CorrMorph: Unsupervised Deformable Brain MRI Registration Based on Correlation Mining

Deformable image registration, as a fundamental prerequisite for many medical image analysis tasks, has received considerable attention. However, existing methods suffer from two key issues: 1) single-stream methods that stack moving and fixed images as input are prone to interference from spatial misalignment and style discrepancy, while dual-stream methods that use fully parallel encoders face challenges in learning correlations between images. 2) CNN-based methods are difficult to capture the complex spatial correspondences between images, while Transformer-based methods lack the ability to capture local context information. Therefore, we propose an unsupervised deformable brain MRI registration network, CorrMorph, which achieves reasonable and accurate registration by mining correlations. Specifically, we design a match-fusion strategy that allows the independent extraction of shallow features from the moving and fixed images while capturing their correlations in deeper layers. Furthermore, we propose two novel modules. 1) Correlation Matching Module (CMM), which mines correlations between images to achieve effective feature matching, 2) Feature Transmission Module (FTM), which extracts important spatial features to achieve effective feature transmission. Extensive experiments are conducted on three brain MRI datasets, and the results indicate that our method achieves state-of-the-art performance, with an average improvement of 2.7% on DSC compared to the representative VoxelMorph.