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A Hierarchical Algorithm for MR Brain Image Parcellation

Institution:	Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:	IEEE Transactions on Medical Imaging.
Publication Date:	Sep-2007
Volume Number:	26
Issue Number:	9
Pages:	1201-1212
Citation:	IEEE Transactions on Medical Imaging. 2007 Sept;26(9):1201-1212.
PubMed ID:	17896593
PMCID:	PMC2768067
Keywords:	Automatic Segmentation, Expectation-Maximization, Parcellation, Data Tree, Statistical Group Comparison Study, MRI, Projects:ShapeBasedSegmentationAndRegistration
Appears in Collections:	SPL, NA-MIC, NAC, PNL, SLICER
Sponsors:	NIH K05 MH 01110 NIH K02 MH 01110 NIH R01 MH 50747 NIH R01 MH 40799 NIH Roadmap for Medical Research Grant U54 EB005149 NCRR NAC P41 RR13218 NCRR mBIRN U24-RR021382 NINDS R01 NS051826 NIAAA R01 AA016748-01 MIND foundation Brain Science Foundation Department of Veterans Affairs Merit Awards Research Enhancement Award Program Middleton Award from the Department of Veterans Affairs National Institute on Alcohol Abuse and Alcoholism Grants No. AA05965 and AA13521.
Generated Citation:	Pohl K.M., Bouix S., Nakamura M., Rohlfing T., McCarley R.W., Kikinis R., Grimson W..E.L., Shenton M.E., Wells III W.M. A Hierarchical Algorithm for MR Brain Image Parcellation. IEEE Transactions on Medical Imaging. 2007 Sept;26(9):1201-1212. PMID: 17896593. PMCID: PMC2768067.
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We 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 firstepisode 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.

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