Retinal Vessel Segmentation using Deep Neural Networks

Martina Melinscak, Pavle Prentasic, Sven Loncaric

2015

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.

Paper Citation

in Harvard Style

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 - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 577-582. DOI: 10.5220/0005313005770582

in Bibtex Style

@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 - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={577-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005313005770582},
isbn={978-989-758-089-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Retinal Vessel Segmentation using Deep Neural Networks
SN - 978-989-758-089-5
AU - Melinscak M.
AU - Prentasic P.
AU - Loncaric S.
PY - 2015
SP - 577
EP - 582
DO - 10.5220/0005313005770582