文献类型：期刊文献

英文题名：ST-Unet: Swin Transformer boosted U-Net with Cross-Layer Feature Enhancement for medical image segmentation

作者：Zhang, Jing[1];Qin, Qiuge[1];Ye, Qi[1];Ruan, Tong[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：153

外文期刊名：COMPUTERS IN BIOLOGY AND MEDICINE

收录：;EI(收录号：20230513467378);WOS:【SCI-EXPANDED(收录号:WOS:000923126800001)】；

基金：Funding This study was funded by the Natural Science Foundation of Shang-hai, China (grant number 22ZR1418400) .

语种：英文

外文关键词：Medical image segmentation; Swin Transformer; ST-Unet; Cross-layer feature enhancement

摘要：Medical image segmentation is an essential task in clinical diagnosis and case analysis. Most of the existing methods are based on U-shaped convolutional neural networks (CNNs), and one of disadvantages is that the long-term dependencies and global contextual connections cannot be effectively established, which results in inaccuracy segmentation. For fully using low-level features to enhance global features and reduce the semantic gap between encoding and decoding stages, we propose a novel Swin Transformer boosted U-Net (ST-Unet) for medical image processing in this paper, in which Swin Transformer and CNNs are used as encoder and decoder respectively. Then a novel Cross-Layer Feature Enhancement (CLFE) module is proposed to realize cross-layer feature learning, and a Spatial and Channel Squeeze & Excitation module is adopted to highlight the saliency of specific regions. Finally, we learn the features fused by the CLFE module through CNNs to recover low-level features and localize local features for realizing more accurate semantic segmentation. Experiments on widely used public datasets Synapse and ISIC 2018 prove that our proposed ST-Unet can achieve 78.86 of dice and 0.9243 of recall performance, outperforming most current medical image segmentation methods.