Abstract
The selection of an appropriate segmentation tool is a challenge facing any researcher aiming to measure gray matter (GM) volume. Many tools have been compared, yet there is currently no method that can be recommended above all others; in particular, there is a lack of validation in disease cohorts. This work utilizes a clinical dataset to conduct an extensive comparison of segmentation tools. Our results confirm that all tools have advantages and disadvantages, and we present a series of considerations that may be of use when selecting a GM segmentation method, rather than a ranking of these tools. Seven segmentation tools were compared using 3 T MRI data from 20 controls, 40 premanifest Huntington’s disease (HD), and 40 early HD participants. Segmented volumes underwent detailed visual quality control. Reliability and repeatability of total, cortical, and lobular GM were investigated in repeated baseline scans. The relationship between each tool was also examined. Longitudinal within-group change over 3 years was assessed via generalized least squares regression to determine sensitivity of each tool to disease effects. Visual quality control and raw volumes highlighted large variability between tools, especially in occipital and temporal regions. Most tools showed reliable performance and the volumes were generally correlated. Results for longitudinal within-group change varied between tools, especially within lobular regions. These differences highlight the need for careful selection of segmentation methods in clinical neuroimaging studies. This guide acts as a primer aimed at the novice or non-technical imaging scientist providing recommendations for the selection of cohort-appropriate GM segmentation software.
Highlights
Neuroimaging is widely used to investigate both pathological and non-pathological neural phenomena
Methodological comparisons of these tools have focused mainly on built-in automated segmentation software within standard neuroimaging analysis packages including Statistical Parametric Mapping (SPM), FMRIB’s Software Library (FSL), and FreeSurfer or on optimization of a single application [2–10]. Using these methods on phantom data has shown that both SPM 8 and FSL FAST are reliable and accurate, whereas FreeSurfer appears to be highly reliable but not necessarily accurate for measuring Gray matter (GM) volume [3], and both SPM 5 and FSL were recommended for GM sensitivity in phantom and control data [9]
Longitudinal analysis demonstrated that while the pattern of total GM and cortical GM (CGM) change was similar across tools, when GM change in Huntington’s disease (HD) participants was statistically compared to GM change in controls, the tools detected differing degrees of change
Summary
Neuroimaging is widely used to investigate both pathological and non-pathological neural phenomena. There are currently a number of automated tools that can be used for GM segmentation Methodological comparisons of these tools have focused mainly on built-in automated segmentation software within standard neuroimaging analysis packages including Statistical Parametric Mapping (SPM), FMRIB’s Software Library (FSL), and FreeSurfer or on optimization of a single application [2–10]. Using these methods on phantom data has shown that both SPM 8 and FSL FAST (version 4.1) are reliable and accurate, whereas FreeSurfer (version 4.5) appears to be highly reliable but not necessarily accurate for measuring GM volume [3], and both SPM 5 and FSL were recommended for GM sensitivity in phantom and control data [9]
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