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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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BEaST: brain extraction based on nonlocal segmentation technique.

Simon F Eskildsen1, Pierrick Coupé, Vladimir Fonov

  • 1McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Canada. se@hst.aau.dk

Neuroimage
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

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Brain extraction is challenging due to image variability. A new robust method, BEaST, uses nonlocal segmentation and a prior library for accurate brain segmentation, outperforming existing methods.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Brain extraction is crucial for analyzing brain images.
  • Variations in brain morphology and MRI sequences complicate automated brain extraction.
  • Existing methods struggle with consistency and accuracy across diverse datasets.

Purpose of the Study:

  • To develop a robust and accurate brain extraction method (BEaST).
  • To achieve consistent brain segmentation across different individuals and imaging conditions.
  • To improve upon the performance of existing brain extraction algorithms.

Main Methods:

  • Nonlocal segmentation embedded in a multi-resolution framework.
  • Semi-automatic construction of a prior library from large neuroimaging databases (e.g., ADNI).

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  • Leave-one-out cross-validation using a subset of the prior library.

    • Main Results:

    • Achieved a high mean Dice similarity coefficient (0.9834±0.0053) in cross-validation.
    • Ranked top in the online Segmentation Validation Engine (mean Dice 0.9781±0.0047).
    • Demonstrated robustness on Alzheimer's Disease Neuroimaging Initiative (ADNI) data with a low failure rate.
    • Outperformed two widely used methods and recent hybrid approaches in segmentation accuracy.
    • Achieved comparable results to a label fusion approach, but was 40x faster with a smaller prior library.

    Conclusions:

    • BEaST offers a robust, accurate, and efficient solution for brain extraction.
    • The method's performance surpasses current state-of-the-art techniques.
    • BEaST is suitable for large-scale neuroimaging studies requiring reliable brain segmentation.