The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe (MTL) regions from T1-weighted magnetic resonance (MR) images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to (a) distinguish between patients with Alzheimer disease (AD), patients with amnestic mild cognitive impairment (aMCI), and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and (b) infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD (17 men, 44 women; mean deviation (SD), ; Mini Mental State Examination (MMSE) score, ), 42 patients with aMCI (11 men, 31 women; mean , ; MMSE score, ), and 30 elderly healthy controls (10 men, 20 women; mean , ; MMSE score, ). For the evaluation of the statistical indicator, 150 patients with mild AD (62 men, 88 women; mean , ; MMSE score, ), 247 patients with aMCI (143 men, 104 women; mean , ; MMSE score, ), and 135 elderly healthy controls (61 men, 74 women; mean , ). Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences wereassessed by using two-sample test. Individual classification was analyzed by using receiver operating characteristic (ROC) curves. Compared to controls, significant differences in the intensity-based MTL atrophy measure were detected in both groups of patients (AD vs controls, vs , ; aMCI vs controls, vs , ). Moreover, the subgroup of aMCI converters was significantly different from controls ( vs , ). Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.
The authors thank Mr. E. Deseri for image acquisition and Dr. C. E. Neumaier for MR image reporting. Data collection and sharing for this project was funded by the ADNI (Principal Investigator: Michael Weiner; NIH Grant No. U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and through generous contributions from the following: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Co., GlaxoSmithKline, Merck & Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, Alzheimer Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging, with participation from the US Food and Drug Administration. Industry partnerships are coordinated through the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles. This work benefited from the use of the Insight Segmentation and Registration Toolkit (ITK), an open source software developed as an initiative of the US National Library of Medicine and available at http://www.itk.org.60
II. DEVELOPMENT OF THE SOFTWARE
II.B. Image acquisition
II.C. Extraction of the hippocampal boxes
II.D. Selection of templates
III. APPLICATION OF THE SOFTWARE
III.B. The -box score
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