DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation.

• DOI: 10.48550/arxiv.2310.12570
• Publication Date: 2024-05-16
• Authors: Guanqun Sun; Guanqun Sun; Yizhi Pan; Weikun Kong; Zichang Xu; Jianhua Ma; Teeradaj Racharak; Le-Minh Nguyen; Junyi Xin; Junyi Xin; Junyi Xin
• Keywords: FOS: Computer and information sciences; medical image segmentation; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; deep learning; Bioengineering and Biotechnology; Electrical Engineering and Systems Science - Image and Video Processing; U-net; Graphics (cs.GR); Machine Learning (cs.LG); 03 medical and health sciences; Computer Science - Graphics; 0302 clinical medicine; dual attention; transformer; FOS: Electrical engineering, electronic engineering, information engineering; TP248.13-248.65; Biotechnology

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. However, they lack the ability to harness the image’s intrinsic position and channel features. Research employing Dual Attention mechanisms of position and channel have not been specifically optimized for the high-detail demands of medical images. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block (DA-Block) into the traditional U-shaped architecture. Also, DA-TransUNet tailored for the high-detail requirements of medical images, optimizes the intermittent channels of Dual Attention (DA) and employs DA in each skip-connection to effectively filter out irrelevant information. This integration significantly enhances the model’s capability to extract features, thereby improving the performance of medical image segmentation. DA-TransUNet is validated in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across 5 datasets. In summary, DA-TransUNet has made significant strides in medical image segmentation, offering new insights into existing techniques. It strengthens model performance from the perspective of image features, thereby advancing the development of high-precision automated medical image diagnosis. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.