In recent years a number of semi-automated and automated segmentation tools and brain atlases have been developed to facilitate morphometric analyses of large MRI datasets. These tools are much faster than manual tracing and demonstrate excellent test–retest reliabilities. Reliabilities of automated segmentations relative to “gold standard” manual tracings have, however, been shown to vary by brain region and in different cohorts. It remains uncertain to what extent smaller brain volumes and potential changes in grey/white matter contrasts in paediatric brains impact on the performance of automated methods, and how pathology may influence performance. This study examined whether using data from automated FreeSurfer segmentation would alter our ability, compared to manual segmentation, to detect prenatal alcohol exposure (PAE)-related volume changes in subcortical regions and the corpus callosum (CC) in pre-adolescent children. High-resolution T1-weighted images were acquired, using a sequence optimized for morphometric neuroanatomical analysis, on a Siemens 3T Allegra MRI scanner in 71 right-handed, 9- to 11-year-old children (27 fetal alcohol syndrome (FAS) and partial FAS (PFAS), 25 non-syndromal heavily exposed (HE) and 19 non-exposed controls) from a high-risk community in Cape Town, South Africa. Data from timeline follow-back interviews administered to the mothers prospectively during pregnancy were used to quantify the amount of alcohol (in ounces absolute alcohol per day, AA/day) that the children had been exposed to prenatally. Volumes of corpus callosum (CC) and bilateral caudate nuclei, hippocampi and nucleus accumbens (NA) were obtained by manual tracing and automated segmentation using both FreeSurfer versions 5.1 and 6.0. Reliability across methods was assessed using intraclass correlation (ICC) estimates for consistency and absolute agreement, and Cronbach’s α. Ability to detect regions showing PAE effects was assessed separately for each segmentation method using ANOVA and linear regression of regional volumes with AA/day. Our results support findings from other studies showing excellent reliability across methods for easy-to-segment structures, such as the CC and caudate nucleus. Volumes from FreeSurfer 6.0 were smaller than those from version 5.1 in all regions except the right caudate, for which they were similar, and right hippocampus and CC, for which they were larger. Despite poor absolute agreement between methods in the NA and hippocampus, all three segmentation methods detected dose-dependent volume reductions in regions for which reliabilities on ICC consistency across methods reached at least 0.70, namely the CC, and bilateral caudate nuclei and hippocampi. PAE-related changes in the NA for which ICC consistency did not reach this minimum were inconsistent across methods and should be interpreted with caution. This is the first study to demonstrate in a pre-adolescent cohort the ability of automated segmentation with FreeSurfer to detect regional volume changes associated with pathology similar to those found using manual tracing.

近年来，已经开发了许多半自动和自动分割工具和脑图谱，以促进大型 MRI 数据集的形态测量分析。这些工具比手动跟踪快得多，并且表现出出色的测试-再测试可靠性。然而，自动分割相对于“黄金标准”手动追踪的可靠性已被证明因大脑区域和不同群体而异。目前仍不确定儿科大脑体积较小和灰质/白质对比的潜在变化对自动化方法的性能有何影响，以及病理学如何影响性能。本研究检验了与手动分割相比，使用自动 FreeSurfer 分割的数据是否会改变我们检测青春期前儿童皮层下区域和胼胝体 (CC) 与产前酒精暴露 (PAE) 相关的体积变化的能力。使用针对形态神经解剖学分析而优化的序列，在西门子 3T Allegra MRI 扫描仪上对 71 名 9 至 11 岁的右撇子儿童（27 名胎儿酒精综合症 (FAS) 和来自南非开普敦高风险社区的部分 FAS (PFAS)、25 名非综合征重度暴露 (HE) 和 19 名非暴露对照）。怀孕期间对母亲进行的时间线跟踪访谈数据用于量化孩子在产前接触的酒精量（每天盎司无水酒精，AA/天）。使用 FreeSurfer 5.1 和 6.0 版本通过手动追踪和自动分割获得胼胝体 (CC) 和双侧尾状核、海马和伏隔核 (NA) 的体积。 使用类内相关性 (ICC) 估计的一致性和绝对一致性以及 Cronbach's α 来评估不同方法的可靠性。使用方差分析和 AA/天区域体积的线性回归，分别评估每种分割方法检测显示 PAE 效应的区域的能力。我们的结果支持了其他研究的结果，显示了易于分割结构（例如 CC 和尾状核）的方法具有出色的可靠性。 FreeSurfer 6.0 的所有区域的体积均小于版本 5.1 的体积，但右侧尾状核除外（它们相似），以及右侧海马体和 CC（它们较大）。尽管 NA 和海马的方法之间的绝对一致性较差，但所有三种分割方法都检测到区域的剂量依赖性体积减少，这些区域的 ICC 一致性可靠性至少达到 0.70，即 CC、双侧尾状核和海马。 ICC 一致性未达到最低值的 NA 中与 PAE 相关的变化在不同方法中不一致，应谨慎解释。这是第一项在青春期前队列中证明使用 FreeSurfer 自动分割的能力，以检测与手动追踪发现的病理相关的区域体积变化的能力。