Segmentation of Diabetic Retinopathy Lesions by Deep Learning: Achievements and Limitations

Pedro Furtado

Abstract

Analysis of Eye Fundus Images (EFI) allows early diagnosis and grading of Diabetic Retinopathy (DR), detecting micro-aneurisms, exudates, haemorrhages, neo-vascularizations and other signs. Automated detection of individual lesions helps visualizing, characterizing and determining degree of DR. Today modified deep convolution neural networks (DCNNs) are state-of-the-art in most segmentation tasks. But the task of segmenting lesions in EFI is challenging due to sizes, varying shapes, similarity and lack of contrast with other parts of the EFI, so that the results are ambiguous. In this paper we test two DCNNs to do a preliminary evaluation of the strengths and limitations using publicly available data. We already conclude that the accuracies are good but the segmentations still have relevant deficiencies. Based on this, we identify the need for further assessment and suggest future work to improve segmentation approaches.

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Paper Citation


in Harvard Style

Furtado P. (2020). Segmentation of Diabetic Retinopathy Lesions by Deep Learning: Achievements and Limitations.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-398-8, pages 95-101. DOI: 10.5220/0008881100950101


in Bibtex Style

@conference{bioimaging20,
author={Pedro Furtado},
title={Segmentation of Diabetic Retinopathy Lesions by Deep Learning: Achievements and Limitations},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year={2020},
pages={95-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008881100950101},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,
TI - Segmentation of Diabetic Retinopathy Lesions by Deep Learning: Achievements and Limitations
SN - 978-989-758-398-8
AU - Furtado P.
PY - 2020
SP - 95
EP - 101
DO - 10.5220/0008881100950101