Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE)

Abstract

Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. First, a representation learning with a Vector-Quantized Variational Auto-Encoder (VQ-VAE) followed by Auto-Regressive (AR) modeling. Second, it exploits epistemic uncertainty estimates from Bayesian U-Net employed to segment the vasculature on OCTA en face images. Evaluation on two large public datasets, DRAC and OCTA-500, demonstrates effective anomaly detection (an AUROC of 0.92 for the DRAC and an AUROC of 0.75 for the OCTA-500) and localization (a mean Dice score of 0.61 for the DRAC) on this challenging task. To our knowledge, this is the first work that addresses anomaly detection in OCTA.