Deep learning artery-vein classification in OCT angiography

Early disease diagnosis and effective treatment assessment are crucial to prevent vision loss. Retinal arteries and veins can be affected differently by different eye diseases, e.g., arterial narrowing and venous beading in diabetic retinopathy (DR). Therefore, differential artery-vein (AV) analysis can provide valuable information for early disease detection and better stage classification. However, manual, or semi-automated methods for AV identification are inefficient in a clinical setting. This study is to demonstrate the use of deep learning for automated AV classification in optical coherence tomography angiography (OCTA). We present ‘AV-Net’, a fully convolutional network (CNN) based on a modified Ushaped architecture. The input to AV-Net is a 2-channel system that combines grayscale enface OCT and OCTA. The enface OCT is a near infrared image, equivalent to a fundus image, which provides the vessel intensity profiles. In contrast, the OCTA contains the information of the blood flow strength, and vessel geometric features. The output of AV-Net is an RGB (red-green-blue) image, with R and B corresponding to arteries and veins, respectively, and the G channel represents the background. The dataset in this study is comprised of images from 50 individuals (20 controls and 30 DR patients). Transfer learning and regularization techniques, such as data augmentation and cross validation, were employed during training to prevent overfitting. The results reveal robust vessel segmentation and AV classification. A fully automated platform is essential for fostering efficient clinical deployment of AI-based screening, diagnosis, and treatment evaluation.