Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11492).
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Book |
| Language: | English |
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Springer International Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/129338 |
| _version_ | 1826190967151001600 |
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| author | Iglesias Gonzalez, Juan Eugenio Van Leemput, Koen Golland, Polina Yendiki, Anastasia |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Iglesias Gonzalez, Juan Eugenio Van Leemput, Koen Golland, Polina Yendiki, Anastasia |
| author_sort | Iglesias Gonzalez, Juan Eugenio |
| collection | MIT |
| description | Part of the Lecture Notes in Computer Science book series (LNCS, volume 11492). |
| first_indexed | 2024-09-23T08:48:23Z |
| format | Book |
| id | mit-1721.1/129338 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:48:23Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1293382022-09-30T11:23:12Z Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases Iglesias Gonzalez, Juan Eugenio Van Leemput, Koen Golland, Polina Yendiki, Anastasia Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Part of the Lecture Notes in Computer Science book series (LNCS, volume 11492). Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalamus and its nuclei) can be extracted more accurately by fusing the two modalities. Following the framework of Bayesian segmentation with probabilistic atlases and unsupervised appearance modeling, we present here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We propose a hierarchical likelihood term for the dMRI defined on the unit ball, which combines the Beta and Dimroth-Scheidegger-Watson distributions to model the data at each voxel. This term is integrated with a mixture of Gaussians for the sMRI data, such that the resulting joint unsupervised likelihood enables the analysis of multi-modal scans acquired with any type of MRI contrast, b-values, or number of directions, which enables wide applicability. We also propose an inference algorithm to estimate the maximum-a-posteriori model parameters from input images, and to compute the most likely segmentation. Using a recently published atlas derived from histology, we apply our method to thalamic nuclei segmentation on two datasets: HCP (state of the art) and ADNI (legacy) – producing lower sample sizes than Bayesian segmentation with sMRI alone. NIH (Grants R21AG050122, P41EB015902) 2021-01-08T14:30:04Z 2021-01-08T14:30:04Z 2019-05 2020-12-15T19:25:51Z Book http://purl.org/eprint/type/ConferencePaper 9783030203504 9783030203511 0302-9743 1611-3349 https://hdl.handle.net/1721.1/129338 Iglesias, Juan Eugenio et al. "Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases." IPMI 2019: Information Processing in Medical Imaging, Lecture Notes in Computer Science, 11492, Springer International Publishing, 2019, 767-779. © 2019 Springer Nature en http://dx.doi.org/10.1007/978-3-030-20351-1_60 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 arXiv |
| spellingShingle | Iglesias Gonzalez, Juan Eugenio Van Leemput, Koen Golland, Polina Yendiki, Anastasia Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases |
| title | Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases |
| title_full | Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases |
| title_fullStr | Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases |
| title_full_unstemmed | Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases |
| title_short | Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases |
| title_sort | joint inference on structural and diffusion mri for sequence adaptive bayesian segmentation of thalamic nuclei with probabilistic atlases |
| url | https://hdl.handle.net/1721.1/129338 |
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