@inbook{7b5149401d824f0ebc79b77e00541b55,
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.",
author = "Heckemann, {Rolf A.} and Hajnal, {Joseph V.} and Paul Aljabar and Daniel Rueckert and Alexander Hammers",
year = "2006",
doi = "10.1007/11866763_100",
language = "English",
volume = "4191",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "815--822",
booktitle = "Medical Image Computing and Computer-Assisted Intervention",
note = "9th International Conference on Computing and Computer-Assisted Intervention ; Conference date: 01-10-2006 Through 06-10-2006",
}