SPC-Net: Structure-Aware Pixel-Level Contrastive Learning Network for OCTA A/V Segmentation and Differentiation

Recent studies have indicated that morphological changes in retinal vessels are associated with many ophthalmic diseases, which have different impacts on arteries and veins (A/V) respectively. To this end, retinal vessel segmentation and further A/V classification are essential for quantitative analysis of related diseases. OCTA is a newly non-invasive vascular imaging technique that provides visualization of microvasculatures with higher resolution than traditional fundus imaging modality. Recently, the task of A/V classification has attracted a lot of attention in the field of OCTA imaging. However, there exist two main challenges in this task. On one hand, there is a lack of intensity information in OCTA images to differentiate between arteries and veins. On the other hand, signal fluctuations during OCTA imaging could also bring about vessel discontinuity. In this paper, we propose a novel Structure-aware Pixel-level Contrastive learning network (SPC-Net) for A/V classification. In the proposed SPC-Net, a latent alignment-based network is first utilized to produce a vessel segmentation map in the original OCTA images. Then a pixel-level contrast learning-based network is used to further differentiate between arteries and veins according to the topology of vessels. This network adopts a novel pixel-level contrast learning topology loss to accurately classify the vessel pixels into arteries and veins by taking full account of global semantic similarity. The experimental results demonstrate the superiority of our method compared with the existing state-of-the-art methods respectively on one public OCTA dataset and one in-house OCTA dataset.