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

Abstract

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 CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.

Fig. 1.

Network architecture for AV-Net, (a) overview of the blocks in AV-Net architecture, (b) the individual blocks that comprises AV-Net. In this figure, Conv stands for convolution operations, AP stands for Average Pooling operation. Each transition block has two outputs, Output A is the output of the AP operation, and Output B is the output of the Conv operation. The skip-connections from each transition block are Output B. In the decoder block, the Input A is the output of the preceding layer, whereas Output B is the output of the appropriately sized transition block.

Fig. 2.

Examples of control and DR (top and bottom, respectively) (a) input OCTA, (b) enface OCT, (c) the ground truth, (d) UNet predicted AV-maps, and (e) AV-Net predicted AV-maps.