Symmetric pyramid network for medical image inverse consistent diffeomorphic registration

Over the past few years, deep learning-based image registration methods have achieved remarkable performance in medical image analysis. However, many existing methods struggle to ensure accurate registration while preserving the desired diffeomorphic properties and inverse consistency of the final deformation field. To address the problem, this paper presents a novel symmetric pyramid network for medical image inverse consistent diffeomorphic registration. Specifically, we first encode the multi-scale images to the feature pyramids via a shared-weights encoder network and then progressively conduct the feature-level diffeomorphic registration. The feature-level registration is implemented symmetrically to ensure inverse consistency. We independently carry out the forward and backward feature-level registration and average the estimated bidirectional velocity fields for more robust estimation. Finally, we employ symmetric multi-scale similarity loss to train the network. Experimental results on three public datasets, including Mindboggle101, CANDI, and OAI, show that our method significantly outperforms others, demonstrating that the proposed network can achieve accurate alignment and generate the deformation fields with expected properties. Our code will be available at https://github.com/zhangliutong/SPnet.