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Colliot O, Chételat G, Chupin M, Desgranges B, Magnin B, Benali H,
Dubois B, Garnero L, Eustache F, Lehéricy S.

Discrimination between Alzheimer disease, mild cognitive impairment,
and normal aging by using automated segmentation of the hippocampus.

Radiology. 2008 Jul;248(1):194-201. Epub 2008 May 5.PMID: 18458242
[PubMed - indexed for MEDLINE]

PURPOSE: 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. MATERIALS AND 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. CONCLUSION: This automated method
can serve as an alternative to manual tracing and may thus prove
useful in assisting with the diagnosis of AD. (c) RSNA, 2008.