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±1.99 mmHg to 34.48±5.35 mmHg, we achieve the decreases of the choroidal vessel density and flow index from 0.491±0.020 to 0.463±0.019 and from 0.336±0.025 to 0.300±0.019, respectively.
@article{arxiv.1907.01271,
title = {Eliminating Shadow Artifacts via Generative Inpainting Networks to Quantify Vascular Changes of the Choroid},
author = {Huihong Zhang and Jianlong Yang and Kang Zhou and Fei Li and Yan Hu and Shenghua Gao and Xiulan Zhang and Jiang Liu},
journal= {arXiv preprint arXiv:1907.01271},
year = {2019}
}