title: Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation.
abstract: 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.
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Department of Medicine, Monash Medical Centre, Monash UniversityMelbourne, VIC, Australia.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Beare
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Department of Medicine, Monash Medical Centre, Monash UniversityMelbourne, VIC, Australia.
authors.forename: Jian
authors.initials: J
authors.lastname: Chen
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's Hospital Melbourne, VIC, Australia.
authors.forename: Claire E
authors.initials: CE
authors.lastname: Kelly
authors.affiliation: Department of Neurology, Washington University School of Medicine St. Louis, MO, USA.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Alexopoulos
authors.affiliation: Department of Neurology, Washington University School of Medicine St. Louis, MO, USA.
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Smyser
authors.affiliation: Department of Psychiatry, Washington University School of Medicine St. Louis, MO, USA.
authors.forename: Cynthia E
authors.initials: CE
authors.lastname: Rogers
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Florey Institute of Neuroscience and Mental HealthMelbourne, VIC, Australia.
authors.forename: Wai Y
authors.initials: WY
authors.lastname: Loh
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Department of Paediatrics, University of MelbourneMelbourne, VIC, Australia; Royal Women's HospitalMelbourne, VIC, Australia.
authors.forename: Lillian G
authors.initials: LG
authors.lastname: Matthews
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Royal Women's HospitalMelbourne, VIC, Australia; Department of Obstetrics and Gynaecology, University of MelbourneMelbourne, VIC, Australia.
authors.forename: Jeanie L Y
authors.initials: JL
authors.lastname: Cheong
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Royal Women's HospitalMelbourne, VIC, Australia; Department of Physiotherapy, University of MelbourneMelbourne, VIC, Australia.
authors.forename: Alicia J
authors.initials: AJ
authors.lastname: Spittle
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Department of Paediatrics, University of MelbourneMelbourne, VIC, Australia.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Anderson
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Department of Paediatrics, University of MelbourneMelbourne, VIC, Australia; Royal Women's HospitalMelbourne, VIC, Australia; Department of Obstetrics and Gynaecology, University of MelbourneMelbourne, VIC, Australia.
authors.forename: Lex W
authors.initials: LW
authors.lastname: Doyle
authors.affiliation: Department of Pediatric Newborn Medicine, Harvard Medical School, Brigham and Women's Hospital Boston, MA, USA.
authors.forename: Terrie E
authors.initials: TE
authors.lastname: Inder
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Department of Paediatrics, University of MelbourneMelbourne, VIC, Australia.
authors.forename: Marc L
authors.initials: ML
authors.lastname: Seal
authors.affiliation: Murdoch Childrens Research Institute, The Royal Children's HospitalMelbourne, VIC, Australia; Florey Institute of Neuroscience and Mental HealthMelbourne, VIC, Australia; Department of Paediatrics, University of MelbourneMelbourne, VIC, Australia.
authors.forename: Deanne K
authors.initials: DK
authors.lastname: Thompson
keywords: magnetic resonance imaging; neonate; preterm birth; statistical parametric mapping; tissue classification