title: A toolbox for multiple sclerosis lesion segmentation.
abstract: BACKGROUND
  Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images.
  
  
  METHODS
  Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image.
  
  
  RESULTS
  The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches.
  
  
  CONCLUSIONS
  Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.
authors.affiliation: Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, 17071, Girona, Spain. eloyroura@eia.udg.edu.
authors.forename: Eloy
authors.initials: E
authors.lastname: Roura
authors.affiliation: Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, 17071, Girona, Spain.
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.affiliation: Magnetic Resonance Unit, Dept. of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain.
authors.forename: Mariano
authors.initials: M
authors.lastname: Cabezas
authors.affiliation: Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, 17071, Girona, Spain.
authors.forename: Sergi
authors.initials: S
authors.lastname: Valverde
authors.affiliation: Magnetic Resonance Unit, Dept. of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain.
authors.forename: Deborah
authors.initials: D
authors.lastname: Pareto
authors.affiliation: Girona Magnetic Resonance Center, Girona, Spain.
authors.forename: Joan C
authors.initials: JC
authors.lastname: Vilanova
authors.affiliation: Multiple Sclerosis and Neuroimmunology Unit, Dr. Josep Trueta University Hospital, Institut d'Investigació Biomèdica de Girona, Girona, Spain.
authors.forename: Lluís
authors.initials: L
authors.lastname: Ramió-Torrentà
authors.affiliation: Magnetic Resonance Unit, Dept. of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain.
authors.forename: Àlex
authors.initials: À
authors.lastname: Rovira
authors.affiliation: Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, 17071, Girona, Spain.
authors.forename: Xavier
authors.initials: X
authors.lastname: Lladó
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation
keywords: Automated tool; Lesion detection; Lesion segmentation; Magnetic resonance images; Multiple sclerosis