Retinal Vessel Segmentation using Deep Neural Networks

Martina Melinscak; Pavle Prentasic; Sven Loncaric

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Paper

Retinal Vessel Segmentation using Deep Neural Networks

Topics: Medical Image Applications; Segmentation and Grouping

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, 577-582, 2015 , Berlin, Germany

Authors: Martina Melinscak ; Pavle Prentasic and Sven Loncaric

Affiliation: The University of Zagreb, Croatia

Keyword(s): Blood Vessel Segmentation, Retinal Imaging, Deep Neural Networks, GPU.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Medical Image Applications ; Segmentation and Grouping

Abstract: Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test our method on publicly-available DRIVE dataset and our results demonstrate the high effectiveness of the deep learning approach. Our method achieves an average accuracy and AUC of 0.9466 and 0.9749, respectively.

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Paper citation in several formats:

Melinscak, M.; Prentasic, P. and Loncaric, S. (2015). Retinal Vessel Segmentation using Deep Neural Networks. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 2: VISAPP; ISBN 978-989-758-089-5; ISSN 2184-4321, SciTePress, pages 577-582. DOI: 10.5220/0005313005770582

@conference{visapp15,
author={Martina Melinscak. and Pavle Prentasic. and Sven Loncaric.},
title={Retinal Vessel Segmentation using Deep Neural Networks},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 2: VISAPP},
year={2015},
pages={577-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005313005770582},
isbn={978-989-758-089-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 2: VISAPP
TI - Retinal Vessel Segmentation using Deep Neural Networks
SN - 978-989-758-089-5
IS - 2184-4321
AU - Melinscak, M.
AU - Prentasic, P.
AU - Loncaric, S.
PY - 2015
SP - 577
EP - 582
DO - 10.5220/0005313005770582
PB - SciTePress