MSR-UNet: enhancing multi-scale and long-range dependencies in medical image segmentation.

Transformer-based technology has attracted widespread attention in medical image segmentation. Due to the diversity of organs, effective modelling of multi-scale information and establishing long-range dependencies between pixels are crucial for successful medical image segmentation. However, most studies rely on a fixed single-scale window for modeling, which ignores the potential impact of window size on performance. This limitation can hinder window-based models' ability to fully explore multi-scale and long-range relationships within medical images. To address this issue, we propose a multi-scale reconfiguration self-attention (MSR-SA) module that accurately models multi-scale information and long-range dependencies in medical images. The MSR-SA module first divides the attention heads into multiple groups, each assigned an ascending dilation rate. These groups are then uniformly split into several non-overlapping local windows. Using dilated sampling, we gather the same number of keys to obtain both long-range and multi-scale information. Finally, dynamic information fusion is achieved by integrating features from the sampling points at corresponding positions across different windows. Based on the MSR-SA module, we propose a multi-scale reconfiguration U-Net (MSR-UNet) framework for medical image segmentation. Experiments on the Synapse and automated cardiac diagnosis challenge (ACDC) datasets show that MSR-UNet can achieve satisfactory segmentation results. The code is available at https://github.com/davidsmithwj/MSR-UNet (DOI: 10.5281/zenodo.13969855).