Introduction

Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T 2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T 2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray matter for coronal images acquired at 30 weeks. This demonstrates that MANTiS' performance is competitive with existing techniques. For the WUNDeR dataset, mean Dice scores comparing MANTiS with manually edited segmentations demonstrated good agreement, where all scores were above 0.75, except for the hippocampus and amygdala. The results show that MANTiS is able to segment neonatal brain tissues well, even in images that have brain abnormalities common in preterm infants. MANTiS is available for download as an SPM toolbox from http://developmentalimagingmcri.github.io/mantis.

Publications

  1. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation.
    Cite this
    Beare RJ, Chen J, Kelly CE, Alexopoulos D, Smyser CD, Rogers CE, Loh WY, Matthews LG, Cheong JL, Spittle AJ, Anderson PJ, Doyle LW, Inder TE, Seal ML, Thompson DK, 2016-01-01 - Frontiers in neuroinformatics

Credits

  1. Richard J Beare
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  2. Jian Chen
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  3. Claire E Kelly
    Developer

    Murdoch Childrens Research Institute, The Royal Children's Hospital Melbourne, Australia

  4. Dimitrios Alexopoulos
    Developer

    Department of Neurology, Washington University School of Medicine St. Louis, United States of America

  5. Christopher D Smyser
    Developer

    Department of Neurology, Washington University School of Medicine St. Louis, United States of America

  6. Cynthia E Rogers
    Developer

    Department of Psychiatry, Washington University School of Medicine St. Louis, United States of America

  7. Wai Y Loh
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  8. Lillian G Matthews
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  9. Jeanie L Y Cheong
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  10. Alicia J Spittle
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  11. Peter J Anderson
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  12. Lex W Doyle
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  13. Terrie E Inder
    Developer

    Department of Pediatric Newborn Medicine, Harvard Medical School, United States of America

  14. Marc L Seal
    Developer

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

  15. Deanne K Thompson
    Investigator

    Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, Australia

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Summary
AccessionBT000632
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC++
User InterfaceTerminal Command Line
Download Count0
Country/RegionAustralia
Submitted ByDeanne K Thompson