BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration (2405.14019v3)

Abstract: 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.

• The paper presents BrainMorph, a novel keypoint-based foundation model offering robust and interpretable 3D brain MRI registration across various tasks.
• BrainMorph demonstrates versatility across rigid, affine, and nonlinear transformations for both pairwise and efficient groupwise registration, improving scalability.
• Trained on an extensive, diverse dataset, the model outperforms state-of-the-art methods, particularly under large misalignments, ensuring strong generalization.

BrainMorph: A Keypoint-Based Model for Robust Brain MRI Registration

The paper "BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration" presents a novel approach to brain MRI registration through a keypoint-based foundation model named BrainMorph. This model addresses various complexities in MRI registration, such as multi-modal, pairwise, and groupwise settings, by utilizing the previously proposed KeyMorph framework. The model is trained on an expansive dataset of over 100,000 3D volumes, representing nearly 16,000 distinct subjects, both healthy and diseased, making it robust in its application to a wide range of misalignments and pathologies. BrainMorph's capabilities highlight its proficiency in 3D rigid, affine, and nonlinear registration, outperforming several state-of-the-art methods in terms of accuracy and speed, particularly under conditions of considerable initial misalignments and large data group settings.

Key Contributions and Findings

1. Keypoint-Based Framework: BrainMorph leverages a novel keypoint-based framework, providing a marked advancement in robustness and interpretability over existing registration techniques. Unlike dense deformation models or iterative frameworks, the BrainMorph model utilizes a keypoint detection network to identify salient features automatically. This feature benefits users by offering a more transparent registration process where users can evaluate the keypoints that drive the registration process.
2. Task Versatility: The model demonstrates versatile performance across different types of transformation tasks—rigid, affine, and nonlinear—simultaneously supporting both pairwise and groupwise registration. Such versatility is achieved by an adaptable training scheme that integrates various transformation types and objective loss functions.
3. Scalability and Efficiency: Particularly noteworthy is the introduction of a novel groupwise registration strategy that efficiently computes the average space and transformation among multiple images using keypoints. This method enables BrainMorph to handle extensive datasets feasibly, achieving considerable computational efficiency improvements over current methods such as ITK-Elastix.
4. Comprehensive Dataset: The extensive dataset used for BrainMorph's training ensures robustness across various MRI modalities and subject conditions (e.g., healthy, diseased, skull-stripped). This dataset diversity allows BrainMorph to generalize well to unseen data and perform effectively irrespective of preprocessing steps such as skull stripping, usually a requirement for other methods.
5. Performance Metrics: In empirical evaluations, BrainMorph consistently surpassed existing baselines, particularly at large misalignment angles and demonstrated significant improvements in Dice scores and Hausdorff distances. Such quantitative metrics underline the model's superior alignment accuracy.

Implications and Future Directions

The development of BrainMorph represents a substantial stride in the field of medical image registration, offering a model that is not only robust and interpretable but also computationally efficient for real-world applications involving large, complex datasets. The introduction of keypoint-based registration methods marks a paradigm shift from traditional dense deformation models, focusing on leveraging keypoint correspondences to achieve accurate and scalable registrations.

The model’s adaptability to various MRI types and its non-reliance on intensive pre-processing make it an appealing option for widespread clinical application. Future research directions may explore further enhancing model robustness to outliers or noise in data, fine-tuning keypoint detection mechanisms, and extending these methods to non-MRI modalities or non-brain anatomical sites. Additionally, integrating such techniques with emerging technologies like augmenting AI with real-time data could further improve clinical diagnostics and treatment planning in neurology and radiology.

Overall, BrainMorph's contributions illustrate a promising trajectory towards more accessible and scalable solutions in medical image registration, with potential applications that extend beyond its current implementation.