U-Shaped Network Based on Particle Swarm Optimization for Retinal Vessel Segmentation

Accurate retinal vessel segmentation plays a critical role in the early detection and monitoring of ophthalmic diseases. In this work, we propose a novel retinal vessel segmentation method that integrates Neural Architecture Search (NAS) with a U-shaped encoder-decoder network, optimized using particle swarm optimization (PSO). The framework automates the design of scalable architectures by exploring an extensible search space built with lightweight construction modules, including 3 × 3 convolutions, batch normalization, attention modules, and residual connections. Experimental results on the DRIVE and CHASE_DB1 datasets demonstrate that the searched model achieves superior segmentation accuracy with the fewest parameters (only 0.04M) compared to existing methods. Furthermore, the model exhibits competitive performance on the crack bench-mark dataset CrackLS315, highlighting the strong generalization capability of the searched architecture. In conclusion, the proposed method achieves an optimal balance between segmentation accuracy and model complexity, demonstrating its potential for clinical applications.