Symmetric Multi-Level Gradient-Inverse Consistency Network for Brain Image Registration with Large Deformation

Abstract

Accurate and robust deformable image registration is crucial for brain image analysis. While deep learning has significantly advanced this field, existing methods often lack robustness for large deformations due to inter-subject variability, frequently requiring pre-registration and relying heavily on data-driven approaches. To address these limitations, we propose an end-to-end Symmetric Multis-level Gradient-Inverse Consistency Network (SM-GICNet) for accurate and robust brain image registration. SM-GICNet employs 1) a symmetric multi-level framework with an attention gate mechanism to capture complex deformations at multiple scales, 2) a symmetric registration strategy at each level to mitigate directional bias, and 3) a gradient inverse consistency strategy to reduce reliance on data-driven constraints and control deformation field complexity. Experimental results demonstrate that our method is able to eliminate the need for pre-registration and outperforms state-of-the-art methods on large deformation registration tasks, achieving a Dice similarity coefficient of 0.797. The implementation of our SM-GICNet is available online at https://github.com/LSYLAB/SM-GICNet.git.