title: Probabilistic multiple sclerosis lesion classification based on modeling regional intensity variability and local neighborhood information.
abstract: OBJECTIVE
  In this paper, a fully automatic probabilistic method for multiple sclerosis (MS) lesion classification is presented, whereby the posterior probability density function over healthy tissues and two types of lesions (T1-hypointense and T2-hyperintense) is generated at every voxel.
  
  
  METHODS
  During training, the system explicitly models the spatial variability of the intensity distributions throughout the brain by first segmenting it into distinct anatomical regions and then building regional likelihood distributions for each tissue class based on multimodal magnetic resonance image (MRI) intensities. Local class smoothness is ensured by incorporating neighboring voxel information in the prior probability through Markov random fields. The system is tested on two datasets from real multisite clinical trials consisting of multimodal MRIs from a total of 100 patients with MS. Lesion classification results based on the framework are compared with and without the regional information, as well as with other state-of-the-art methods against the labels from expert manual raters. The metrics for comparison include Dice overlap, sensitivity, and positive predictive rates for both voxel and lesion classifications.
  
  
  RESULTS
  Statistically significant improvements in Dice values ( ), for voxel-based and lesion-based sensitivity values ( ), and positive predictive rates ( and respectively) are shown when the proposed method is compared to the method without regional information, and to a widely used method [1]. This holds particularly true in the posterior fossa, an area where classification is very challenging.
  
  
  CONCLUSIONS
  The proposed method allows us to provide clinicians with accurate tissue labels for T1-hypointense and T2-hyperintense lesions, two types of lesions that differ in appearance and clinical ramifications, and with a confidence level in the classification, which helps clinicians assess the classification results.
authors.forename: Rola
authors.initials: R
authors.lastname: Harmouche
authors.forename: Nagesh K
authors.initials: NK
authors.lastname: Subbanna
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Arnold
authors.forename: Tal
authors.initials: T
authors.lastname: Arbel
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000145:classification* / Q000473:pathology*