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Title: Retinal Image Segmentation with Small Datasets
Authors: Ndipenoch, N
Miron, A
Wang, Z
Li, Y
Keywords: medical imaging;retinal layers and fluid segmentation;deep learning;convolutional neural network;optical coherence tomography (OCT)
Issue Date: 16-Feb-2023
Publisher: SciTePress – Science and Technology Publications, Lda.
Citation: Ndipenoch, N. (2023) 'Retinal Image Segmentation with Small Datasets', Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies, Lisbon, Portugal, 16-18 February, Volume 2: BIOIMAGING, pp. 129 - 137. doi: 10.5220/0011779200003414.
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.
Description: Presented at BIOIMAGING 2023 : 10th International Conference on Bioimaging, Lisbon, Portugal. BIOIMAGING is part of BIOSTEC, the 16th International Joint Conference on Biomedical Engineering Systems and Technologies.
ISBN: 978-989-758-631-6
ISSN: 2184-349X
Other Identifiers: ORCID iDs: Alina Miron; Zidong Wang; Yongmin Li
Appears in Collections:Dept of Computer Science Research Papers

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