SA-UNetv2: Rethinking Spatial Attention U-Net for Retinal Vessel Segmentation

Retinal vessel segmentation is a crucial yet challenging task for early diagnosis of diseases such as diabetes and hypertension. SA-UNet achieves promising performance by introducing spatial attention in the bottleneck, but it underuses attention in skip connections and fails to address the severe foreground-background imbalance. We propose SA-UNetv2, a lightweight model that injects cross-scale spatial attention into all skip connections to strengthen multi-scale feature fusion and adopts a weighted Binary Cross-Entropy (BCE) + Matthews Correlation Coefficient (MCC) loss to improve robustness to class imbalance. On the public DRIVE and STARE datasets, SA-UNetv2 achieves state-of-the-art performance with only$\mathbf{1. 2 M B}$memory and$\mathbf{0. 2 6 M}$parameters (less than 50% of SA-UNet), and$\mathbf{1}$second CPU inference on$592 \times 592 \times 3$images, demonstrating strong efficiency and deployability in resource-constrained, CPU-only settings. The code is available at github.com/clguo/SA-UNetv2.