Prior-Guided Spatial–Frequency Interactive Network for Retinal Vessel Segmentation

Retinal vessel segmentation is essential for diagnosing eye diseases. However, complex multiscale vascular structures and low contrast make accurate segmentation difficult. Current methods often overlook structural priors and seldom explore spatial–frequency synergies, resulting in poor segmentation of tiny vessels. To address these issues, we propose a Prior-Guided Spatial–Frequency Interactive Network (PGSFI-Net) that operates in both spatial and frequency domains. The Sobel-Attention Prior module extracts vascular edge priors using a single-scale Sobel operator and spatial attention, followed by multi-scale prior injection to strengthen faint-vessel representations. The Four-Branch Selective Attention Aggregation module captures multi-directional vascular features through strip convolutions and multi-dimensional attention. The Multi-Branch Spatial–Frequency Fusion module achieves bidirectional feature complementation via cross-gating interaction. The Local–Global Fusion module recovers subtle vascular structures by combining differential CNN branches with channel self-attention. On DRIVE, CHASE_DB1, and STARE, PGSFI-Net achieves ACC/F1/AUC values of 0.9646/0.8136/0.9838, 0.9727/0.8290/0.9838, and 0.9746/0.8393/0.9884, respectively. These results demonstrate competitive performance against state-of-the-art methods across the evaluated datasets and challenging low-contrast cases.