  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.
