FCSU-Net: A novel full-scale Cross-dimension Self-attention U-Net with collaborative fusion of multi-scale feature for medical image segmentation

• Published in: Computers in biology and medicine 2024-09, Vol.180, p.108947, Article 108947
• Main Authors: Xu, Shijie, Chen, Yufeng, Yang, Shukai, Zhang, Xiaoqian, Sun, Feng
• Format: Article
• Language: English
• Subjects: Collaboration; Feature fusion; Image acquisition; Image processing; Image segmentation; Medical image segmentation; Medical imaging; Self-attention; UNet
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108947

Recently, ViT and CNNs based on encoder–decoder architecture have become the dominant model in the field of medical image segmentation. However, there are some deficiencies for each of them: (1) It is difficult for CNNs to capture the interaction between two locations with consideration of the longer distance. (2) ViT cannot acquire the interaction of local context information and carries high computational complexity. To optimize the above deficiencies, we propose a new network for medical image segmentation, which is called FCSU-Net. FCSU-Net uses the proposed collaborative fusion of multi-scale feature block that enables the network to obtain more abundant and more accurate features. In addition, FCSU-Net fuses full-scale feature information through the FFF (Full-scale Feature Fusion) structure instead of simple skip connections, and establishes long-range dependencies on multiple dimensions through the CS (Cross-dimension Self-attention) mechanism. Meantime, every dimension is complementary to each other. Also, CS mechanism has the advantage of convolutions capturing local contextual weights. Finally, FCSU-Net is validated on several datasets, and the results show that FCSU-Net not only has a relatively small number of parameters, but also has a leading segmentation performance. •Developed a new generalized medical image segmentation network, FCSU-Net.•Proposed a collaborative approach of feature fusion.•Complete self-attention from multiple perspectives based on global information.