White Matter Lesion Segmentation for Multiple Sclerosis Patients implementing deep learning

The aim of this work is to address the problem of White Matter Lesion (WML) segmentation employing Magnetic Resonance Imaging (MRI) images from Multiple Sclerosis (MS) patients through the application of deep learning. A U-net based architecture containing a contrastive path and an expanding path pr...

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Bibliographic Details
Published in:	2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 3818 - 3821
Main Authors:	Papadopoulos, Theofilos G., Tripoliti, Evanthia E., Plati, Daphne, Zelilidou, Styliani, Vlachos, Kostas, Konitsiotis, Spiros, Fotiadis, Dimitrios I.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 11-07-2022
Subjects:	Deep learning Ethics Europe Hospitals Image segmentation Magnetic resonance imaging Multiple sclerosis
Online Access:	Get full text
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Summary:	The aim of this work is to address the problem of White Matter Lesion (WML) segmentation employing Magnetic Resonance Imaging (MRI) images from Multiple Sclerosis (MS) patients through the application of deep learning. A U-net based architecture containing a contrastive path and an expanding path prior to the final pixel-wise classification is implemented. The data are provided by the Ippokratio Radiology Center of Ioannina and include Fluid-Attenuated Inversion Recovery (FLAIR) MRI images from 30 patients in three phases, baseline and two follow ups. The prediction results are quite significant in terms of pixel-wise classification. The implemented deep learning model demonstrates Dice coefficient 0.7292, Precision 75.92% and Recall 70.16% in 2D slices of FLAIR MRI non-skull stripped images.
ISSN:	2694-0604
DOI:	10.1109/EMBC48229.2022.9871401