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Comparing new templates and atlas‐based segmentations in the volumetric analysis of brain magnetic resonance images for diagnosing Alzheimer's disease
Author(s) -
Shen Qian,
Zhao Weizhao,
Loewenstein David A.,
Potter Elizabeth,
Greig Maria T.,
Raj Ashok,
Barker Warren,
Potter Huntington,
Duara Ranjan
Publication year - 2012
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2011.07.002
Subject(s) - template , segmentation , cognitive impairment , brain atlas , atlas (anatomy) , pattern recognition (psychology) , set (abstract data type) , artificial intelligence , magnetic resonance imaging , computer science , cognition , psychology , neuroscience , medicine , radiology , anatomy , programming language
Background The segmentation of brain structures on magnetic resonance imaging scans for calculating regional brain volumes, using automated anatomic labeling, requires the use of both brain atlases and templates (template sets). This study aims to improve the accuracy of volumetric analysis of hippocampus (HP) and amygdala (AMG) in the assessment of early Alzheimer's disease (AD) by developing template sets that correspond more closely to the brains of elderly individuals. Methods Total intracranial volume and HP and AMG volumes were calculated for elderly subjects with no cognitive impairment (n = 103), with amnestic mild cognitive impairment (n = 68), or with probable AD (n = 46) using the following: (1) a template set consisting of a standard atlas (atlas S), drawn on a young adult male brain, and the widely used Montreal Neurological Institute template (MNI template set); (2) a template set (template S set) in which the template is based on smoothing the image from which atlas S is derived; and (3) a new template set (template E set) in which the template is based on an atlas (atlas E) created from the brain of an elderly individual. Results Correspondence to HP and AMG volumes derived from manual segmentation was highest with automated segmentation by template E set, intermediate with template S set, and lowest with the MNI template set. The areas under the receiver operating curve for distinguishing elderly subjects with no cognitive impairment from elderly subjects with amnestic mild cognitive impairment or probable AD and the correlations between HP and AMG volumes and cognitive and functional scores were highest for template E set, intermediate for template S set, and lowest for the MNI template set. Conclusions The accuracy of automated anatomic labeling and the diagnostic value of the derived volumes are improved with template sets based on brain atlases closely resembling the anatomy of the to‐be‐segmented brain magnetic resonance imaging scans.