A Local-global Gaussian Weighted Attention-based Approach to Medical Image Segmentation

This work suggests a medical image segmentation approach based on local-global Gaussian-weighted attention as a remedy for the U-Net model’s lack of explicit modeling of long-range dependencies for medical image segmentation problems. The approach uses a Transformer module built on a local-global Gaussian weighted attention mechanism and a unified decoder module in the U-Net model to enable relevant relationship mining and centralized multi-resolution feature processing. Additionally, this paper validates the efficacy of the method put forth in this paper on two openly accessible datasets, Synapse and ACDC, by expanding the dataset with random angle rotation and mirroring operations for pre-processing and using a loss function combining HD95 and Dice for training. According to the experimental findings, the suggested method is competitive with other existing methods and outperforms TransUNet and U-Net in terms of the model’s generalization and segmentation effects.