Deformable medical image registration is widely used in medical image processing with the invertible and one-to-one mapping between images. While state-of-the-art image registration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on computer vision tasks. Existing models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. These methods also ignore the topology preservation and invertibility of the transformation although they achieve fast image registration. In this paper, we propose a novel, symmetric unsupervised learning network Swin-VoxelMorph based on the Swin Transformer which minimizes the dissimilarity between images and estimates both forward and inverse transformations simultaneously. Specifically, we propose 3D Swin-UNet, which applies hierarchical Swin Transformer with shifted windows as the encoder to extract context features. And a symmetric Swin Transformer-based decoder with patch expanding layer is designed to perform the up-sampling operation to estimate the registration fields. Besides, our objective loss functions can guarantee substantial diffeomorphic properties of the predicted transformations. We verify our method on two datasets including ADNI and PPMI, and it achieves state-of-the-art registration accuracy while maintaining desirable diffeomorphic properties. <<<

An effective backbone network is important to deep learning-based Deformable Medical Image Registration (DMIR), because it extracts and matches the features between two images to discover the mutual correspondence for fine registration. However, the existing deep networks focus on single image situation and are limited in registration task which is performed on paired images. Therefore, we advance a novel backbone network, XMorpher, for the effective corresponding feature representation in DMIR. 1) It proposes a novel full transformer architecture including dual parallel feature extraction networks which exchange information through cross attention, thus discovering multi-level semantic correspondence while extracting respective features gradually for final effective registration. 2) It advances the Cross Attention Transformer (CAT) blocks to establish the attention mechanism between images which is able to find the correspondence automatically and prompts the features to fuse efficiently in the network. 3) It constrains the attention computation between base windows and searching windows with different sizes, and thus focuses on the local transformation of deformable registration and enhances the computing efficiency at the same time. Without any bells and whistles, our XMorpher gives Voxelmorph 2.8% improvement on DSC, demonstrating its effective representation of the features from the paired images in DMIR. We believe that our XMorpher has great application potential in more paired medical images. Our XMorpher is open on https://github.com/Solemoon/XMorpher <<<