Enhancing unsupervised learning in medical image registration through scale-aware context aggregation.

Deformable image registration (DIR) is essential for medical image analysis, facilitating the establishment of dense correspondences between images to analyze complex deformations. Traditional registration algorithms often require significant computational resources due to iterative optimization, while deep learning approaches face challenges in managing diverse deformation complexities and task requirements. We introduce ScaMorph, an unsupervised learning model for DIR that employs scale-aware context aggregation, integrating multiscale mixed convolution with lightweight multiscale context fusion. This model effectively combines convolutional networks and vision transformers, addressing various registration tasks. We also present diffeomorphic variants of ScaMorph to maintain topological deformations. Extensive experiments on 3D medical images across five applications-atlas-to-patient and inter-patient brain magnetic resonance imaging (MRI) registration, inter-modal brain MRI registration, inter-patient liver computed tomography (CT) registration as well as inter-modal abdomen MRI-CT registration-demonstrate that our model significantly outperforms existing methods, highlighting its effectiveness and broader implications for enhancing medical image registration techniques.