Probabilistic One Class Learning for Automatic Detection of Multiple Sclerosis Lesions

Yogesh Karpate; Olivier Commowick; Christian Barillot

Probabilistic One Class Learning for Automatic Detection of Multiple Sclerosis Lesions

Yogesh Karpate ¹ Olivier Commowick ¹ 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

Abstract : This paper presents an automatic algorithm for the detec- tion of multiple sclerosis lesions (MSL) from multi-sequence magnetic resonance imaging (MRI). We build a probabilistic classifier that can recognize MSL as a novel class, trained only on Normal Appearing Brain Tissues (NABT). Patch based intensity information of MRI images is used to train a classifier at the voxel level. The classifier is in turn used to compute a probability characterizing the likelihood of each voxel to be a lesion. This probability is then used to identify a lesion voxel based on simple Otsu thresholding. The pro- posed framework is evaluated on 16 patients and our analysis reveals that our approach is well suited for MSL detection and outperforms other benchmark approaches.

Document type :

Conference papers

Domain :

Life Sciences [q-bio] / Neurons and Cognition [q-bio.NC]

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https://www.hal.inserm.fr/inserm-01127690
Contributor : Olivier Commowick <>
Submitted on : Wednesday, May 6, 2015 - 2:58:27 PM
Last modification on : Monday, August 5, 2019 - 5:56:01 PM
Long-term archiving on : Wednesday, April 19, 2017 - 6:17:09 PM

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