title: Learning likelihoods for labeling (L3): a general multi-classifier segmentation algorithm.
abstract: OBJECTIVE
  To develop an MRI segmentation method for brain tissues, regions, and substructures that yields improved classification accuracy. Current brain segmentation strategies include two complementary strategies. Multi-spectral classification techniques generate excellent segmentations for tissues with clear intensity contrast, but fail to identify structures defined largely by location, such as lobar parcellations and certain subcortical structures. Conversely, multi-template label fusion methods are excellent for structures defined largely by location, but perform poorly when segmenting structures that cannot be accurately identified through a consensus of registered templates.
  
  
  METHODS
  We propose here a novel multi-classifier fusion algorithm with the advantages of both types of segmentation strategy. We illustrate and validate this algorithm using a group of 14 expertly hand-labeled images.
  
  
  RESULTS
  Our method generated segmentations of cortical and subcortical structures that were more similar to hand-drawn segmentations than majority vote label fusion or a recently published intensity/label fusion method.
  
  
  CONCLUSIONS
  We have presented a novel, general segmentation algorithm with the advantages of both statistical classifiers and label fusion techniques.
authors: Computational Radiology Laboratory, Children's Hospital Boston, Harvard Medical School, Boston, MA, USA.
authors: Neil I
authors: NI
authors: Weisenfeld
authors: Simon K
authors: SK
authors: Warfield
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007858:Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012984:Software