RE-XswinUnet: Rotary Positional Encoding and Inter-Slice Contextual Connections for Multi-Organ Segmentation

Medical image segmentation has been a central research focus in deep learning, but methods based on convolutions have limitations in modeling the long-range validity of images. To overcome this issue, hybrid CNN-Transformer architectures have been explored, with SwinUNet being a classic approach. However, SwinUNet still faces challenges such as insufficient modeling of relative position information, limited feature fusion capabilities in skip connections, and the loss of translational invariance caused by Patch Merging. To overcome these limitations, the architecture RE-XswinUnet is presented as a novel solution for medical image segmentation. In our design, relative position biases are replaced with rotary position embedding to enhance the model’s ability to extract detailed information. During the decoding stage, XskipNet is designed to improve cross-scale feature fusion and learning capabilities. Additionally, an SCAR Block downsampling module is incorporated to preserve translational invariance more effectively. The experimental results demonstrate that RE-XswinUnet achieves improvements of 2.65% and 0.95% in Dice coefficients on the Synapse multi-organ and ACDC datasets, respectively, validating its superiority in medical image segmentation tasks.