Diagnosis of Choroidal Disease With Deep Learning-Based Image Enhancement and Volumetric Quantification of Optical Coherence Tomography.
Diagnosis of Choroidal Disease With Deep Learning-Based Image Enhancement and Volumetric Quantification of Optical Coherence Tomography.
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DOI：
10.1167/tvst.11.1.22
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发表时间：
2022-01-03
影响因子：
3
通讯作者：
Nishida K
中科院分区：
文献类型：
--
作者：
Maruyama K;Mei S;Sakaguchi H;Hara C;Miki A;Mao Z;Kawasaki R;Wang Z;Sakimoto S;Hashida N;Quantock AJ;Chan K;Nishida K
本研究的目的是使用光学相干断层扫描（OCT）和深度学习分析在三维（3D）中量化病理眼的脉络膜血管（CV）。一项包括34例患者34只眼的单中心回顾性研究（7名女性和27名男性），患有未经治疗的中心性浆液性脉络膜视网膜病变（CSC），17名患者的33只眼在2012年10月至2019年5月期间，对患有Vogt-Koyanagi-Harada病（VKH）或交感性眼炎（SO）的患者（7名女性和10名男性）进行了连续成像，使用扫频源OCT。对39名年龄匹配的志愿者（26名女性和13名男性）的7只眼睛进行成像以进行比较，所述志愿者没有眼部病变的迹象。基于深度学习的图像增强管道实现了CV分割和3D可视化，之后获取定量血管体积图以比较正常和患病眼睛并跟踪疾病组中眼睛的临床过程。基于区域的血管容积和血管指数用于疾病诊断。基于OCT的CV容积图显示CSC、VKH或SO患者的局部CV变化。三个度量（i）脉络膜体积、（ii）CV体积和（iii）CV指数）在区分病理性脉络膜与健康脉络膜方面表现出高灵敏度和特异性。本文描述的OCT图像的深度学习分析提供了脉络膜的3D可视化，并允许量化数据集中的特征，以识别脉络膜疾病并区分不同的疾病。这种新颖的分析可以回顾性地应用于现有的OCT数据集，并且它代表了基于脉管系统的观察和量化的脉络膜病理的自动诊断的显著进步。
The purpose of this study was to quantify choroidal vessels (CVs) in pathological eyes in three dimensions (3D) using optical coherence tomography (OCT) and a deep-learning analysis. A single-center retrospective study including 34 eyes of 34 patients (7 women and 27 men) with treatment-naïve central serous chorioretinopathy (CSC) and 33 eyes of 17 patients (7 women and 10 men) with Vogt-Koyanagi-Harada disease (VKH) or sympathetic ophthalmitis (SO) were imaged consecutively between October 2012 and May 2019 with a swept source OCT. Seventy-seven eyes of 39 age-matched volunteers (26 women and 13 men) with no sign of ocular pathology were imaged for comparison. Deep-learning-based image enhancement pipeline enabled CV segmentation and visualization in 3D, after which quantitative vessel volume maps were acquired to compare normal and diseased eyes and to track the clinical course of eyes in the disease group. Region-based vessel volumes and vessel indices were utilized for disease diagnosis. OCT-based CV volume maps disclose regional CV changes in patients with CSC, VKH, or SO. Three metrics, (i) choroidal volume, (ii) CV volume, and (iii) CV index, exhibit high sensitivity and specificity in discriminating pathological choroids from healthy ones. The deep-learning analysis of OCT images described here provides a 3D visualization of the choroid, and allows quantification of features in the datasets to identify choroidal disease and distinguish between different diseases. This novel analysis can be applied retrospectively to existing OCT datasets, and it represents a significant advance toward the automated diagnosis of choroidal pathologies based on observations and quantifications of the vasculature.
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