Purpose To develop and validate a deep learning model for the segmentation of five retinal biomarkers associated with neovascular age-related macular degeneration (nAMD). Methods 300 optical coherence tomography volumes from subject eyes with nAMD were collected. Images were manually segmented for the presence of five crucial nAMD features: intraretinal fluid, subretinal fluid, subretinal hyperreflective material, drusen/drusenoid pigment epithelium detachment (PED) and neovascular PED. A deep learning architecture based on a U-Net was trained to perform automatic segmentation of these retinal biomarkers and evaluated on the sequestered data. The main outcome measures were receiver operating characteristic curves for detection, summarised using the area under the curves (AUCs) both on a per slice and per volume basis, correlation score, enface topography overlap (reported as two-dimensional (2D) correlation score) and Dice coefficients. Results The model obtained a mean (±SD) AUC of 0.93 (±0.04) per slice and 0.88 (±0.07) per volume for fluid detection. The correlation score (R2) between automatic and manual segmentation obtained by the model resulted in a mean (±SD) of 0.89 (±0.05). The mean (±SD) 2D correlation score was 0.69 (±0.04). The mean (±SD) Dice score resulted in 0.61 (±0.10). Conclusions We present a fully automated segmentation model for five features related to nAMD that performs at the level of experienced graders. The application of this model will open opportunities for the study of morphological changes and treatment efficacy in real-world settings. Furthermore, it can facilitate structured reporting in the clinic and reduce subjectivity in clinicians’ assessments. Data are available on reasonable request.

中文翻译：

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验证深度学习模型，用于自动检测和量化与新生血管性年龄相关性黄斑变性相关的五种 OCT 关键视网膜特征

目的 开发和验证深度学习模型，用于分割与新生血管性年龄相关性黄斑变性 (nAMD) 相关的五种视网膜生物标志物。方法 收集来自 nAMD 受试者眼睛的 300 个光学相干断层扫描体积。对图像进行手动分割，以确定是否存在五个关键的 nAMD 特征：视网膜内液体、视网膜下液体、视网膜下高反射物质、玻璃疣/玻璃疣样色素上皮脱离 (PED) 和新生血管 PED。基于 U-Net 的深度学习架构经过训练，可以对这些视网膜生物标记物进行自动分割，并根据隔离的数据进行评估。主要结果指标是用于检测的接收者操作特征曲线，使用每切片和每体积的曲线下面积 (AUC) 进行总结、相关评分、表面形貌重叠（报告为二维 (2D) 相关评分）和骰子系数。结果 该模型获得的液体检测平均 (±SD) AUC 为每切片 0.93 (±0.04) 和每体积 0.88 (±0.07)。该模型获得的自动和手动分割之间的相关性得分 (R2) 的平均值 (±SD) 为 0.89 (±0.05)。平均 (±SD) 2D 相关评分为 0.69 (±0.04)。平均 (±SD) Dice 得分为 0.61 (±0.10)。结论 我们针对与 nAMD 相关的五个特征提出了一个完全自动化的分割模型，其性能达到了经验丰富的分级人员的水平。该模型的应用将为研究现实环境中的形态变化和治疗效果提供机会。此外，它可以促进临床中的结构化报告并减少临床医生评估的主观性。可根据合理要求提供数据。