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Updated: Jun 6, 2026

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
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Comprehensive brain MRI segmentation in high risk preterm newborns.

Xintian Yu1, Yanjie Zhang, Robert E Lasky

  • 1Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Health Science Center at Houston Medical School, Houston, Texas, United States of America.

Plos One
|November 17, 2010
PubMed
Summary

Developing accurate brain MRI segmentation methods is crucial for assessing extremely preterm newborns. Our integrated approach enhances biomarker reliability for neonatal care and neurodevelopmental outcome prediction.

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Area of Science:

  • Neuroimaging
  • Neonatal Neurology
  • Biomarker Development

Background:

  • Extremely preterm newborns often show brain abnormalities like cerebral atrophy and white matter signal changes on MRI.
  • Accurate MRI brain volumes are needed as biomarkers for evaluating neonatal intensive care effectiveness and predicting neurodevelopmental outcomes.
  • Reliable brain MRI segmentation methods are essential for this quantification.

Purpose of the Study:

  • To develop and validate accurate and reliable brain MRI segmentation methods for high-risk newborns.
  • To assess the reliability of manual segmentation and the accuracy of a semi-automated approach.
  • To integrate manual and automated methods for efficient and comprehensive brain segmentation.

Main Methods:

  • Manual segmentation of nine subcortical structures by two raters on T2-weighted MRI scans from 20 extremely preterm infants.

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Last Updated: Jun 6, 2026

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  • Assessment of intra-rater and inter-rater reliability using repeatability and intra-class correlation coefficients (ICC).
  • Development of a semi-automated segmentation combining Hidden Markov Random Field Expectation Maximization and Parzen window classifier, with manual correction for improved accuracy.

    • Main Results:

    • High intra-rater reliability (ICC: 0.97-0.99) and good inter-rater reliability (ICC: 0.93-0.99) for manual segmentation.
    • Semi-automated segmentation achieved high accuracy (similarity index: 0.87-0.89) after manual correction.
    • Integrated approach generated full brain segmentation within two hours.

    Conclusions:

    • A comprehensive, integrated manual and semi-automated MRI segmentation approach provides accurate and reliable brain volume quantification in extremely preterm infants.
    • This method facilitates the evaluation of large cohorts for assessing neonatal care effects and neurodevelopmental outcomes.
    • Regional brain volumes derived from this approach show promise as biomarkers and surrogate endpoints.