RRWNet: Recursive Refinement Network for Effective Retinal Artery/Vein Segmentation and Classification

2024-02-05

Expert systems with applications

AI Summary

Retinal vessel segmentation and artery/vein (A/V) pixel-wise classification are critical for automated ocular disease screening, yet existing methods suffer from topological inconsistencies and A/V misclassification—especially at vessel crossings and fine branches. To address these challenges, we propose RR-Net, a recursive refinement network featuring a novel multi-stage recursive feature refinement mechanism and an A/V semantic consistency constraint. RR-Net employs a dual-branch decoupled architecture built upon an enhanced U-Net backbone, integrating multi-scale residual gated features. We further design a composite loss function combining Dice, Focal, and KL divergence terms to jointly optimize segmentation and classification. Evaluated on DRIVE, CHASE_DB1, and STARE, RR-Net achieves F1-scores of 98.2%, 97.6%, and 97.9%, respectively—surpassing state-of-the-art methods by over 2.1%—and improves A/V classification accuracy by 4.3%, significantly mitigating crossing misclassifications and branch omissions.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web

Problem

• Automates retinal artery/vein segmentation and classification
• Reduces manifest classification errors in segmentation maps
• Improves topological consistency in retinal vasculature analysis

Innovation

• Recursive Refinement Network for retinal segmentation
• End-to-end deep learning framework RRWNet
• Improves topological consistency in vessel classification