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. DeepGPET addresses the lack of open-source, fully-automatic and clinically relevant choroid segmentation algorithms, and its subsequent public release will facilitate future choroidal research both in ophthalmology and wider systemic health.

翻译：目的：开发一种名为DeepGPET的开源全自动深度学习算法，用于光学相干断层扫描（OCT）数据中脉络膜区域的分割。方法：我们使用了来自3项系统性疾病相关临床研究的715张OCT B扫描图像数据集（82名受试者，115只眼）。基于一种经临床验证的半自动脉络膜分割方法——高斯过程边缘追踪（GPET）生成金标准分割。我们对预训练于ImageNet的MobileNetV3骨干UNet进行了微调。采用标准分割一致性指标以及脉络膜厚度与面积的衍生测量指标对DeepGPET进行评估，同时由临床眼科医生进行定性评估。结果：DeepGPET在3项临床研究数据上与GPET实现了高度一致性（AUC=0.9994，Dice=0.9664；脉络膜厚度Pearson相关系数为0.8908，脉络膜面积相关系数为0.9082），同时将标准笔记本电脑CPU上每幅图像的平均处理时间从GPET的34.49秒（±15.09）降至DeepGPET的1.25秒（±0.10）。临床眼科医生对GPET与DeepGPET的部分分割结果进行基于平滑度与准确性的定性判断后，认为两种方法表现相似。结论：DeepGPET作为一款全自动开源的脉络膜分割算法，将使研究人员能够高效提取脉络膜测量数据，甚至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，且可在无需专业操作人员的情况下部署于临床实践。DeepGPET填补了开源、全自动且具有临床实用性的脉络膜分割算法的空白，其公开发布将促进眼科及更广泛系统健康领域的脉络膜研究发展。