[2603.21760] Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2603.21760 (eess) [Submitted on 23 Mar 2026] Title:Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration Authors:Jiaqi Shang, Haojin Wu, Yinyi Lai, Zongyu Li, Chenghao Zhang, Jia Guo View a PDF of the paper titled Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration, by Jiaqi Shang and 5 other authors View PDF Abstract:Deformable image registration plays a fundamental role in medical image analysis by enabling spatial alignment of anatomical structures across subjects. While recent deep learning-based approaches have significantly improved computational efficiency, many existing methods remain limited in capturing long-range anatomical correspondence and maintaining deformation consistency. In this work, we present a cycle inverse-consistent transformer-based framework for deformable brain MRI registration. The model integrates a Swin-UNet architecture with bidirectional consistency constraints, enabling the joint estimation of forward and backward deformation fields. This design allows the framework to capture both local anatomical details and global spatial relationships while improving deformation stability. We conduct a comprehensive evaluation of the proposed framework on a large multi-center dataset consisting of 2851 T1-weighted brain MRI scans aggregated from 13 public datasets. Experimental results...