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Related Experiment Video

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Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation.

Richard J Beare1, Jian Chen1, Claire E Kelly2

  • 1Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Department of Medicine, Monash Medical Centre, Monash UniversityMelbourne, VIC, Australia.

Frontiers in Neuroinformatics
|April 12, 2016
PubMed
Summary

A new method, Morphologically Adaptive Neonatal Tissue Segmentation (MANTiS), accurately segments brain tissues in preterm infants. This tool aids in studying neonatal brain development and potential biomarkers for neurodevelopmental outcomes.

Keywords:
magnetic resonance imagingneonatepreterm birthstatistical parametric mappingtissue classification

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Medical Image Analysis

Background:

  • Neonatal brain tissue classification is crucial for understanding typical and atypical brain development, especially in preterm infants.
  • Standard magnetic resonance imaging (MRI) analysis methods face challenges with neonatal brain images, necessitating specialized approaches.
  • Accurate segmentation can identify biomarkers for neurodevelopmental outcomes.

Purpose of the Study:

  • To introduce MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), a novel method for neonatal brain tissue classification.
  • To adapt existing unified segmentation techniques for the unique characteristics of neonatal brain MRI.
  • To segment the neonatal brain into eight distinct tissue classes.

Main Methods:

  • MANTiS combines unified segmentation with template adaptation using morphological segmentation and topological filtering.
  • The method was evaluated on two independent datasets: NeoBrainS12 and Washington University NeuroDevelopmental Research (WUNDeR).
  • Performance was assessed by comparing MANTiS segmentations against manual and manually edited segmentations using Dice scores.

Main Results:

  • MANTiS achieved competitive performance on the NeoBrainS12 dataset, with mean Dice scores generally above 0.7.
  • On the WUNDeR dataset, MANTiS demonstrated good agreement, with mean Dice scores above 0.75 for most tissue classes.
  • The method effectively segmented neonatal brain tissues, including those with abnormalities common in preterm infants.

Conclusions:

  • MANTiS provides accurate and reliable neonatal brain tissue segmentation, addressing challenges specific to this population.
  • The tool's performance is comparable to existing techniques and shows good agreement with manual segmentations.
  • MANTiS is a valuable tool for research into neonatal brain development and neurodevelopmental outcomes, available as an SPM toolbox.