OCT中AMD与DME病灶的2D和3D自动分割

中文摘要 AI 辅助

年龄相关性黄斑变性（AMD）和糖尿病黄斑水肿（DME）是视力丧失的主要原因，光学相干断层扫描（OCT）是检测和监测驱动治疗决策的细微病灶的标准模态。目前大多数针对OCT的深度学习分割工作仅在域内验证，未测试其对不同采集协议下临床数据的泛化能力。本研究开发并系统消融了四种病灶分割流水线——AMD和DME的2D及3D变体，在域内验证集上达到了0.76至0.82的Dice系数，且具有强体积和表面校准性（四种流水线的体积相关系数r_vol、表面相关系数r_surf均≥0.97）。消融过程确立了全体积、校准感知的采用标准，该标准可捕捉普通切片级评估会忽略的机制，并确定集成组合是提升性能最一致的驱动因素。为测试泛化性，研究在OLIVES（无病灶级真值的外部临床队列）上对模型进行评估，使用围绕生物标志物AUROC、中心子场厚度（CST）相关性和纵向一致性构建的代理指标框架。预测结果显示，模型在训练分布外仍能追踪临床生物标志物，尽管强度弱于域内表现，这为病灶负荷自动追踪作为临床工具提供了证据，但并非对其的验证。

英文摘要

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are leading causes of vision loss, and optical coherence tomography (OCT) is the standard modality for detecting and monitoring the subtle lesions that drive treatment decisions. Most deep-learning segmentation work for OCT is validated only in-domain, leaving generalization to clinical data collected under different acquisition protocols largely untested. This work develops and systematically ablates four lesion-segmentation pipelines -- 2D and 3D variants for AMD and DME -- reaching Dice scores of 0.76 to 0.82 with strong volumetric and surface calibration (r vol, r surf greater than or equal to 0.97 across all four pipelines) on an in-domain validation set. The ablation process establishes a full-volume, calibration-aware adoption standard that catches mechanisms an ordinary slice-level evaluation would keep, and identifies ensemble composition as the most consistent driver of improvement. To test generalization, the models are evaluated on OLIVES, an external clinical cohort with no lesion-level ground truth, using a proxy-metric framework built around biomarker AUROC, central subfield thickness (CST) correlation, and longitudinal concordance. Predictions track clinical biomarkers outside the training distribution, though less strongly than in-domain -- evidence for, not validation of, automated lesion-burden tracking as a clinical tool.

发表机构

• Technical University of Munich（慕尼黑工业大学）
• University of Wisconsin-Madison（威斯康星大学麦迪逊分校）

机构由 AI 辅助整理，请以论文原文为准。