Retinal Image Segmentation with Small Datasets

Nchongmaje Ndipenoch, Alina Miron, Zidong Wang, Yongmin Li

2023

Abstract

Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version. The Optical Coherence Tomography (OCT), a 3D scan of the retina with high qualitative information about the retinal morphology, can be used to diagnose and monitor changes in the retinal anatomy. Many Deep Learning (DL) methods have shared the success of developing an automated tool to monitor pathological changes in the retina. However, the success of these methods depends mainly on large datasets. To address the challenge from very small and limited datasets, we proposed a DL architecture termed CoNet (Coherent Network) for joint segmentation of layers and fluids in retinal OCT images on very small datasets (less than a hundred training samples). The proposed model was evaluated on the publicly available Duke DME dataset consisting of 110 B-Scans from 10 patients suffering from DME. Experimental results show that the proposed model outperformed both the human experts’ annotation and the current state-of-the-art architectures by a clear margin with a mean Dice Score of 88% when trained on 55 images without any data augmentation.

Paper Citation

in Harvard Style

Ndipenoch N., Miron A., Wang Z. and Li Y. (2023). Retinal Image Segmentation with Small Datasets. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING; ISBN 978-989-758-631-6, SciTePress, pages 129-137. DOI: 10.5220/0011779200003414

in Bibtex Style

@conference{bioimaging23,
author={Nchongmaje Ndipenoch and Alina Miron and Zidong Wang and Yongmin Li},
title={Retinal Image Segmentation with Small Datasets},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING},
year={2023},
pages={129-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011779200003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING
TI - Retinal Image Segmentation with Small Datasets
SN - 978-989-758-631-6
AU - Ndipenoch N.
AU - Miron A.
AU - Wang Z.
AU - Li Y.
PY - 2023
SP - 129
EP - 137
DO - 10.5220/0011779200003414
PB - SciTePress