BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration
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arXiv
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| Format: | Preprint |
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2024
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| author | Wang, Alan Q. Saluja, Rachit Kim, Heejong He, Xinzi Dalca, Adrian Sabuncu, Mert R. |
| author_facet | Wang, Alan Q. Saluja, Rachit Kim, Heejong He, Xinzi Dalca, Adrian Sabuncu, Mert R. |
| contents | We present a keypoint-based foundation model for general purpose brain MRI registration, based on the recently-proposed KeyMorph framework. Our model, called BrainMorph, serves as a tool that supports multi-modal, pairwise, and scalable groupwise registration. BrainMorph is trained on a massive dataset of over 100,000 3D volumes, skull-stripped and non-skull-stripped, from nearly 16,000 unique healthy and diseased subjects. BrainMorph is robust to large misalignments, interpretable via interrogating automatically-extracted keypoints, and enables rapid and controllable generation of many plausible transformations with different alignment types and different degrees of nonlinearity at test-time. We demonstrate the superiority of BrainMorph in solving 3D rigid, affine, and nonlinear registration on a variety of multi-modal brain MRI scans of healthy and diseased subjects, in both the pairwise and groupwise setting. In particular, we show registration accuracy and speeds that surpass many classical and learning-based methods, especially in the context of large initial misalignments and large group settings. All code and models are available at https://github.com/alanqrwang/brainmorph. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14019 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration Wang, Alan Q. Saluja, Rachit Kim, Heejong He, Xinzi Dalca, Adrian Sabuncu, Mert R. Computer Vision and Pattern Recognition We present a keypoint-based foundation model for general purpose brain MRI registration, based on the recently-proposed KeyMorph framework. Our model, called BrainMorph, serves as a tool that supports multi-modal, pairwise, and scalable groupwise registration. BrainMorph is trained on a massive dataset of over 100,000 3D volumes, skull-stripped and non-skull-stripped, from nearly 16,000 unique healthy and diseased subjects. BrainMorph is robust to large misalignments, interpretable via interrogating automatically-extracted keypoints, and enables rapid and controllable generation of many plausible transformations with different alignment types and different degrees of nonlinearity at test-time. We demonstrate the superiority of BrainMorph in solving 3D rigid, affine, and nonlinear registration on a variety of multi-modal brain MRI scans of healthy and diseased subjects, in both the pairwise and groupwise setting. In particular, we show registration accuracy and speeds that surpass many classical and learning-based methods, especially in the context of large initial misalignments and large group settings. All code and models are available at https://github.com/alanqrwang/brainmorph. |
| title | BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.14019 |