MAGeT brain
Given a set of labelled MR images (atlases) and unlabelled 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.
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The major difference is that, in MAGeT brain, segmentations from each atlas (typically manually delineated) are propogated via image registration to a subset of the subject images (known as the ‘template library’) before being propogated to each subject image and fused. It is our hypothesis that by propogating labels to a template library, we are able to make use of the neuroanatomical variability of the subjects in order to ‘fine tune’ each individual subject’s segmentation.
Algorithm:
M Mallar Chakravarty, Patrick Steadman, Matthijs C van Eede, Rebecca D Calcott, Victoria Gu, Philip Shaw, Armin Raznahan, D Louis Collins, and Jason P Lerch. Performing label-fusion-based segmentation using multiple automatically generated templates. Hum Brain Mapp, 34(10):2635–54, October 2013. (doi:10.1002/hbm.22092)
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Hippocampus and subfields:
Jon Pipitone, Min Tae M Park, Julie Winterburn, Tristram A Lett, Jason P Lerch, Jens C Pruessner, Martin Lepage, Aristotle N Voineskos, and M Mallar Chakravarty. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage, 101:494–512, November 2014. (doi:10.1016/j.neuroimage.2014.04.054)
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Cerebellum:
Min Tae M Park, Jon Pipitone, Lawrence H Baer, Julie L Winterburn, Yashvi Shah, Sofia Chavez, Mark M Schira, Nancy J Lobaugh, Jason P Lerch, Aristotle N Voineskos, and M Mallar Chakravarty. Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates. Neuroimage, 95:217–31, July 2014. (doi:10.1016/j.neuroimage.2014.03.037)