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

Eileanoir Johnson; Sarah Gregory; Hans Johnson; Alexandra Durr; Blair Leavitt; Raymund Roos; Geraint Rees; Sarah Tabrizi; Rachael Scahill

doi:10.3389/fneur.2017.00519

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

Journal article

Open access

Peer reviewed

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

Eileanoir Johnson, Sarah Gregory, Hans Johnson, Alexandra Durr, Blair Leavitt, Raymund Roos, Geraint Rees, Sarah Tabrizi and Rachael Scahill

Frontiers in neurology, Vol.8, pp.519-519

2017

DOI: 10.3389/fneur.2017.00519

PMCID: PMC5641297

PMID: 29066997

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https://doi.org/10.3389/fneur.2017.00519View

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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 pre-manifest 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.

Bioengineering

Life Sciences

Imaging

Neurons and Cognition

Details

Title: Subtitle: Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington’s Disease
Creators: Eileanoir Johnson - UCL Institute of Neurology
Sarah Gregory - UCL Institute of Neurology
Hans Johnson - University of Iowa
Alexandra Durr - ICM Partners
Blair Leavitt - University of British Columbia
Raymund Roos - Department of neurology
Geraint Rees - Wellcome Trust Centre for Neuroimaging
Sarah Tabrizi - UCL Institute of Neurology
Rachael Scahill - UCL Institute of Neurology
Resource Type: Journal article
Publication Details: Frontiers in neurology, Vol.8, pp.519-519
DOI: 10.3389/fneur.2017.00519
PMID: 29066997
PMCID: PMC5641297
NLM abbreviation: Front Neurol
ISSN: 1664-2295
eISSN: 1664-2295
Publisher: Frontiers
Language: English
Date published: 2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry
Record Identifier: 9984185464502771

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