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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 T2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T2-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 T2-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.
Highlights
Brain development during the neonatal period is an important determinant for a range of neurodevelopmental outcomes in childhood and adolescence
Given that the NeoBrainS12 dataset had anisotropic voxels in which the largest dimension was 1.2 or 2 mm, the mean surface distances obtained in the current study suggest that the MANTiS segmentation and manual segmentation boundaries were usually within one voxel of each other
Results are given for the cortical gray matter, white matter, deep nuclear gray matter, cerebellum, cerebrospinal fluid (CSF), brainstem, hippocampus and amygdala, for each group of infants separately
Summary
Brain development during the neonatal period is an important determinant for a range of neurodevelopmental outcomes in childhood and adolescence. Characterizing and quantifying differences in brain developmental trajectories associated with preterm birth is important for generating biomarkers of neurodevelopmental outcomes and for evaluating the efficacy of interventions to improve long-term outcomes for infants born preterm (Anderson et al, 2015; Van Horn and Pelphrey, 2015). Tissue classification enables volumetric studies to provide quantitative measures that are pivotal for studying brain injury and altered development associated with preterm birth, as previously demonstrated (Huppi et al, 1998; Inder et al, 2005; Thompson et al, 2007; Cheong et al, 2013). Tissue classification is a precursor to many other analytic approaches, such as cortical parcellation, cortical thickness measurement and structural connectivity, and can provide seeds and masks for tractography and functional MR imaging studies
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