在糖尿病视网膜病变中利用深度学习对拼接式超广角光学相干断层扫描血管造影中的无灌注区进行定量分析。
Quantification of Nonperfusion Area in Montaged Widefield OCT Angiography Using Deep Learning in Diabetic Retinopathy.
作者信息
Guo Yukun, Hormel Tristan T, Gao Liqin, You Qisheng, Wang Bingjie, Flaxel Christina J, Bailey Steven T, Choi Dongseok, Huang David, Hwang Thomas S, Jia Yali
机构信息
Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
出版信息
Ophthalmol Sci. 2021 May 12;1(2):100027. doi: 10.1016/j.xops.2021.100027. eCollection 2021 Jun.
PURPOSE
To examine the efficacy of a deep learning-based algorithm to quantify the nonperfusion area (NPA) on montaged widefield OCT angiography (OCTA) for assessment of diabetic retinopathy (DR) severity.
DESIGN
Cross-sectional study.
PARTICIPANTS
One hundred thirty-seven participants with a full range of DR severity and 26 healthy participants.
METHODS
A deep learning-based algorithm was developed for detecting and quantifying NPA in the superficial vascular complex on widefield OCTA comprising 3 horizontally montaged 6 × 6-mm OCTA scans from the nasal, macular, and temporal regions. We trained the algorithm on 978 volumetric OCTA scans from all participants using 5-fold cross-validation. The algorithm can distinguish NPA from shadow artifacts. The F1 score evaluated segmentation accuracy. The area under the receiver operating characteristic curve and sensitivity with specificity fixed at 95% quantified network performance to distinguish patients with diabetes from healthy control participants, referable DR from nonreferable DR (nonproliferative DR [NPDR] less than moderate severity), and severe DR (severe NPDR, proliferative DR, or DR with edema) from nonsevere DR (mild to moderate NPDR).
MAIN OUTCOME MEASURES
Widefield OCTA NPA, visual acuity (VA), and DR severities.
RESULTS
Automatically segmented NPA showed high agreement with the manually delineated ground truth, with a mean ± standard deviation F1 score of 0.78 ± 0.05 in nasal, 0.82 ± 0.07 in macular, and 0.78 ± 0.05 in temporal scans. The extrafoveal avascular area (EAA) in the macular scan showed the best sensitivity at 54% for differentiating those with diabetes from healthy control participants, whereas montaged widefield OCTA scan showed significantly higher sensitivity than macular scans ( < 0.0001, McNemar's test) for detecting eyes with DR at 66%, referable DR at 63%, and severe DR at 62%. Montaged widefield OCTA showed the highest correlation (Spearman ρ = 0.74; < 0.0001) between EAA and DR severity. The macular scan showed the strongest negative correlation (Pearson ρ = -0.42; < 0.0001) between EAA and best-corrected VA.
CONCLUSIONS
A deep learning-based algorithm for montaged widefield OCTA can detect NPA accurately and can improve the detection of clinically important DR.
目的
研究基于深度学习的算法对拼接式超广角光学相干断层扫描血管造影（OCTA）上的无灌注区（NPA）进行量化，以评估糖尿病视网膜病变（DR）严重程度的有效性。
设计
横断面研究。
参与者
137名患有各种严重程度DR的参与者和26名健康参与者。
方法
开发了一种基于深度学习的算法，用于在超广角OCTA的浅表血管复合体中检测和量化NPA，该OCTA由来自鼻侧、黄斑和颞侧区域的3幅水平拼接的6×6-mm OCTA扫描图像组成。我们使用5折交叉验证在所有参与者的978幅容积OCTA扫描图像上训练该算法。该算法可以将NPA与阴影伪影区分开来。F1分数用于评估分割准确性。在特异性固定为95%的情况下，受试者工作特征曲线下面积和灵敏度量化了网络区分糖尿病患者与健康对照参与者、可转诊DR与不可转诊DR（非增殖性DR [NPDR] 低于中度严重程度）以及重度DR（重度NPDR、增殖性DR或伴有水肿的DR）与非重度DR（轻度至中度NPDR）的性能。
主要观察指标
超广角OCTA的NPA、视力（VA）和DR严重程度。
结果
自动分割的NPA与手动勾勒的真实情况高度一致，鼻侧扫描的平均±标准差F1分数为0.78±0.05，黄斑扫描为0.82±0.07，颞侧扫描为0.78±0.05。黄斑扫描中的黄斑外无血管区（EAA）在区分糖尿病患者与健康对照参与者方面显示出最佳灵敏度，为54%，而拼接式超广角OCTA扫描在检测患有DR的眼睛方面显示出比黄斑扫描显著更高的灵敏度（P<0.0001，McNemar检验），检测可转诊DR的灵敏度为63%，检测重度DR的灵敏度为62%。拼接式超广角OCTA显示EAA与DR严重程度之间的相关性最高（Spearman ρ = 0.74；P<0.0001）。黄斑扫描显示EAA与最佳矫正视力之间的负相关性最强（Pearson ρ = -0.42；P<0.0001）。
结论
一种用于拼接式超广角OCTA的基于深度学习的算法可以准确检测NPA，并可改善对具有临床重要性的DR的检测。
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