title: Automatic brain tumor segmentation by subject specific modification of atlas priors.
abstract: OBJECTIVE
  Manual segmentation of brain tumors from magnetic resonance images is a challenging and time-consuming task. An automated system has been developed for brain tumor segmentation that will provide objective, reproducible segmentations that are close to the manual results. Additionally, the method segments white matter, grey matter, cerebrospinal fluid, and edema. The segmentation of pathology and healthy structures is crucial for surgical planning and intervention.
  
  
  METHODS
  The method performs the segmentation of a registered set of magnetic resonance images using an expectation-maximization scheme. The segmentation is guided by a spatial probabilistic atlas that contains expert prior knowledge about brain structures. This atlas is modified with the subject-specific brain tumor prior that is computed based on contrast enhancement.
  
  
  RESULTS
  Five cases with different types of tumors are selected for evaluation. The results obtained from the automatic segmentation program are compared with results from manual and semi-automated methods. The automated method yields results that have surface distances at roughly 1-4 mm compared with the manual results.
  
  
  CONCLUSIONS
  The automated method can be applied to different types of tumors. Although its performance is below that of the semi-automated method, it has the advantage of requiring no user supervision.
authors.affiliation: Department of Computer Science, CB #3175, Sitterson Hall, Chapel Hill, NC 27599, USA.
authors.forename: Marcel
authors.initials: M
authors.lastname: Prastawa
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Bullitt
authors.forename: Nathan
authors.initials: N
authors.lastname: Moon
authors.forename: Koen
authors.initials: K
authors.lastname: Van Leemput
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
mesh_terms: D001331:Automation; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D012680:Sensitivity and Specificity