A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation

Florence Forbes 1, * Senan Doyle 1 Daniel Garcia-Lorenzo 2 Christian Barillot 2 Michel Dojat 3
* Corresponding author
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 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 : We propose a technique for fusing the output of multiple Magnetic Resonance (MR) sequences to robustly and accurately segment brain lesions. It is based on an augmented multi-sequence hidden Markov model that includes additional weight variables to account for the relative importance and control the impact of each sequence. The augmented framework has the advantage of allowing 1) the incorporation of expert knowledge on the a priori relevant information content of each sequence and 2) a weighting scheme which is modified adaptively according to the data and the segmentation task under consideration. The model, applied to the detection of multiple sclerosis and stroke lesions shows promising results.
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Florence Forbes, Senan Doyle, Daniel Garcia-Lorenzo, Christian Barillot, Michel Dojat. A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation. Thirteenth International Conference on Artificial Intelligence and Statistics, May 2010, Sardinia, Italy. pp.225-232. ⟨inserm-00723808⟩

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