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Automatic cortical segmentation in the developing brain.

Hui Xue1, Latha Srinivasan, Shuzhou Jiang

  • 1Imaging Sciences Department, Imperial College, London, Du cane Road, UK. hui.xue@imperial.ac.uk

Information Processing in Medical Imaging : Proceedings of the ... Conference
|July 19, 2007
PubMed
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This study presents an automatic algorithm to segment neonatal brains from MRI scans, improving accuracy in differentiating grey and white matter, crucial for infant brain development research.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Developmental Neuroscience

Background:

  • Neonatal brain MRI segmentation is challenging due to incomplete myelination causing inverted grey matter (GM) and white matter (WM) contrast.
  • Incomplete myelination leads to mislabeled voxels, particularly at the grey matter (GM) and cerebrospinal fluid (CSF) interface.
  • Accurate segmentation is vital for understanding early brain development and identifying potential abnormalities.

Purpose of the Study:

  • To develop a fully automatic algorithm for accurate neonatal cortical segmentation.
  • To address challenges posed by inverted GM/WM contrast and partial volume effects in infant MR images.
  • To improve the reliability of brain structure analysis in neonates.

Main Methods:

  • A knowledge-based approach was employed to detect and correct mislabeled voxels.

Related Experiment Videos

  • Local priors were adjusted to favor correct classification of grey matter (GM) and white matter (WM).
  • The algorithm was validated on 25 neonates (27–45 weeks gestational age) and compared against manual segmentation.

    • Main Results:

    • The proposed algorithm successfully corrected segmentation errors in both grey matter (GM) and white matter (WM) compared to the Expectation-Maximization (EM) scheme.
    • Quantitative analysis showed good performance with mean Dice similarity coefficients of 0.758 ± 0.037 for GM and 0.794 ± 0.078 for WM.
    • The method demonstrated robustness across a range of neonatal gestational ages.

    Conclusions:

    • The developed automatic algorithm significantly enhances the accuracy of neonatal brain MRI segmentation.
    • This method offers a reliable tool for researchers studying infant brain development and neurological conditions.
    • Improved segmentation accuracy facilitates more precise quantitative analysis of neonatal brain structures.