首页|Subcortical and hippocampal brain segmentation in 5‐year‐old children: Validation of FSL‐FIRST and FreeSurfer against manual segmentation
Subcortical and hippocampal brain segmentation in 5‐year‐old children: Validation of FSL‐FIRST and FreeSurfer against manual segmentation
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Wiley
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.
brainbrain (growth and development)childneuroimaging
Kristian Lidauer、Elmo P. Pulli、Anni Copeland、Linnea Karlsson、Hasse Karlsson、Jetro J. Tuulari、Eero Silver、Venla Kumpulainen、Niloofar Hashempour、Harri Merisaari、Jani Saunavaara、Riitta Parkkola、Tuire L?hdesm?ki、Ekaterina Saukko、Saara Nolvi、Eeva‐Leena Kataja
展开 >
FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine
Department of Medical Physics,Turku University Hospital
Department of Radiology,University of Turku
Department of Paediatric Neurology,Turku University Hospital and University of Turku
NSTL
Wiley
