Weighted window attention and recover feature resolution-based network for deformable abdominal image registration

Abstract

The Swin Transformer has attracted attention in the field of medical image analysis since its introduction in 2021 owing to its computational efficiency and long-range modeling capabilities. Because of these characteristics, the Swin Transformer can establish distant relationships between corresponding voxels at distant locations for complex abdominal image registration tasks. Transformer-based registration methods model the features of multiple fused voxels and output coarse-grained features of the same size. To enable the Transformer to output fine-grained information and strengthen the contribution of the Transformer in the registration model, we propose the recover feature resolution network (RFRNet), which converts the features outputted by the Transformer into fine-grained spatial information. In addition, fixed-window partitioning restricts the Transformer from modeling the global connection of semantic information with uncertain distances. Therefore, we proposed a weighted window attenuation (WWA) mechanism to achieve automatic global-scale interaction of window information after a window partitioning operation. Based on these improvements, we proposed a single-modal unsupervised deformable abnormal image registration model called RFR-WWANet. Qualitative and quantitative results showed that RFR-WWANet achieved significant improvements compared with the current state-of-the-art methods. Ablation experiments demonstrated the effectiveness of the RFRNet and WWA designs. Our code is available at https://github.com/MingR-Ma/RFR-WWANet.