Retinal vessel segmentation is a crucial task in biomedical image analysis, enabling the understanding of vascular structures. Precise vessel segmentation reveals underlying vascular abnormalities in disease progression. Recent approaches have improved segmentation performance; however, maintaining computational efficiency while preserving accurate delineation of thin and low-contrast vessels remains an open engineering challenge. This study presents DSCBAM-Net, a lightweight dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms with multi-scale dilated convolution feature encoding to enhance vessel representation, particularly for thin and low-contrast vessel structures. The architecture follows a multi-stage design: (1) A robust encoder that extracts spatial and contextual features using standard and dilated convolutions enhanced with attention mechanisms. (2) A bottleneck module that fuses contextual features using parallel dilations. (3) A decoder with deep supervision for progressive vessel enhancement, enabling precise segmentation with fewer parameters and faster convergence. A composite hybrid loss function that integrates Dice, Focal-Tversky, and Top-k loss is introduced to address class imbalance and emphasize difficult vessel pixels. Following segmentation, various morphological features are extracted and used in a Retinal Vein Occlusion (RVO) detection and grading module, which estimates occlusion probability, stratifies the severity, and highlights dominant vascular drivers for explainability. Extensive experiments were conducted on the merged dataset with an impressive dice score of 0.82. DSCBAM-Net also demonstrates superior cross-dataset performance, achieving a dice score of 0.879, 0.890, and 0.883 on DRIVE, STARE, and CHASE-DB1. In addition to segmentation, vessel-based structural features are extracted from the predicted masks to enable feature-driven vascular analysis and interpretability. Qualitative visualization, further highlights the effectiveness of the proposed architecture. Thus, DSCBAM-Net provides a robust and efficient solution for retinal vessel segmentation and downstream analytics tasks. • Lightweight dual-attention DSCBAM-Net for vessel segmentation. • Dilated convolutional design improves thin and low-contrast vessel recovery. • Channel-spatial CBAM enhances feature selectivity and structural stability. • Robust cross-dataset performance on DRIVE,STARE, and CHASE-DB1. • Vessel features enable RVO-oriented structural analysis.
Rajatha et al. (Sun,) studied this question.