Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks

Erol Kazancli, Vesna Prchkovska, Paulo Rodrigues, Pablo Villoslada, Laura Igual

Abstract

The Multiple Sclerosis (MS) lesion segmentation is critical for the diagnosis, treatment and follow-up of the MS patients. Nowadays, the MS lesion segmentation in Magnetic Resonance Image (MRI) is a time-consuming manual process carried out by medical experts, which is subject to intra- and inter- expert variability. Machine learning methods including Deep Learning has been applied to this problem, obtaining solutions that outperformed other conventional automatic methods. Deep Learning methods have especially turned out to be promising, attaining human expert performance levels. Our aim is to develop a fully automatic method that will help experts in their task and reduce the necessary time and effort in the process. In this paper, we propose a new approach based on Convolutional Neural Networks (CNN) to the MS lesion segmentation problem. We study different CNN approaches and compare their segmentation performance. We obtain an average dice score of 57.5% and a true positive rate of 59.7% for a real dataset of 59 patients with a specific CNN approach, outperforming the other CNN approaches and a commonly used automatic tool for MS lesion segmentation.

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Paper Citation


in Harvard Style

Kazancli E., Prchkovska V., Rodrigues P., Villoslada P. and Igual L. (2018). Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 260-269. DOI: 10.5220/0006540902600269


in Bibtex Style

@conference{visapp18,
author={Erol Kazancli and Vesna Prchkovska and Paulo Rodrigues and Pablo Villoslada and Laura Igual},
title={Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={260-269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006540902600269},
isbn={978-989-758-290-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks
SN - 978-989-758-290-5
AU - Kazancli E.
AU - Prchkovska V.
AU - Rodrigues P.
AU - Villoslada P.
AU - Igual L.
PY - 2018
SP - 260
EP - 269
DO - 10.5220/0006540902600269