# An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography

arXiv: https://arxiv.org/abs/2307.00904 · HTML: https://mlsanity.com/paper/2307.00904/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Edge Tracing using Gaussian Process Regression](https://mlsanity.com/paper/2111.03605/) (2021)
- [Evaluation of an automated choroid segmentation algorithm in a longitudinal kidney donor and recipient cohort](https://mlsanity.com/paper/2306.10952/) (2023)
- [Automatic Segmentation of Choroid Layer in EDI OCT Images Using Graph Theory in Neutrosophic Space](https://mlsanity.com/paper/1812.01989/) (2018)

## Built on by (most cited first, 4 of 4)

- [Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography](https://mlsanity.com/paper/2312.02956/) (2023)
- [SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images](https://mlsanity.com/paper/2406.16466/) (2024)
- [Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images](https://mlsanity.com/paper/2405.14453/) (2024)
- [OCTolyzer: Fully automatic toolkit for segmentation and feature extracting in optical coherence tomography and scanning laser ophthalmoscopy data](https://mlsanity.com/paper/2407.14128/) (2024)

## Abstract (arXiv)

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 (±15.09) using GPET to 1.25s (±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.