Subcortical brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation

Abstract

Developing accurate subcortical volumetric quantification tools is a crucial issue for neurodevelopmental studies, as they could reduce the need for challenging and time-consuming manual segmentation. In this study the accuracy of two automated segmentation tools, FSL-FIRST (with three different boundary correction settings) and FreeSurfer were compared against manual segmentation of subcortical nuclei, including the hippocampus, amygdala, thalamus, putamen, globus pallidus, caudate and nucleus accumbens, using volumetric and correlation analyses in 80 5-year-olds. Both FSL-FIRST and FreeSurfer overestimated the volume on all structures except the caudate, and the accuracy depended on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced considerable overestimations and weaker correlations with all automated methods. Larger and more readily distinguishable structures such as the caudate and putamen produced notably lower overestimations and stronger correlations. Overall, the segmentations performed by FSL-FIRST's Default pipeline were the most accurate, while FreeSurfer's results were weaker across the structures. In line with prior studies, the accuracy of automated segmentation tools was imperfect with respect to manually defined structures. However, apart from amygdala and nucleus accumbens, FSL-FIRST's agreement could be considered satisfactory (Pearson correlation > 0.74, Intraclass correlation coefficient (ICC) > 0.68 and Dice Score coefficient (DSC) > 0.87) with highest values for the striatal structures (putamen, globus pallidus and caudate) (Pearson correlation > 0.77, ICC > 0.87 and DSC > 0.88, respectively). Overall, automated segmentation tools do not always provide satisfactory results, and careful visual inspection of the automated segmentations is strongly advised.

Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab

“…On the other hand, cerebellum was mislabeled only once in our final sample ( Supplementary Figure 19 ). Additionally, we did observe erroneous automatic segmentation in the subcortical regions, and we are preparing an article regarding the manual segmentation of these areas ( Lidauer et al, 2021 ). Similarly, pediatric atlases have their own challenges.…”

“…Errors were addressed by adding control points, but the edits were largely unsuccessful. Consequently, we are currently working on separately validating subcortical segmentation procedures for our data ( Lidauer et al, 2021 ). All information regarding the subcortical labeling is presented in Supplementary Material ( Data Sheet 2 , pages 15–16, Subcortex).…”