通过生成修复网络消除阴影伪影以量化脉络膜的血管变化
Eliminating Shadow Artifacts via Generative Inpainting Networks to Quantify Vascular Changes of the Choroid
摘要：
来自视网膜血管的阴影伪影阻碍了光学相干断层扫描（OCT）中定量脉络膜生物标志物的发展，这限制了脉络膜OCT的临床应用于层厚度的测量。在本文中，我们提出了一种新的框架，可以消除视网膜阴影，实现脉络膜血管的量化。与现有的方法不同，我们将阴影消除转化为一个物体去除任务，可以通过图像修复技术来处理。我们采用并微调了一个两阶段的生成修复网络，该网络在重新填充阴影污染区域之前连接血管的边缘。与传统的修复算法相比，该算法在修复脉络膜血管方面表现出了优异的性能。我们进一步验证的可行性，提出的框架使用前瞻性观察研究，检测脉络膜血管的变化相关的眼内压（IOP）升高的34名健康志愿者。当平均IOP从$15.84\pm1.99$ mmHg增加到$34.48\pm5.35$ mmHg时，我们实现脉络膜血管密度和血流指数分别从$0.491\pm0.020$降低到$0.463\pm0.019$和从$0.336\pm0.025$降低到$0.300\pm0.019$。
英文摘要：
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.
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