BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration
This provides a robust and flexible tool for medical imaging researchers and clinicians, enabling efficient registration of brain MRI scans in healthy and diseased subjects, though it builds incrementally on the KeyMorph framework.
The authors tackled brain MRI registration by developing BrainMorph, a keypoint-based foundation model that achieves superior accuracy and speed in 3D rigid, affine, and nonlinear registration for multi-modal scans, surpassing many classical and learning-based methods.
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.