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dc.contributor.authorPohl, Kilian M.
dc.contributor.authorBouix, Sylvain
dc.contributor.authorNakamura, Motoaki
dc.contributor.authorRohlfing, Torsten
dc.contributor.authorMcCarley, Robert William
dc.contributor.authorKikinis, Ron
dc.contributor.authorGrimson, W. Eric L.
dc.contributor.authorShenton, Martha Elizabeth
dc.contributor.authorWells, William Mercer
dc.date.accessioned2016-09-26T15:25:33Z
dc.date.issued2007
dc.identifier.citationPohl, Kilian M., Sylvain Bouix, Motoaki Nakamura, Torsten Rohlfing, Robert W. McCarley, Ron Kikinis, W. Eric L. Grimson, Martha E. Shenton, and William M. Wells. 2007. “A Hierarchical Algorithm for MR Brain Image Parcellation.” IEEE Transactions on Medical Imaging 26 (9) (September): 1201–1212. doi:10.1109/tmi.2007.901433.en_US
dc.identifier.issn0278-0062en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:28552562
dc.description.abstractWe introduce an algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter. The algorithm is guided by prior information represented within a tree structure. The tree mirrors the hierarchy of anatomical structures and the sub-trees correspond to limited segmentation problems. The solution to each problem is estimated via a conventional classifier. Our algorithm can be adapted to a wide range of segmentation problems by modifying the tree structure or replacing the classifier. We evaluate the performance of our new segmentation approach by revisiting a previously published statistical group comparison between first-episode schizophrenia patients, first-episode affective psychosis patients, and comparison subjects. The original study is based on 50 MR volumes in which an expert identified the brain tissue classes as well as the superior temporal gyrus, amygdala, and hippocampus. We generate analogous segmentations using our new method and repeat the statistical group comparison. The results of our analysis are similar to the original findings, except for one structure (the left superior temporal gyrus) in which a trend-level statistical significance (p=0.07) was observed instead of statistical significance.en_US
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofdoi:10.1109/TMI.2007.901433en_US
dc.relation.hasversionhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2768067/en_US
dash.licenseLAA
dc.subjectautomatic segmentationen_US
dc.subjectdata treeen_US
dc.subjectexpectation-maximizationen_US
dc.subjectparcellationen_US
dc.subjectstatistical group comparison studyen_US
dc.subjectMRIen_US
dc.titleA Hierarchical Algorithm for MR Brain Image Parcellationen_US
dc.typeJournal Articleen_US
dc.description.versionAccepted Manuscripten_US
dc.relation.journalIEEE Transactions on Medical Imagingen_US
dash.depositing.authorShenton, Martha Elizabeth
dc.date.available2016-09-26T15:25:33Z
dc.identifier.doi10.1109/TMI.2007.901433*
dash.identifier.orcid0000-0003-4235-7879en_US
dash.contributor.affiliatedWells, William
dash.contributor.affiliatedMcCarley, Robert William
dash.contributor.affiliatedKikinis, Ron
dash.contributor.affiliatedBouix, Sylvain
dash.contributor.affiliatedShenton, Martha
dc.identifier.orcid0000-0001-5705-7495
dc.identifier.orcid0000-0001-7227-7058


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