Eliminating Shadow Artifacts via Generative Inpainting Networks to Quantify Vascular Changes of the Choroid

TL;DR

This paper introduces a novel generative inpainting framework to remove shadow artifacts from retinal OCT images, enabling accurate quantification of choroidal vasculature and detecting vascular changes related to intra-ocular pressure elevation.

Contribution

The paper presents a two-stage generative inpainting network specifically designed for shadow removal in OCT images, improving vascular quantification over traditional methods.

Findings

The inpainting method surpasses traditional algorithms in repairing choroidal vasculature.

The framework successfully detects vascular changes associated with intra-ocular pressure elevation.

Quantitative measurements of vessel density and flow index decrease with increased IOP.

Abstract

Shadow artifacts from retinal vessels hinder the development of quantitative choroidal biomarkers in Optical Coherence Tomography (OCT), which limits the clinical applications of the choroidal OCT to the measures of layer thickness. In this paper, we present a novel framework that could eliminate the retinal shadows and realize the quantification of choroidal vasculature. Different from existing methods, we convert the shadow elimination into an object removal task, which can be handled by image inpainting techniques. We adopt and finetune a two-stage generative inpainting network which connects the edges of the vessels ahead of refilling the shadow-contaminated areas. It shows surpassing performance in repairing the choroidal vasculature compared with traditional inpainting algorithms. We further verify the feasibility of the proposed frame using a prospective observational study, which detects the choroidal vascular changes related to the Intra-Ocular Pressure (IOP) elevation of 34 healthy volunteers. As the average IOP increases from $15.84\pm1.99$ mmHg to $34.48\pm5.35$ mmHg, we achieve the decreases of the choroidal vessel density and flow index from $0.491\pm0.020$ to $0.463\pm0.019$ and from $0.336\pm0.025$ to $0.300\pm0.019$, respectively.