Back to articles
Volume: 32 | Article ID: art00002
Learning a CNN on multiple sclerosis lesion segmentation with self-supervision
  DOI :  10.2352/ISSN.2470-1173.2020.17.3DMP-003  Published OnlineJanuary 2020

Multiple Sclerosis (MS) is a chronic, often disabling, autoimmune disease affecting the central nervous system and characterized by demyelination and neuropathic alterations. Magnetic Resonance (MR) images plays a pivotal role in the diagnosis and the screening of MS. MR images identify and localize demyelinating lesions (or plaques) and possible associated atrophic lesions whose MR aspect is in relation with the evolution of the disease. We propose a novel MS lesions segmentation method for MR images, based on Convolutional Neural Networks (CNNs) and partial self-supervision and studied the pros and cons of using self-supervision for the current segmentation task. Investigating the transferability by freezing the firsts convolutional layers, we discovered that improvements are obtained when the CNN is retrained from the first layers. We believe such results suggest that MRI segmentation is a singular task needing high level analysis from the very first stages of the vision process, as opposed to vision tasks aimed at day-to-day life such as face recognition or traffic sign classification. The evaluation of segmentation quality has been performed on full image size binary maps assembled from predictions on image patches from an unseen database.

Subject Areas :
Views 80
Downloads 22
 articleview.views 80
 articleview.downloads 22
  Cite this article 

Alexandre Fenneteau, Pascal Bourdon, David Helbert, Christine Fernandez-Maloigne, Christophe Habas, Remy Guillevin, "Learning a CNN on multiple sclerosis lesion segmentation with self-supervisionin Proc. IS&T Int’l. Symp. on Electronic Imaging: 3D Measurement and Data Processing,  2020,  pp 3-1 - 3-7,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
Electronic Imaging
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA