SDA-SwinNet: Swin-UNet with Dense Skip and Shift-ASPP for Retinal Vessel Segmentation.
Authors
Affiliations (5)
Affiliations (5)
- School of Computer Science, Yangtze University, Jingzhou 434025, China.
- School of Cyber Science and Engineering, Wuxi University, Wuxi 214105, China.
- Wuxi Key Laboratory of Artificial Intelligence and Security, Wuxi University, Wuxi 214105, China.
- School of Physics & Electronic Engineering, Hubei University of Arts and Science, Xiangyang 441053, China.
- School of Computer Science, University of Liverpool, Liverpool L69 3DR, UK.
Abstract
Retinal artery/vein segmentation is a prerequisite for many ophthalmic diagnostic tools. Yet, the task remains difficult: vessels form complex trees, vary widely in caliber, and often appear low-contrast at terminal branches. We propose SDA-SwinNet to handle these challenges. The network adopts Swin-UNet as its backbone and adds three modifications: a Shift-ASPP module for multi-scale context, an HF-Bridge for cross-level feature fusion, and a fractal-constrained loss with a differentiable topology surrogate. Experimental results on the DRIVE-AV and LES-AV datasets show that the proposed model achieves an overall F1-score of 73.13% on DRIVE-AV and 67.85% on LES-AV, with additional class-wise evaluations for arteries and veins. The results demonstrate that SDA-SwinNet achieves a competitive trade-off between segmentation accuracy and computational efficiency.