Format

Send to

Choose Destination
See comment in PubMed Commons below
PLoS One. 2014 Jan 29;9(1):e77810. doi: 10.1371/journal.pone.0077810. eCollection 2014.

Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates.

Author information

  • 1School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China ; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America.
  • 2Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America.
  • 3Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, Maryland, United States of America.
  • 4School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China.
  • 5Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, United States of America ; Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.

Abstract

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55 ∼ 90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18 ∼ 96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5 ∼ 18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.

[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk