RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration

The Swin transformer has recently attracted attention in medical image analysis due to its computational efficiency and long-range modeling capability, which enables the establishment of more distant relationships between corresponding voxels. However, transformer-based models split images into tokens, which results in transformers that can only model and output coarse-grained spatial information representations. To address this issue, we propose Recovery Feature Resolution Network (RFRNet), which enables the transformer to contribute with fine-grained spatial information and rich semantic correspondences. Furthermore, shifted window partitioning operations are inflexible, indicating that they cannot perceive the semantic information over uncertain distances and automatically bridge the global connections between windows. Therefore, we present a Weighted Window Attention (WWA) to automatically build global interactions between windows after the regular and cyclic shifted window partitioning operations for Swin transformer blocks. The proposed unsupervised deformable image registration model, named RFR-WWANet, senses the long-range correlations, thereby facilitating meaningful semantic relevance of anatomical structures. Qualitative and quantitative results show that RFR-WWANet achieves significant performance improvements over baseline methods. Ablation experiments demonstrate the effectiveness of the RFRNet and WWA designs.

Symmetric Transformer-based Network for Unsupervised Image Registration

Medical image registration is a fundamental and critical task in medical image analysis. With the rapid development of deep learning, convolutional neural networks (CNN) have dominated the medical image registration field. Due to the disadvantage of the local receptive field of CNN, some recent registration methods have focused on using transformers for non-local registration. However, the standard Transformer has a vast number of parameters and high computational complexity, which causes Transformer can only be applied at the bottom of the registration models. As a result, only coarse information is available at the lowest resolution, limiting the contribution of Transformer in their models. To address these challenges, we propose a convolution-based efficient multi-head self-attention (CEMSA) block, which reduces the parameters of the traditional Transformer and captures local spatial context information for reducing semantic ambiguity in the attention mechanism. Based on the proposed CEMSA, we present a novel Symmetric Transformer-based model (SymTrans). SymTrans employs the Transformer blocks in the encoder and the decoder respectively to model the long-range spatial cross-image relevance. We apply SymTrans to the displacement field and diffeomorphic registration. Experimental results show that our proposed method achieves state-of-the-art performance in image registration. Our code is publicly available at \url{https://github.com/MingR-Ma/SymTrans}.