Purpose: To develop an open-source, fully-automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from 3 clinical studies related to systemic disease. Ground truth segmentations were generated using a clinically validated, semi-automatic choroid segmentation method, Gaussian Process Edge Tracing (GPET). We finetuned a UNet with MobileNetV3 backbone pre-trained on ImageNet. Standard segmentation agreement metrics, as well as derived measures of choroidal thickness and area, were used to evaluate DeepGPET, alongside qualitative evaluation from a clinical ophthalmologist. Results: DeepGPET achieves excellent agreement with GPET on data from 3 clinical studies (AUC=0.9994, Dice=0.9664; Pearson correlation of 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34.49s ($\pm$15.09) using GPET to 1.25s ($\pm$0.10) using DeepGPET. Both methods performed similarly according to a clinical ophthalmologist, who qualitatively judged a subset of segmentations by GPET and DeepGPET, based on smoothness and accuracy of segmentations. Conclusions: DeepGPET, a fully-automatic, open-source algorithm for choroidal segmentation, will enable researchers to efficiently extract choroidal measurements, even for large datasets. As no manual interventions are required, DeepGPET is less subjective than semi-automatic methods and could be deployed in clinical practice without necessitating a trained operator.

翻译：目的：开发用于光学相干断层扫描（OCT）数据中脉络膜区域分割的开源全自动深度学习算法DeepGPET。方法：采用来自3项系统性相关临床研究的715幅OCT B扫描图像（82名受试者，115只眼睛）。利用临床验证的半自动脉络膜分割方法——高斯过程边缘追踪（GPET）生成真实分割标签。在ImageNet预训练的MobileNetV3主干UNet模型上进行微调。采用标准分割一致性指标及衍生的脉络膜厚度与面积测量值评估DeepGPET，并由临床眼科医生进行定性评价。结果：DeepGPET在三个临床研究数据中与GPET具有优异一致性（AUC=0.9994，Dice=0.9664；脉络膜厚度Pearson相关系数0.8908，面积相关系数0.9082），同时在标准笔记本电脑CPU上将单幅图像平均处理时间从GPET的34.49秒（±15.09）降至1.25秒（±0.10）。临床眼科医生基于分割平滑性与准确性对GPET与DeepGPET的部分分割结果进行定性评判，两种方法表现相近。结论：全自动开源算法DeepGPET使研究者能够高效提取脉络膜测量参数，尤其适用于大规模数据集。由于无需人工干预，DeepGPET较半自动方法更具客观性，可在无需专业操作人员的情况下部署于临床实践。