Wang Jie, Hormel Tristan T, You Qisheng, Guo Yukun, Wang Xiaogang, Chen Liu, Hwang Thomas S, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA.
Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.
Biomed Opt Express. 2019 Dec 18;11(1):330-345. doi: 10.1364/BOE.11.000330. eCollection 2020 Jan 1.
Non-perfusion area (NPA) is a quantitative biomarker useful for characterizing ischemia in diabetic retinopathy (DR). Projection-resolved optical coherence tomographic angiography (PR-OCTA) allows visualization of retinal capillaries and quantify NPA in individual plexuses. However, poor scan quality can make current NPA detection algorithms unreliable and inaccurate. In this work, we present a robust NPA detection algorithm using convolutional neural network (CNN). By merging information from OCT angiograms and OCT reflectance images, the CNN could exclude signal reduction and motion artifacts and detect the avascular features from local to global with the resolution preserved. Across a wide range of signal strength indices, and on both healthy and DR eyes, the algorithm achieved high accuracy and repeatability.
无灌注区（NPA）是一种用于表征糖尿病视网膜病变（DR）缺血情况的定量生物标志物。投影分辨光学相干断层扫描血管造影（PR-OCTA）能够可视化视网膜毛细血管并量化各个神经纤维层中的NPA。然而，扫描质量不佳会使当前的NPA检测算法不可靠且不准确。在这项研究中，我们提出了一种使用卷积神经网络（CNN）的稳健NPA检测算法。通过融合来自OCT血管造影和OCT反射图像的信息，CNN可以排除信号衰减和运动伪影，并在保留分辨率的情况下从局部到全局检测无血管特征。在广泛的信号强度指数范围内，以及在健康眼睛和DR眼睛上，该算法都实现了高精度和可重复性。
