The impact of processing workflow in performance of automatic white matter lesion segmentation in Multiple Sclerosis

Daniel García-Lorenzo 1, * Sylvain Prima 1 Laura Parkes 2 Jean-Christophe Ferré 3 Sean Patrick Morrissey 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
2 MARIARC - Magnetic Resonance and Image Analysis Research Centre
3 Service de radiologie et imagerie médicale [Rennes]
Abstract : The design of a robust automatic segmentation workflow is crucial to deal with the shortcomings of images that impacts on their analysis. In this paper, different workflows, using state-of-the-art tools, are compared in order to evaluate the role of the different preprocessing tasks.We propose some methods in order to improve the computing time, robustness and accuracy of the segmentation method. We compare with manual segmentation as ground truth the workflows and improvements in the segmentation method. Finally, we present a new automatic workflow for white matter lesions segmentation in Multiple Sclerosis.
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Daniel García-Lorenzo, Sylvain Prima, Laura Parkes, Jean-Christophe Ferré, Sean Patrick Morrissey, et al.. The impact of processing workflow in performance of automatic white matter lesion segmentation in Multiple Sclerosis. MICCAI workshop on Medical Image Analysis on Multiple Sclerosis (validation and methodological issues) (MIAMS'2008), Sep 2008, New York, United States. pp.104-112. ⟨inserm-00421702⟩

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