Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus.
OBJECTIVE
To prospectively evaluate the accuracy of automated hippocampal volumetry to help distinguish between patients with Alzheimer disease (AD), patients with mild cognitive impairment (MCI), and elderly controls, by using established criteria for patients with AD and MCI as the reference standard.


METHODS
The regional ethics committee approved the study and written informed consent was obtained from all participants. The study included 25 patients with AD (11 men, 14 women; mean age +/- standard deviation [SD], 73 years +/- 6; Mini-Mental State Examination (MMSE) score, 24.4 +/- 2.7), 24 patients with amnestic MCI (10 men, 14 women; mean age +/- SD, 74 years +/- 8; MMSE score, 27.2 +/- 1.4) and 25 elderly healthy controls (13 men, 12 women; mean age +/- SD, 64 years +/- 8). For each participant, the hippocampi were automatically segmented on three-dimensional T1-weighted magnetic resonance (MR) images with high spatial resolution. Segmentation was performed by using recently developed software that allows fast segmentation with minimal user input. Group differences in hippocampal volume were assessed by using Student t tests. To obtain robust estimates of P values, the correct classification rate, sensitivity, and specificity, bootstrap methods were used.


RESULTS
Significant hippocampal volume reductions were detected in all groups of patients (-32% in AD patients vs controls, P < .001; -19% in MCI patients vs controls, P < .001; and -15% in AD patients vs MCI patients, P < .01). Individual classification on the basis of hippocampal volume resulted in 84% correct classification (sensitivity, 84%; specificity, 84%) between AD patients and controls and 73% correct classification (sensitivity, 75%; specificity, 70%) between MCI patients and controls.


CONCLUSIONS
This automated method can serve as an alternative to manual tracing and may thus prove useful in assisting with the diagnosis of AD.
Colliot
Olivier
O
Cognitive Neuroscience and Brain Imaging Laboratory, Centre National de la Recherche Scientifique, UPR640-LENA, Université Pierre et Marie Curie-Paris 6, Hôpital de la Pitié-Salpêtrière, Paris, France. olivier.colliot@chups.jussieu.fr
Chételat
Gaël
G
Chupin
Marie
M
Desgranges
Béatrice
B
Magnin
Benoît
B
Benali
Habib
H
Dubois
Bruno
B
Garnero
Line
L
Eustache
Francis
F
Lehéricy
Stéphane
S
D000368:Aged; D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis*; D001185:Artificial Intelligence; D003072:Cognition Disorders / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity