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.

MRI Volumetric Analysis of the Hypothalamus and Limbic System across the Pediatric Age Span

“…Similarly, the hippocampus growth trajectory is consistent with prior studies, which reported a fast volume increase until 8 to 10 years of life followed by a slower volume increase [ 37 ]. The hippocampal volume at 5 years of age is consistent with manual segmentations in a cohort from Finland [ 38 ] and the amygdala volume is consistent with a prior study by Zhou and colleagues [ 39 ]. Østby et al reported the thalamic volume in normal developing children at ages 8–11 and 12–15.…”

Deep learning segmentation of organs‐at‐risk with integration into clinical workflow for pediatric brain radiotherapy

“…While such methods have shown state‐of‐the‐art segmentation performance, they were developed specifically for the adult brain and showed limitations when tested on pediatric data. Methods focusing specifically on pediatric brain segmentation have also been investigated but focused on specific applications targeting a single OAR 16,17 . Additionally, these methods do not segment structures proven to be associated with neurocognitive functions such as the frontal white matter, temporal lobe, and corpus callosum.…”

“…Methods focusing specifically on pediatric brain segmentation have also been investigated but focused on specific applications targeting a single OAR. 16 , 17 Additionally, these methods do not segment structures proven to be associated with neurocognitive functions such as the frontal white matter, temporal lobe, and corpus callosum.…”