Towards Accurate, Automatic Segmentation of the Hippocampus and Amygdala from MRI

Authors:
D. Louis Collins;Jens C. Pruessner
Affiliations:
McConnell Brain Imaging Center, Montreal Neurological Institute, and Department Biomedical Engineering,;McConnell Brain Imaging Center, Montreal Neurological Institute, and Douglas Hospital Research Center, Department of Psychology, McGill University, Montreal, Canada
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

We describe progress towards fully automatic segmentation of the hippocampus (HC) and amygdala (AG) in human subjects from MRI data. Three methods are described and tested with a set of MRIs from 80 young normal controls, using manual labeling of the HC and AG as a gold standard. The methods include: 1) our ANIMAL atlas-based method that uses non-linear registration to a pre-labeled non-linear average template (ICBM152). HC and AG labels, defined on the template are mapped through the inverse transformation to segment these structures on the subject's MRI; 2) template-based segmentation, where we select the most similar MRI from the set of 80 labeled datasets to use as a template in the standard ANIMAL segmentation scheme; 3) label fusion methods where we combine segmentations from the `n' most similar templates. The label fusion technique yields the best results with median kappas of 0.886 and 0.826 for HC and AG, respectively.