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

arXiv: https://arxiv.org/abs/2006.03742 · HTML: https://mlsanity.com/paper/2006.03742/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation](https://mlsanity.com/paper/2310.07183/) (2023)
- [OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection](https://mlsanity.com/paper/2401.12344/) (2024)
- [Cardiac Adipose Tissue Segmentation via Image-Level Annotations](https://mlsanity.com/paper/2206.04238/) (2022)
- [Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification](https://mlsanity.com/paper/2409.11375/) (2024)
- [ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography](https://mlsanity.com/paper/2201.12625/) (2022)
- [Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review](https://mlsanity.com/paper/2502.14935/) (2025)
- [Cross-Modal Fusion of OCT and OCT angiography enface for Improved Diagnostics of Diabetic Retinopathy](https://mlsanity.com/paper/2607.03959/) (2026)

## Abstract (arXiv)

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.