title: Multiclassifier fusion in human brain MR segmentation: modelling convergence.
abstract: Segmentations of MR images of the human brain can be generated by propagating an existing atlas label volume to the target image. By fusing multiple propagated label volumes, the segmentation can be improved. We developed a model that predicts the improvement of labelling accuracy and precision based on the number of segmentations used as input. Using a cross-validation study on brain image data as well as numerical simulations, we verified the model. Fit parameters of this model are potential indicators of the quality of a given label propagation method or the consistency of the input segmentations used.
authors.affiliation: Imaging Sciences Department, MRC Clinical Sciences Centre, Imperial College at Hammersmith Hospital Campus, London, UK.
authors.forename: Rolf A
authors.initials: RA
authors.lastname: Heckemann
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
authors.forename: Paul
authors.initials: P
authors.lastname: Aljabar
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
authors.forename: Alexander
authors.initials: A
authors.lastname: Hammers
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique