation (U.S.). NeuroNex Grant (Grant 1707312) National Science Foundation (U.S.). Career (1748377) National Institutes of Health (U.S.). Shared Instrumentation Grant Program (Grants 1S10RR023401, 1S10RR019307, and 1S10RR023043) 2021-01-25T20:10:27Z 2021-01-25T20:10:27Z 2019-10 2020-12-16T16:53:46Z Article http://purl.org/eprint/type/ConferencePaper 0302-9743 https://hdl.handle.net/1721.1/129557 Dalca,Adrian V. et al. “Unsupervised Deep Learning for Bayesian Brain MRI Segmentation.” MICCAI 2019: Medical Image Computing and Computer Assisted Intervention, Lecture Notes in Computer Science, 11766, Springer, October 2019, 356–365. © 2019 The Author(s) en 10.1007/978-3-030-32248-9_40 Lecture Notes in Computer Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer International Publishing PMC |
| spellingShingle | Dalca, Adrian Vasile Golland, Polina Iglesias Gonzalez, Juan Eugenio Unsupervised Deep Learning for Bayesian Brain MRI Segmentation |
| title | Unsupervised Deep Learning for Bayesian Brain MRI Segmentation |
| title_full | Unsupervised Deep Learning for Bayesian Brain MRI Segmentation |
| title_fullStr | Unsupervised Deep Learning for Bayesian Brain MRI Segmentation |
| title_full_unstemmed | Unsupervised Deep Learning for Bayesian Brain MRI Segmentation |
| title_short | Unsupervised Deep Learning for Bayesian Brain MRI Segmentation |
| title_sort | unsupervised deep learning for bayesian brain mri segmentation |
| url | https://hdl.handle.net/1721.1/129557 |
| work_keys_str_mv | AT dalcaadrianvasile unsuperviseddeeplearningforbayesianbrainmrisegmentation AT gollandpolina unsuperviseddeeplearningforbayesianbrainmrisegmentation AT iglesiasgonzalezjuaneugenio unsuperviseddeeplearningforbayesianbrainmrisegmentation |