MAGeT Brain: Automated Brain Segmentation Using Custom Atlases
Given a set of labeled MR images (atlases) and unlabeled images (subjects), MAGeT produces a segmentation for each subject using a multi-atlas voting procedure based on a template library made up of images from the subject set.
The major difference is that, in MAGeT brain, segmentations from each atlas (typically manually delineated) are propagated via image registration to a subset of the subject images (known as the ‘template library’) before being propagated to each subject image and fused. It is our hypothesis that by propagating labels to a template library, we are able to make use of the neuro-anatomical variability of the subjects in order to ‘fine tune’ each individual subject’s segmentation.
To cite MAGeT brain in publications, please use:
Pipitone J, Park MT, Winterburn J, et al. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage. 2014.