Improve Retinal Artery/Vein Classification via Channel Couplin

2025-07-31

AI Summary

Existing retinal artery-vein classification methods treat artery segmentation, vein segmentation, and overall vessel segmentation as three independent binary tasks, neglecting their anatomical coupling and leading to inconsistent predictions. To address this, we propose a collaborative learning framework: (1) a channel-wise vascular consistency loss that enforces feature-level alignment of the three segmentation outputs across channel dimensions; and (2) an intra-image pixel-level contrastive loss to enhance fine-grained discriminability. The method is built upon a standard CNN architecture, requiring no additional annotations or pretraining. Evaluated on three benchmark datasets—RITE, LES-AV, and HRF—it consistently outperforms state-of-the-art approaches, achieving significant improvements in classification accuracy and structural consistency. Our framework establishes an anatomically aware, interpretable, and robust paradigm for retinal image analysis.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Retinal vessel segmentation plays a vital role in analyzing fundus images for the diagnosis of systemic and ocular diseases. Building on this, classifying segmented vessels into arteries and veins (A/V) further enables the extraction of clinically relevant features such as vessel width, diameter and tortuosity, which are essential for detecting conditions like diabetic and hypertensive retinopathy. However, manual segmentation and classification are time-consuming, costly and inconsistent. With the advancement of Convolutional Neural Networks, several automated methods have been proposed to address this challenge, but there are still some issues. For example, the existing methods all treat artery, vein and overall vessel segmentation as three separate binary tasks, neglecting the intrinsic coupling relationships between these anatomical structures. Considering artery and vein structures are subsets of the overall retinal vessel map and should naturally exhibit prediction consistency with it, we design a novel loss named Channel-Coupled Vessel Consistency Loss to enforce the coherence and consistency between vessel, artery and vein predictions, avoiding biasing the network toward three simple binary segmentation tasks. Moreover, we also introduce a regularization term named intra-image pixel-level contrastive loss to extract more discriminative feature-level fine-grained representations for accurate retinal A/V classification. SOTA results have been achieved across three public A/V classification datasets including RITE, LES-AV and HRF. Our code will be available upon acceptance.

Problem

• Automate retinal artery/vein classification to replace manual methods
• Address inconsistency in existing binary segmentation approaches
• Enhance feature-level accuracy for clinical diagnosis

Innovation

• Channel-Coupled Vessel Consistency Loss enforces prediction coherence
• Intra-image pixel-level contrastive loss improves feature discrimination
• Combines artery vein segmentation with vessel consistency