Semi-Automatic Classification of Lesion Patterns in Patients with Clinically Isolated Syndrome

Alessandro Crimi 1 Olivier Commowick 1 Jean-Christophe Ferré 2 Adil Maarouf 3, 4 Gilles Edan 2 Christian Barillot 1, *
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 Service de radiologie et imagerie médicale [Rennes]
3 CRMBM - Centre de résonance magnétique biologique et médicale
4 Service de neurologie [Reims]
Abstract : Multiple sclerosis (MS) is neuro-degenerative disease of the Central Nervous System characterized by the loss of myelin. A Clinically Isolated Syndrome (CIS) is a first neurological episode caused by inflammation/demyelination in the central nervous system which may lead to MS. Better understanding of the disease at its onset will lead to a better discovery of pathogenic mechanisms, allowing suitable therapies at an early stage. We propose an automatic segmentation algorithm for two different contrast agents, used within a framework for early characterization of CIS patients according to lesion patterns, and more specifically according to the nature of the inflammatory patterns of these lesions. We expect that the proposed framework can infer new prospective figures from the earliest imaging signs of MS since it can provide a classification of different types of lesions across patients. The lesion detection algorithm based on intensity normalization and subtraction of the used MRI data is a pivotal step, since it avoids the time-demanding task of manual delineation.
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Submitted on : Tuesday, July 30, 2013 - 9:02:07 PM
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Alessandro Crimi, Olivier Commowick, Jean-Christophe Ferré, Adil Maarouf, Gilles Edan, et al.. Semi-Automatic Classification of Lesion Patterns in Patients with Clinically Isolated Syndrome. International Symposium on Biomedical Imaging: From Nano to Macro, Apr 2013, San Francisco, United States. pp.1102-1105, ⟨10.1109/ISBI.2013.6556671⟩. ⟨inserm-00800723⟩

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