MDH-Net: advancing 3D brain MRI registration with multi-stage transformer and dual-stream feature refinement hybrid network

Chenou Liu, Kangjian He, Dan Xu, Hongzhen Shi

Abstract: Since the advent of the registration method based on deep learning, it has demonstrated a time efficiency advantage several orders of magnitude higher than traditional methods. However, the current deep networks have not fully explored the potential to capture spatial relationships comprehensively. Faced with the complex anatomical structures and detailed deformations in the brain, existing methods often run into difficulties. Therefore, addressing this challenging issue, we propose an unsupervised registration network for 3D brain magnetic resonance imaging (MRI). This framework adopts a hybrid structure of CNN transformer, gradually refining the deformation field using a pyramid structure and multi-level strategies, introducing a local salient position transformer to focus on local details of the deformation, and a hierarchical feature fusion module to merge features of multi-layer deformation fields further. These strategies help preserve high-level semantic information in the brain, thereby improving the quality of the deformation field and achieving deformable registration more effectively. We evaluated the proposed method on two publicly available brain MRI datasets, and the results show that our method outperforms all advanced competing methods in terms of accuracy.