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This study is to demonstrate deep learning for automated artery-vein (AV)classification in optical coherence tomography angiography (OCTA). The AV-Net,a fully convolutional network (FCN) based on modified U-shaped CNNarchitecture, incorporates enface OCT and OCTA to differentiate arteries andveins. For the multi-modal training process, the enface OCT works as a nearinfrared fundus image to provide vessel intensity profiles, and the OCTAcontains blood flow strength and vessel geometry features. A transfer learningprocess is also integrated to compensate for the limitation of availabledataset size of OCTA, which is a relatively new imaging modality. By providingan average accuracy of 86.75%, the AV-Net promises a fully automated platformto foster clinical deployment of differential AV analysis in OCTA.

AV-Net: Deep learning for fully automated artery-vein classification in optical coherence tomography angiography