A Deep Learning Network for Classifying Arteries and Veins in Montaged Widefield OCT Angiograms.

ABSTRACT:

Purpose

To propose a deep-learning-based method to differentiate arteries from veins in montaged widefield OCT angiography (OCTA).

Design

Cross-sectional study.

Participants

A total of 232 participants, including 109 participants with diabetic retinopathy (DR), 64 participants with branch retinal vein occlusion (BRVO), 27 participants with diabetes but without DR, and 32 healthy participants.

Methods

We propose a convolutional neural network (CAVnet) to classify retinal blood vessels on montaged widefield OCTA en face images as arteries and veins. A total of 240 retinal angiograms from 88 eyes were used to train CAVnet, and 302 retinal angiograms from 144 eyes were used for testing. This method takes the OCTA images as input and outputs the segmentation results

SUBMITTER: Gao M 

PROVIDER: S-EPMC9562370 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature