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Robust Segmentation of Focal Lesions on Multi-Sequence MRI in Multiple Sclerosis

Daniel García-Lorenzo 1
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 : Multiple sclerosis (MS) aects around 80.000 people in France. Magnetic resonance imaging (MRI) is an essential tool for diagnosis of MS and MRI-derived surrogate markers such as MS lesion volumes are often used as measures in MS clinical trials for the development of new treatments. The manual segmentation of these MS lesions is a time-consuming task that shows high inter- and intra-rater variability. We developed an automatic workow for the segmentation of focal MS lesions on MRI. The segmentation method is based on the robust estimation of a parametric model of the intensities of the brain; lesions are detected as outliers to the model. We proposed two methods to include spatial information in the segmentation using mean shift and graph cut. We performed a quantitative evaluation of our workow using synthetic and clinical images of two different centers to verify its accuracy and robustness
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Submitted on : Friday, May 21, 2010 - 12:17:49 PM
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  • HAL Id : tel-00485645, version 1


Daniel García-Lorenzo. Robust Segmentation of Focal Lesions on Multi-Sequence MRI in Multiple Sclerosis. Informatique [cs]. Université Rennes 1, 2010. Français. ⟨tel-00485645⟩



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