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.
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https://www.hal.inserm.fr/inserm-01127690
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Yogesh Karpate, Olivier Commowick, Christian Barillot. Probabilistic One Class Learning for Automatic Detection of Multiple Sclerosis Lesions. IEEE International Symposium on Biomedical Imaging (ISBI), Apr 2015, Brooklyn, United States. pp.486-489. ⟨inserm-01127690⟩

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