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Front Neurol. 2017 Oct 10;8:519. doi: 10.3389/fneur.2017.00519. eCollection 2017.

Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's Disease.

Author information

1
Huntington's Disease Centre, UCL Institute of Neurology, University College London, London, United Kingdom.
2
Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States.
3
Department of Genetics and Cytogenetics, INSERMUMR S679, APHP, ICM Institute, Hôpital de la Salpêtrière, Paris, France.
4
Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
5
Department of Neurology, Leiden University Medical Centre, Leiden, Netherlands.
6
George-Huntington-Institut, münster, Germany.
7
Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.

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.

KEYWORDS:

FMRIB’s Software Library; FreeSurfer; Huntington’s disease; Multi-Atlas Label Propagation with Expectation–Maximization-based refinement; advanced normalization tools; gray matter; segmentation; statistical parametric mapping

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