RIP-AV: Joint Representative Instance Pre-training with Context Aware Network for Retinal Artery/Vein Segmentation

Accurate deep learning-based segmentation of retinal arteries and veins (A/V) enables improved diagnosis, monitoring, and management of ocular fundus diseases and systemic diseases. However, existing resized and patch-based algorithms face challenges with redundancy, overlooking thin vessels, and underperforming in low-contrast edge areas of the retinal images, due to imbalanced background-to-A/V ratios and limited contexts. Here, we have developed a novel deep learning framework for retinal A/V segmentation, named RIP-AV, which integrates a Representative Instance Pre-training (RIP) task with a context-aware network for retinal A/V segmentation for the first time. Initially, we develop a direct yet effective algorithm for vascular patch-pair selection (PPS) and then introduce a RIP task, formulated as a multi-label problem, aiming at enhancing the network's capability to learn latent arteriovenous features from diverse spatial locations across vascular patches. Subsequently, in the training phase, we introduce two novel modules: Patch Context Fusion (PCF) module and Distance Aware (DA) module. They are designed to improve the discriminability and continuity of thin vessels, especially in low-contrast edge areas, by leveraging the relationship between vascular patches and their surrounding contexts cooperatively and complementarily. The effectiveness of RIP-AV has been validated on three publicly available retinal datasets: AV-DRIVE, LES-AV, and HRF, demonstrating remarkable accuracies of 0.970, 0.967, and 0.981, respectively, thereby outperforming existing state-of-the-art methods. Notably, our method achieves a significant 1.7% improvement in accuracy on the HRF dataset, particularly enhancing the segmentation of thin edge arteries and veins.