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

Abstract

Developing accurate subcortical volumetric quantification tools is crucial 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 the hippocampus and subcortical nuclei, including the 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 varied depending on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced significant 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, whereas 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, 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.

Keywords: brain; brain (growth and development); child; neuroimaging.

FIGURE 1

Scatter plots of automated segmentation methods against manual segmentation for the hippocampus. DSC, dice score coefficient

FIGURE 2

Scatter plots of automated segmentation methods against manual segmentation for the amygdala. DSC, dice score coefficient

FIGURE 3

Scatter plots of automated segmentation methods against manual segmentation for the thalamus. DSC, dice score coefficient

FIGURE 4

Scatter plots of automated segmentation methods against manual segmentation for the putamen. DSC, dice score coefficient

FIGURE 5

Scatter plots of automated segmentation methods against manual segmentation for the GP. DSC = dice score coefficient.

FIGURE 6

Scatter plots of automated segmentation methods against manual segmentation for the caudate. DSC, dice score coefficient

FIGURE 7

Scatter plots of automated segmentation methods against manual segmentation for the nucleus accumbens. DSC, dice score coefficient

FIGURE 8

Scatter plots of automated segmentation methods against manual segmentation for the combined segmentations of caudate and nucleus accumbens. DSC, dice score coefficient

FIGURE 9

Transversal view of the segmentations of the hippocampus and amygdala. Yellow, hippocampus; turquoise, amygdala

FIGURE 10

Transversal view of segmentations of the putamen, globus pallidus (GP), thalamus and caudate. Putamen, pink; GP, blue; thalamus, green; caudate, light blue

FIGURE 11

Sagittal view of the thalamus, caudate and nucleus accumbens. Thalamus, green; caudate, light blue; nucleus accumbens, orange