Abstract
Pediatric brain volumetric analysis based on Magnetic Resonance Imaging (MRI) is of particular interest in order to understand the typical brain development and to characterize neurodevelopmental disorders at an early age. However, it has been shown that the results can be biased due to head motion, inherent to pediatric data, and due to the use of methods based on adult brain data that are not able to accurately model the anatomical disparity of pediatric brains. To overcome these issues, we proposed childmetrix, a tool developed for the analysis of pediatric neuroimaging data that uses an age-specific atlas and a probabilistic model-based approach in order to segment the gray matter (GM) and white matter (WM). The tool was extensively validated on 55 scans of children between 5 and 6 years old (including 13 children with developmental dyslexia) and 10 pairs of test-retest scans of children between 6 and 8 years old and compared with two state-of-the-art methods using an adult atlas, namely icobrain (applying a probabilistic model-based segmentation) and Freesurfer (applying a surface model-based segmentation). The results obtained with childmetrix showed a better reproducibility of GM and WM segmentations and a better robustness to head motion in the estimation of GM volume compared to Freesurfer. Evaluated on two subjects, childmetrix showed good accuracy with 82–84% overlap with manual segmentation for both GM and WM, thereby outperforming the adult-based methods (icobrain and Freesurfer), especially for the subject with poor quality data. We also demonstrated that the adult-based methods needed double the number of subjects to detect significant morphological differences between dyslexics and typical readers. Once further developed and validated, we believe that childmetrix would provide appropriate and reliable measures for the examination of children's brain.
Highlights
Brain volumetric analyses have been performed in many Magnetic Resonance Imaging (MRI)-based studies on typical brain development and neurodevelopmental disorders (Anderson et al, 2012; Holland et al, 2014; Hu et al, 2013; Krogsrud et al, 2014; Nie et al, 2013)
Manual segmentation is still considered as the gold standard, this procedure is subject to inter- and intra-rater variability and its application is rather limited for population-based studies or clinical practice, since it requires time investment and excellent anatomical expertise
We propose a tool adapted to the pediatric population, called childmetrix, which applies a probabilistic model-based segmentation using an age-matched pediatric atlas in order to segment the whole brain into gray matter (GM) and white matter (WM), and to estimate the tissue volume
Summary
Brain volumetric analyses have been performed in many Magnetic Resonance Imaging (MRI)-based studies on typical brain development and neurodevelopmental disorders (Anderson et al, 2012; Holland et al, 2014; Hu et al, 2013; Krogsrud et al, 2014; Nie et al, 2013). In order to quantify the structural anatomy of the brain, volume measurements are extracted by segmenting anatomical MRI scans, which are typically T1weighted images. Automated methods have been developed to address issues introduced by the processing of large amounts of data. In the analysis of pediatric data, there are two main issues that automated methods should be able to overcome (Phan et al, 2017)
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