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
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 positi
ve 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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