Brain MAPS: an automated, accurate and robust brain extraction technique using a template library

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

107 Citations (Scopus)

Abstract

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 1.5T and 157 3T 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 1.5T and 3T 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 1.5T and 3T 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 1.5T scans and ≤0.019% for 3T scans, both methods). The median Jaccard index of MAPS were similar in both 1.5T and 3T scans, whereas those of BET, BSE and HWA were higher in 1.5T scans than 3T 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.

Original languageEnglish
Pages (from-to)1091-1108
Number of pages18
JournalNeuroImage
Volume55
Issue number3
Early online date31 Dec 2010
DOIs
Publication statusPublished - 1 Apr 2011

Keywords

  • Algorithms
  • Alzheimer Disease/pathology
  • Artifacts
  • Atlases as Topic
  • Atrophy
  • Automatic Data Processing
  • Brain/anatomy & histology
  • Brain Mapping/methods
  • Databases, Factual
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Magnetic Resonance Imaging

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