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Related Experiment Video

Updated: May 28, 2026

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains
06:36

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Published on: May 19, 2023

Adaptive neonate brain segmentation.

M Jorge Cardoso1, Andrew Melbourne, Giles S Kendall

  • 1Centre for Medical Image Computing (CMIC), University College London, UK.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 19, 2011
PubMed
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Accurate segmentation of neonatal brain MRI is crucial for predicting neurodevelopmental outcomes. A novel Maximum a posteriori Expectation Maximisation (MAP-EM) algorithm improves segmentation accuracy, outperforming existing methods.

Area of Science:

  • Medical imaging
  • Neuroscience
  • Biomarkers

Background:

  • Premature birth increases neurodevelopmental risks.
  • Brain volume measurement aids in defining neurodevelopmental biomarkers.
  • Neonatal brain MRI segmentation is challenging due to image artifacts and anatomical variability.

Purpose of the Study:

  • To develop a robust and adaptive image segmentation pipeline for neonatal brain MRI.
  • To address challenges like partial volume effects and intensity variations in neonatal brain MRI.
  • To improve the accuracy of brain tissue segmentation for neurodevelopmental outcome prediction.

Main Methods:

  • A novel multi-model Maximum a posteriori Expectation Maximisation (MAP-EM) segmentation algorithm.
  • Incorporation of priors on intensities and tissue proportions.

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Last Updated: May 28, 2026

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains
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Published on: May 19, 2023

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Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

  • Inclusion of B0 inhomogeneity correction and a Markov Random Field for spatial homogeneity.
  • Implicit modeling of Partial Volume (PV) effects.

    • Main Results:

    • The proposed method demonstrates robustness and adaptability to anatomical variations.
    • It effectively mitigates neonatal white/grey matter intensity inversion by modeling PV effects.
    • Statistically significant correlations with gestational age and birthweight were observed.
    • The MAP-EM algorithm achieved statistically significant improvements in Dice scores compared to Maximum Likelihood EM.

    Conclusions:

    • The developed segmentation pipeline offers a significant advancement in neonatal brain MRI analysis.
    • Accurate segmentation is vital for establishing reliable neurodevelopmental biomarkers.
    • The novel MAP-EM approach provides a more accurate and reliable method for segmenting challenging neonatal brain images.