Journal
NEUROIMAGE
Volume 43, Issue 1, Pages 59-68Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2008.07.003
Keywords
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Funding
- NIH [U01 AG024904]
- National Institute of Aging
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
- Foundation for the National Institutes of Health
- Pfizer Inc.
- Wyeth Research
- Bristol-Myers Squibb
- Eli Lilly and Company
- GlaxoSmithKline
- Merck Co. Inc.
- AstraZeneca AB
- Novartis Pharmaceuticals Corporation
- Alzheimer's Association
- Eisai Global Clinical Development
- Elan Corporation plc
- Forest Laboratories
- Institute for the Study of Aging (ISOA)
- U.S. Food and Drug Administration
- NIA
- NIBIB
- National Library of Medicine
- National Center for Research Resources [AG016570, EB01651, LM05639, RR019771]
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We introduce a new method for brain MRI segmentation, called the auto context model (ACM), to segment the hippocampus automatically in 3D T1-weighted structural brain MRI Scans Of Subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In a training phase, our algorithm used 21 hand-labeled segmentations to learn a classification rule for hippocampal versus non-hippocampal regions using a modified AdaBoost method, based on similar to 18,000 features (image intensity, position, image curvatures, image gradients, tissue classification maps of gray/white matter and CSF, and mean, standard deviation, and Haar filters of size 1 x 1 x 1 to 7 x 7 x 7). We linearly registered all brains to a standard template to devise a basic shape prior to capture the global shape of the hippocampus, defined as the pointwise summation of all the training masks. We also included curvature, gradient, mean, standard deviation, and Haar filters of the shape prior and the tissue classified images as features. During each iteration of ACM - our extension of AdaBoost the Bayesian posterior distribution of the labeling was fed back in as an input, along with its neighborhood features as new features for AdaBoost to use. In validation studies, we compared our results with hand-labeled segmentations by two experts. Using a leave-one-out approach and standard overlap and distance error metrics, our automated segmentations agreed well with human raters; any differences were comparable to differences between trained human raters. Our error metrics compare favorably with those previously reported for other automated hippocampal segmentations, suggesting the utility of the approach for large-scale studies. Published by Elsevier Inc.
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