loading
Documents

Research.Publish.Connect.

Paper

Authors: Erol Kazancli 1 ; Vesna Prchkovska 2 ; Paulo Rodrigues 2 ; Pablo Villoslada 3 and Laura Igual 4

Affiliations: 1 Universitat de Barcelona and Universitat Politècnica de Catalunya, Spain ; 2 Mint Labs Inc., United States ; 3 Institut d’Investigacions Biomediques August Pi Sunyer (IDIBAPS), Spain ; 4 Universitat de Barcelona, Spain

ISBN: 978-989-758-290-5

Keyword(s): Multiple Sclerosis Lesion Segmentation, Deep Learning, Convolutional Neural Networks.

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: 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 positiv e 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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.236.246.85

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.