Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates

Abstract

Background: Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about the accuracy of how skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FMRIB Software Library's Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-weighted imaging (T1WI)-based brain structural network. Methods: Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a Johns Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, C p ; characteristic path length, L p ; local efficiency, E local; global efficiency, E global) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows. Results: There were significant differences in volumes of 50 brain regions between the three workflows (P < 0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased C p, increased L p, decreased E local, and decreased E global, in contrast to the two automatic ones. Conclusions: Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.