全自动分割视网膜萎缩与光感受器厚度，精准监测老年黄斑变性。
Fully Automated High-Precision Segmentation of Retinal Atrophy and Ellipsoid Zone Thickness in OCT: A Reliable Tool for Real-World GA Monitoring
- 用三个深度学习模型自动分割RPE和光感受器层的损伤边界。
- 分割精确度达Dice系数0.87以上，厚度测量误差仅2.15μm。
- 适合临床试验和日常眼科诊疗，结果稳定可靠。
年龄相关性黄斑变性（AMD）引起的地图样萎缩（GA）需精确监测结构生物标志物以评估疾病阶段、进展及治疗反应。本文提出一种完全自动化的深度学习框架，实现光学相干断层扫描（OCT）中关键生物标志物的高精度像素级分割：视网膜色素上皮（RPE）损失、椭圆体区（EZ）损失及EZ变薄。该流程采用三个专用语义分割模型，分别识别RPE损失、EZ边界（含中断）及布鲁赫膜。模型基于包含298例SD-OCT体积的多样化数据集训练，涵盖完整表型谱的AMD（GA:222，中间期AMD:40，新生血管性AMD:17，健康:19），并在独立外部数据集（n=43）上验证。评估还包含重复性、阅片者间一致性、B-scan密度影响及按病灶大小分组的表现分析。结果显示分割准确率高（RPE损失Dice: 0.88，EZ损失Dice: 0.87，Pearson相关系数>0.99），总EZ厚度测量平均偏差为2.15 μm，可靠性经证实（组内相关系数ICC > 0.98）。该全自动框架可精准一致地量化外层光感受器退化与RPE损失，为临床试验及真实世界眼科诊疗中的GA评估提供高度可靠的工具。
原文摘要 · Abstract (English)
Geographic atrophy (GA) secondary to age-related macular degeneration (AMD) requires precise monitoring of relevant structural biomarkers to assess disease stage, progression, and treatment response. This paper presents a fully automated, deep learning-based framework for the high-precision, pixel-wise segmentation of key biomarkers in optical coherence tomography (OCT) imaging: retinal pigment epithelium (RPE) loss, ellipsoid zone (EZ) loss, and EZ thinning. The proposed pipeline uses three specialized semantic segmentation models to delineate RPE loss, EZ boundaries (including interruptions), and Bruch's membrane. To ensure robustness and generalizability, the models were developed on a diverse dataset of 298 SD-OCT volumes representing the full phenotypic spectrum of AMD (GA:222, intermediate AMD: 40, neovascular AMD: 17, healthy: 19) and validated on an independent external dataset (n=43). The comprehensive evaluation was further strengthened using additional datasets to assess repeatability, inter-reader reliability, the impact of B-scan density on measurement accuracy, and subgroup performance stratified by lesion size. Results demonstrated high segmentation accuracy (Dice RPE loss: 0.88, Dice EZ loss: 0.87, Pearson's r > 0.99). Total EZ thickness measurements exhibited a sub-pixel average deviation of 2.15 $μm$, and segmentation reliability was confirmed by a strong reproducibility score (ICC > 0.98). By accurately and consistently quantifying outer photoreceptor degeneration and RPE loss, this fully automated framework provides a highly reliable tool for GA assessment in both clinical trials and routine real-world ophthalmic care.
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