期刊
NEUROIMAGE
卷 55, 期 3, 页码 1091-1108出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2010.12.067
关键词
Automated brain extraction; Skull-stripping; Segmentation; MAPS; BET; BSE; HWA
资金
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- NIH [P30 AG010129, K01 AG030514]
- Dana Foundation
- Department of Health's NIHR Biomedical Research Centres
- Alzheimer's Research Trust
- Technology Strategy Board [TP1638A]
- Medical Research Council (UK)
- MRC [G0601846, G0401247] Funding Source: UKRI
- Alzheimers Research UK [ART-EG2010B-1] Funding Source: researchfish
- Medical Research Council [G0601846, G0401247] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0508-10123] Funding Source: researchfish
Whole brain extraction is an important pre-processing step in neuroimage analysis. Manual or semi-automated brain delineations are labour-intensive and thus not desirable in large studies, meaning that automated techniques are preferable. The accuracy and robustness of automated methods are crucial because human expertise may be required to correct any suboptimal results, which can be very time consuming. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to extract whole brains from 682 1.5 T and 157 3 T T-1-weighted MR baseline images from the Alzheimer's Disease Neuroimaging Initiative database. Semi-automated brain segmentations with manual editing and checking were used as the gold-standard to compare with the results. The median Jaccard index of MAPS was higher than HWA, BET and BSE in 1.5 land 3 T scans (p < 0.05, all tests), and the 1st to 99th centile range of the Jaccard index of MAPS was smaller than HWA, BET and BSE in 1.5 T and 3 T scans ( p < 0.05, all tests). HWA and MAPS were found to be best at including all brain tissues (median false negative rate <= 0.010% for 1.5 T scans and <= 0.019% for 3 T scans, both methods). The median Jaccard index of MAPS were similar in both 1.5 T and 3 T scans, whereas those of BET, BSE and HWA were higher in 1.5 T scans than 3 T scans (p < 0.05, all tests). We found that the diagnostic group had a small effect on the median Jaccard index of all four methods. In conclusion, MAPS had relatively high accuracy and low variability compared to HWA, BET and BSE in MR scans with and without atrophy. (C) 2010 Elsevier Inc. All rights reserved.
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