Advanced Multi-Level Bidirectional Attention Network for Retinal Vessel Segmentation

Retinal vessel segmentation is crucial for clinical diagnosis due to the rich morphological information in retinal fundus images. Although neural networks perform well, issues like feature loss during encoding and insufficient context fusion in skip connections remain. The complex curvature of small vessels and uneven background brightness further complicate pathological image segmentation. To address these problems, this paper proposes a multi-level bidirectional attention aggregation network. The encoder proposes a Partial Encoder Block (PEB) to reduce feature loss from traditional convolution. A Dynamic Direction Attention Module (DDAM) is proposed in the skip connection to enhance anisotropic geometric representation, preserving fine vessel details and contextual information. Additionally, a Multi-Feature Fusion Module (MFFM) is proposed to fuse multi-level features, retaining details while suppressing background noise. Experiments on DRIVE, STARE, and CHASEDB1 datasets demonstrate the network's effectiveness. On DRIVE, AUC, F1-score, and Sensitivity improved by 0.19%, 0.43%, and 1.17%, respectively. On STARE, AUC, F1, and sensitivity rose by 0.26%, 2.95%, and 2.07%, respectively. On CHASEDB1, AUC, F1-score, and specificity increased by 0.2%, 1.12%, and 0.44%, respectively. Results show the proposed network outperforms existing methods in segmentation performance.