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. 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, Cp; characteristic path length, Lp; local efficiency, Elocal; global efficiency, Eglobal) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows. 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 Cp, increased Lp, decreased Elocal, and decreased Eglobal, in contrast to the two automatic ones. Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.

中文翻译：

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颅骨剥离对足月新生儿基于三维T1加权成像的脑结构网络的构建的影响。

颅骨剥离仍然是新生儿脑MR图像分析的挑战。但是，关于颅骨剥离如何影响新生儿脑组织分割和后续网络构建的准确性知之甚少。因此，本文旨在通过比较构造3D T1加权成像（T1WI）的两个自动过程（FMRIB软件库的脑部提取工具BET；婴儿脑部提取和分析工具箱iBEAT）和半自动过程（带手动校正的iBEAT）来阐明此问题。 ）为基础的大脑结构网络。回顾性研究了22例经MRI检查没有异常的足月新生儿（胎龄37-42周；男孩/女孩13/9）。分别使用两个自动（BET和iBEAT）和一个半自动预处理（带手动校正的iBEAT）工作流程来进行颅骨剥离。脑组织分割和体积计算通过基于约翰·霍普金斯地图集的方法进行。选择了64个灰质区域作为结点。体积协方差网络及其性质（聚类系数Cp；特征路径长度Lp；局部效率Elocal；整体效率Eglobal）通过GRETNA计算。方差分析（ANOVA）用于比较三个工作流程之间的计算量差异。三种工作流程之间的50个大脑区域的体积存在显着差异（P <0.05）。三个新生儿大脑结构网络呈现了小世界拓扑。半自动工作流程显示Cp明显降低，与两种自动方式相比，Lp增加，Elocal降低和Eglobal降低。不完全的颅骨剥离确实影响了足月新生儿的大脑结构网络的准确性。