title: BOOST: a supervised approach for multiple sclerosis lesion segmentation.
abstract: BACKGROUND
  Automatic multiple sclerosis lesion segmentation is a challenging task. An extensive analysis of the most recent techniques indicates an improvement of the results obtained when using prior knowledge and contextual information.
  
  
  METHODS
  We present BOOST, a knowledge-based approach to automatically segment multiple sclerosis lesions through a voxel by voxel classification. We used the Gentleboost classifier and a set of features, including contextual features, registered atlas probability maps and an outlier map.
  
  
  RESULTS
  Results are computed on a set of 45 cases from three different hospitals (15 of each), obtaining a moderate agreement between the manual annotations and the automatically segmented results.
  
  
  CONCLUSIONS
  We quantitatively compared our results with three public state-of-the-art approaches obtaining competitive results and a better overlap with manual annotations. Our approach tends to better segment those cases with high lesion load, while cases with small lesion load are more difficult to accurately segment.
  
  
  CONCLUSIONS
  We believe BOOST has potential applicability in the clinical practice, although it should be improved in those cases with small lesion load.
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain; Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Spain. Electronic address: mcabezas@eia.udg.edu.
authors.forename: Mariano
authors.initials: M
authors.lastname: Cabezas
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Sergi
authors.initials: S
authors.lastname: Valverde
authors.affiliation: Multiple Sclerosis and Neuroimmunology Unit, Dr. Josep Trueta University Hospital, Spain.
authors.forename: Brigitte
authors.initials: B
authors.lastname: Beltran
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Jordi
authors.initials: J
authors.lastname: Freixenet
authors.affiliation: Girona Magnetic Resonance Center, Spain.
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.affiliation: Multiple Sclerosis and Neuroimmunology Unit, Dr. Josep Trueta University Hospital, Spain.
authors.forename: Lluís
authors.initials: L
authors.lastname: Ramió-Torrentà
authors.affiliation: Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Spain.
authors.forename: Alex
authors.initials: A
authors.lastname: Rovira
authors.affiliation: Department of Computer Architecture and Technology, University of Girona, Spain.
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D005260:Female; D006801:Humans; D019359:Knowledge; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated; D011336:Probability
keywords: Artificial intelligence; Brain analysis; Image analysis; Magnetic resonance imaging; Multiple sclerosis