Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE-HD cohort
Journal article
Alexander, Bonnie, Georgiou-Karistianis, Nellie, Beare, Richard, Ahveninen, Lotta M., Lorenzetti, Valentina, Stout, Julie C. and Glikmann-Johnston, Yifat. (2020). Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE-HD cohort. Human Brain Mapping. 41(7), pp. 1875-1888. https://doi.org/10.1002/hbm.24918
Authors | Alexander, Bonnie, Georgiou-Karistianis, Nellie, Beare, Richard, Ahveninen, Lotta M., Lorenzetti, Valentina, Stout, Julie C. and Glikmann-Johnston, Yifat |
---|---|
Abstract | Smaller manually-segmented amygdala volumes have been associated with poorer motor and cognitive function in Huntington's disease (HD). Manual segmentation is the gold standard in terms of accuracy; however, automated methods may be necessary in large samples. Automated segmentation accuracy has not been determined for the amygdala in HD. We aimed to determine which of three automated approaches would most accurately segment amygdalae in HD: FreeSurfer, FIRST, and ANTS nonlinear registration followed by FIRST segmentation. T1-weighted images for the IMAGE-HD cohort including 35 presymptomatic HD (pre-HD), 36 symptomatic HD (symp-HD), and 34 healthy controls were segmented using FreeSurfer and FIRST. For the third approach, images were nonlinearly registered to an MNI template using ANTS, then segmented using FIRST. All automated methods overestimated amygdala volumes compared with manual segmentation. Dice overlap scores, indicating segmentation accuracy, were not significantly different between automated approaches. Manually segmented volumes were most statistically differentiable between groups, followed by those segmented by FreeSurfer, then ANTS/FIRST. FIRST-segmented volumes did not differ between groups. All automated methods produced a bias where volume overestimation was more severe for smaller amygdalae. This bias was subtle for FreeSurfer, but marked for FIRST, and moderate for ANTS/FIRST. Further, FreeSurfer introduced a hemispheric bias not evident with manual segmentation, producing larger right amygdalae by 8%. To assist choice of segmentation approach, we provide sample size estimation graphs based on sample size and other factors. If automated segmentation is employed in samples of the current size, FreeSurfer may effectively distinguish amygdala volume between controls and HD. |
Keywords | amygdala; atrophy; Huntington's disease; segmentation; subcortical; tracing |
Year | 2020 |
Journal | Human Brain Mapping |
Journal citation | 41 (7), pp. 1875-1888 |
Publisher | John Wiley & Sons, Inc. |
ISSN | 1065-9471 |
Digital Object Identifier (DOI) | https://doi.org/10.1002/hbm.24918 |
Scopus EID | 2-s2.0-85079161255 |
Open access | Published as ‘gold’ (paid) open access |
Research or scholarly | Research |
Page range | 1875-1888 |
Funder | National Health and Medical Research Council (NHMRC) |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 07 Feb 2020 |
Publication process dates | |
Accepted | 18 Dec 2019 |
Deposited | 23 Jun 2021 |
Grant ID | NHMRC/606650 |
NHMRC/1100862 |
https://acuresearchbank.acu.edu.au/item/8w442/accuracy-of-automated-amygdala-mri-segmentation-approaches-in-huntington-s-disease-in-the-image-hd-cohort
Download files
Publisher's version
OA_Alexander_2020_Accuracy_of_automated_amygdala_MRI_segmentation.pdf | |
License: CC BY 4.0 | |
File access level: Open |
88
total views38
total downloads4
views this month2
downloads this month