CS U-NET: A Medical Image Segmentation Method Integrating Spatial and Contextual Attention Mechanisms Based on U-NET

Abstract

Medical image segmentation is a crucial process in medical image analysis, with convolutional neural network (CNN)-based methods achieving notable success in recent years. Among these, U-Net has gained widespread use due to its simple yet effective architecture. However, CNNs still struggle to capture global, long-range semantic information. To address this limitation, we present CS U-NET, a novel method built upon Swin-U-Net, which integrates spatial and contextual attention mechanisms. This hybrid approach combines the strengths of both transformers and U-Net architectures to enhance segmentation performance. In this framework, tokenized image patches are processed through a transformer-based U-shaped encoder-decoder, enabling the learning of both local and global semantic features via skip connections. Our method achieves a Dice Similarity Coefficient of 78.64% and a 95% Hausdorff distance of 21.25 on the Synapse multiorgan segmentation dataset, outperforming Trans-U-Net and other state-of-the-art U-Net variants by 4% and 6%, respectively. The experimental results highlight the significant improvements in prediction accuracy and edge detail preservation provided by our approach.