Using Segmentation Networks on Diabetic Retinopathy Lesions: Metrics, Results and Challenges

Pedro Furtado

2021

Abstract

Deep segmentation networks are increasingly used in medical imaging, including detection of Diabetic Retinopathy lesions from eye fundus images (EFI). In spite of very high scores in most EFI analysis tasks, segmentation measured as precise delineation of instances of lesions still involves some challenges and deserves analysis of metrics and comparison with prior deep learning approaches. We build and confront state-of-the-art deep learning segmentation networks with prior results, showing up to 15 percentage points improvement in sensitivity, depending on the lesion. But we also show the importance of metrics and that many frequently used metrics can be deceiving in this context. We use visual and numeric evidence to show why there is still ample space for further improvements of semantic segmentation quality in the context of EFI lesions.

Paper Citation

in Harvard Style

Furtado P. (2021). Using Segmentation Networks on Diabetic Retinopathy Lesions: Metrics, Results and Challenges. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING; ISBN 978-989-758-490-9, SciTePress, pages 128-135. DOI: 10.5220/0010208500002865

in Bibtex Style

@conference{bioimaging21,
author={Pedro Furtado},
title={Using Segmentation Networks on Diabetic Retinopathy Lesions: Metrics, Results and Challenges},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING},
year={2021},
pages={128-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010208500002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING
TI - Using Segmentation Networks on Diabetic Retinopathy Lesions: Metrics, Results and Challenges
SN - 978-989-758-490-9
AU - Furtado P.
PY - 2021
SP - 128
EP - 135
DO - 10.5220/0010208500002865
PB - SciTePress