Abstract
The volumetric quantification of brain structures is of great interest in pediatric populations because it allows the investigation of different factors influencing neurodevelopment. FreeSurfer and FSL both provide frequently used packages for automatic segmentation of brain structures. In this study, we examined the accuracy and consistency of those two automated protocols relative to manual segmentation, commonly considered as the “gold standard” technique, for estimating hippocampus and amygdala volumes in a sample of preadolescent children aged between 6 to 11years. The volumes obtained with FreeSurfer and FSL-FIRST were evaluated and compared with manual segmentations with respect to volume difference, spatial agreement and between- and within-method correlations.Results highlighted a tendency for both automated techniques to overestimate hippocampus and amygdala volumes, in comparison to manual segmentation. This was more pronounced when using FreeSurfer than FSL-FIRST and, for both techniques, the overestimation was more marked for the amygdala than the hippocampus. Pearson correlations support moderate associations between manual tracing and FreeSurfer for hippocampus (right r=0.69, p<0.001; left r=0.77, p<0.001) and amygdala (right r=0.61, p<0.001; left r=0.67, p<0.001) volumes. Correlation coefficients between manual segmentation and FSL-FIRST were statistically significant (right hippocampus r=0.59, p<0.001; left hippocampus r=0.51, p<0.001; right amygdala r=0.35, p<0.001; left amygdala r=0.31, p<0.001) but were significantly weaker, for all investigated structures. When computing intraclass correlation coefficients between manual tracing and automatic segmentation, all comparisons, except for left hippocampus volume estimated with FreeSurfer, failed to reach 0.70. When looking at each method separately, correlations between left and right hemispheric volumes showed strong associations between bilateral hippocampus and bilateral amygdala volumes when assessed using manual segmentation or FreeSurfer. These correlations were significantly weaker when volumes were assessed with FSL-FIRST. Finally, Bland–Altman plots suggest that the difference between manual and automatic segmentation might be influenced by the volume of the structure, because smaller volumes were associated with larger volume differences between techniques.These results demonstrate that, at least in a pediatric population, the agreement between amygdala and hippocampus volumes obtained with automated FSL-FIRST and FreeSurfer protocols and those obtained with manual segmentation is not strong. Visual inspection by an informed individual and, if necessary, manual correction of automated segmentation outputs are important to ensure validity of volumetric results and interpretation of related findings.
Highlights
Childhood is a period of great relevance in the development of risk factors for various neuropsychiatric conditions (Paus et al, 2008)
While we did not find any studies comparing the performance of automated segmentation performed with FSL-FIRST and/or FreeSurfer to manual segmentation in pediatric populations, the validity of these protocols has previously been assessed in healthy adult controls (Cherbuin et al, 2009; Morey et al, 2009; Patenaude et al, 2011) as well as different clinical populations, such as Alzheimer Disease (Pipitone et al, 2014; Sánchez-Benavides et al, 2010; Shen et al, 2010), mood disorders (Doring et al, 2011; Nugent et al, 2013; Tae et al, 2008), temporal-lobe epilepsy (Akhondi-Asl et al, 2011; Pardoe et al, 2009) and psychosis (Pipitone et al, 2014)
When computing the difference between FSL-FIRST and manual segmentation, the mean percentage of volume difference was of 27.61% (SD = 14.49) and 28.39% (SD = 13.07) for the left and right hippocampi, respectively and of 50.32% (SD = 27.65) and 40.29% (SD = 26.09) for the left and right amygdala, respectively
Summary
Childhood is a period of great relevance in the development of risk factors for various neuropsychiatric conditions (Paus et al, 2008). While we did not find any studies comparing the performance of automated segmentation performed with FSL-FIRST and/or FreeSurfer to manual segmentation in pediatric populations, the validity of these protocols has previously been assessed in healthy adult controls (Cherbuin et al, 2009; Morey et al, 2009; Patenaude et al, 2011) as well as different clinical populations, such as Alzheimer Disease (Pipitone et al, 2014; Sánchez-Benavides et al, 2010; Shen et al, 2010), mood disorders (Doring et al, 2011; Nugent et al, 2013; Tae et al, 2008), temporal-lobe epilepsy (Akhondi-Asl et al, 2011; Pardoe et al, 2009) and psychosis (Pipitone et al, 2014). Studies investigating the validity of automated segmentation in children are needed
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