NestedMorph: Enhancing Deformable Medical Image Registration with Nested Attention Mechanisms

Gurucharan Marthi Krishna Kumar, Janine Mendola, Amir Shmuel; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4683-4692

Deformable image registration is crucial for aligning medical images in a nonlinear fashion across different modalities allowing for precise spatial correspondence between varying anatomical structures. This paper presents NestedMorph a novel network utilizing a Nested Attention Fusion approach to improve intra-subject deformable registration between T1-weighted (T1w) MRI and diffusion MRI (dMRI) data. NestedMorph integrates high-resolution spatial details from an encoder with semantic information from a decoder using a multi-scale framework enhancing both local and global feature extraction. Our model notably outperforms existing methods including CNN-based approaches like VoxelMorph MIDIR and CycleMorph as well as Transformer-based models such as TransMorph and ViT-V-Net and traditional techniques like NiftyReg and SyN. Evaluations using the HCP dataset demonstrate that NestedMorph achieves superior performance across key metrics including SSIM HD95 and SDlogJ with the highest SSIM of 0.89 the lowest HD95 of 2.5 and SDlogJ of 0.22. These results highlight NestedMorph's ability to capture both local and global image features effectively leading to superior registration performance. The promising outcomes of this study underscore NestedMorph's potential to significantly advance deformable medical image registration providing a robust framework for future research and clinical applications. The source code and our implementation are available at: https://github.com/AS-Lab/Marthi-et-al-2024-NestedMorph-Deformable-Medical-Image-Registration