Evaluation of OCT Image Synthesis for Choroidal and Retinal Layer Segmentation Using Denoising Diffusion Probabilistic Models

Yudai Yamauchi, Yudai Yamauchi, Yuli Wu, Eiji Okada

2025

Abstract

Machine learning can automatically conduct the layer segmentation task of retinal optical coherence tomography (OCT) image, but annotated data is required to train these models. Synthetic retinal OCT images are generated using denoising diffusion probabilistic models (DDPMs), which can be used to train segmentation models effectively and automatically create annotated data. However, the extent to which these synthetic images contribute to segmentation accuracy compared to real data has not been investigated. In this study, we synthesized retinal OCT images from sketch images using DDPMs, trained a segmentation model using synthetic and real images, and evaluated how the use of synthetic images influenced the accuracy of choroidal and retinal layer segmentation compared to results using only real images. Through a comparison of the Dice score, we confirmed that training with both synthetic and real OCT images led to higher Dice scores than training with only real OCT images. These findings suggest that using synthetic images can enhance segmentation accuracy, offering a promising approach to improving model performance in situations with limited annotated real data.

Paper Citation

in Harvard Style

Yamauchi Y., Wu Y. and Okada E. (2025). Evaluation of OCT Image Synthesis for Choroidal and Retinal Layer Segmentation Using Denoising Diffusion Probabilistic Models. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 340-347. DOI: 10.5220/0013246600003911

in Bibtex Style

@conference{bioimaging25,
author={Yudai Yamauchi and Yuli Wu and Eiji Okada},
title={Evaluation of OCT Image Synthesis for Choroidal and Retinal Layer Segmentation Using Denoising Diffusion Probabilistic Models},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={340-347},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013246600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Evaluation of OCT Image Synthesis for Choroidal and Retinal Layer Segmentation Using Denoising Diffusion Probabilistic Models
SN - 978-989-758-731-3
AU - Yamauchi Y.
AU - Wu Y.
AU - Okada E.
PY - 2025
SP - 340
EP - 347
DO - 10.5220/0013246600003911
PB - SciTePress