SSCU-UNet: a medical image segmentation network integrating spatial shift and content-aware attention(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第6期
- Page:
- 787-797
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- SSCU-UNet: a medical image segmentation network integrating spatial shift and content-aware attention
- Author(s):
- JIN Fengqing1; 2; ZHOU Yuee1; 2; SUO Guodong1; 2; YANG Jianlan2
- 1. School of Medicine Information Science and Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China 2. Quanzhou Orthopedic-Traumatological Hospital, Quanzhou 362000, China
- Keywords:
- Keywords: medical image segmentation spatial shift-enhanced and content-aware upsampling UNet Swin Transformer spatial shift module content-aware upsampling multi-organ segmentation
- PACS:
- R318；TP391.41
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.06.012
- Abstract:
- Abstract: Objective To design a segmentation framework, namely spatial shift-enhanced and content-aware upsampling UNet (SSCU-UNet), which balances global context awareness and high-resolution feature retention to improve performance in multi-organ CT and cardiac MRI segmentation tasks. Methods The proposed SSCU-UNet segmentation framework incorporated 3 key components. (1) A residual convolutional downsampling module adopted a dual-branch structure consisting of a Conv-GELU-Conv main branch and a strided-convolution shortcut branch, and coupled with layer normalization to retain high-resolution spatial information. (2) A SSFormer module was inserted between the window attention and multilayer perceptron of each Swin Transformer block, and operates along the residual branch to enhance local spatial modeling while maintaining global contextual reasoning via spatial shift attention. (3) A content-aware attention upsampling module was used to enable finer boundary restoration, which adaptively generates feature reconstruction weights according to input features. Results The proposed SSCU-UNet achieved an average Dice similarity coefficient of 82.62% on the Synapse dataset, which was 5.77%, 5.14%, and 3.49% higher than U-Net, TransUNet, and Swin-UNet, respectively, and reduced the Hausdorff distance to 18.79 mm, with particularly prominent performance improvements for anatomically complex organs including the gallbladder, pancreas, and stomach. On the ACDC dataset, the SSCU-UNet yielded an average Dice similarity coefficient of 92.34%, outperforming multiple baseline models. Conclusion By integrating residual convolutional downsampling, spatial shift-enhanced local modeling, and content-aware upsampling, the SSCU-UNet effectively balances global semantic reasoning with fine-grained structural reconstruction, and achieves state-of-the-art performance in multi-organ segmentation tasks.
Last Update: 2026-06-29