Multiclassifier fusion in human brain MR segmentation: modelling convergence

Authors:
Rolf A. Heckemann;Joseph V. Hajnal;Paul Aljabar;Daniel Rueckert;Alexander Hammers
Affiliations:
Imaging Sciences Department, MRC Clinical Sciences Centre, Imperial College at Hammersmith Hospital Campus, London, UK;Imaging Sciences Department, MRC Clinical Sciences Centre, Imperial College at Hammersmith Hospital Campus, London, UK;Department of Computing, Imperial College London, UK;Department of Computing, Imperial College London, UK;Division of Neuroscience and Mental Health, MRC Clinical Sciences Centre, Imperial College at Hammersmith Hospital Campus, London, UK
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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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.