title: Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis.
abstract: We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The method performs tissue classification using a model of intensities of the normal appearing brain tissues. In order to estimate the model, a trimmed likelihood estimator is initialized with a hierarchical random approach in order to be robust to MS lesions and other outliers present in real images. The algorithm is first evaluated with simulated images to assess the importance of the robust estimator in presence of outliers. The method is then validated using clinical data in which MS lesions were delineated manually by several experts. Our method obtains an average Dice similarity coefficient (DSC) of 0.65, which is close to the average DSC obtained by raters (0.66).
authors.affiliation: University of Rennes I-CNRS UMR 6074, IRISA, Campus de Beaulieu, Rennes, France.
authors.forename: Daniel
authors.initials: D
authors.lastname: García-Lorenzo
authors.forename: Sylvain
authors.initials: S
authors.lastname: Prima
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Arnold
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Christian
authors.initials: C
authors.lastname: Barillot
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D016011:Normal Distribution; D015203:Reproducibility of Results