TY - JOUR
T1 - Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle
AU - Secondulfo, Laura
AU - Ogier, Augustin C.
AU - Monte, Jithsa R.
AU - Aengevaeren, Vincent L.
AU - Bendahan, David
AU - Nederveen, Aart J.
AU - Strijkers, Gustav J.
AU - Hooijmans, Melissa T.
N1 - Funding Information: Sportinnovator grant of The Netherlands Organization for Health Research and Development, ZonMw (50-53800-98-PR020). Dutch Technology Foundation TTW (DIMASK 15500). Publisher Copyright: © 2020 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Diffusion tensor imaging (DTI) is becoming a relevant diagnostic tool to understand muscle disease and map muscle recovery processes following physical activity or after injury. Segmenting all the individual leg muscles, necessary for quantification, is still a time-consuming manual process. The purpose of this study was to evaluate the impact of a supervised semi-automatic segmentation pipeline on the quantification of DTI indices in individual upper leg muscles. Longitudinally acquired MRI datasets (baseline, post-marathon and follow-up) of the upper legs of 11 subjects were used in this study. MR datasets consisted of a DTI and Dixon acquisition. Semi-automatic segmentations for the upper leg muscles were performed using a transversal propagation approach developed by Ogier et al on the out-of-phase Dixon images at baseline. These segmentations were longitudinally propagated for the post-marathon and follow-up time points. Manual segmentations were performed on the water image of the Dixon for each of the time points. Dice similarity coefficients (DSCs) were calculated to compare the manual and semi-automatic segmentations. Bland-Altman and regression analyses were performed, to evaluate the impact of the two segmentation methods on mean diffusivity (MD), fractional anisotropy (FA) and the third eigenvalue (λ3). The average DSC for all analyzed muscles over all time points was 0.92 ± 0.01, ranging between 0.48 and 0.99. Bland-Altman analysis showed that the 95% limits of agreement for MD, FA and λ3 ranged between 0.5% and 3.0% for the transversal propagation and between 0.7% and 3.0% for the longitudinal propagations. Similarly, regression analysis showed good correlation for MD, FA and λ3 (r = 0.99, p < 60; 0.0001). In conclusion, the supervised semi-automatic segmentation framework successfully quantified DTI indices in the upper-leg muscles compared with manual segmentation while only requiring manual input of 30% of the slices, resulting in a threefold reduction in segmentation time.
AB - Diffusion tensor imaging (DTI) is becoming a relevant diagnostic tool to understand muscle disease and map muscle recovery processes following physical activity or after injury. Segmenting all the individual leg muscles, necessary for quantification, is still a time-consuming manual process. The purpose of this study was to evaluate the impact of a supervised semi-automatic segmentation pipeline on the quantification of DTI indices in individual upper leg muscles. Longitudinally acquired MRI datasets (baseline, post-marathon and follow-up) of the upper legs of 11 subjects were used in this study. MR datasets consisted of a DTI and Dixon acquisition. Semi-automatic segmentations for the upper leg muscles were performed using a transversal propagation approach developed by Ogier et al on the out-of-phase Dixon images at baseline. These segmentations were longitudinally propagated for the post-marathon and follow-up time points. Manual segmentations were performed on the water image of the Dixon for each of the time points. Dice similarity coefficients (DSCs) were calculated to compare the manual and semi-automatic segmentations. Bland-Altman and regression analyses were performed, to evaluate the impact of the two segmentation methods on mean diffusivity (MD), fractional anisotropy (FA) and the third eigenvalue (λ3). The average DSC for all analyzed muscles over all time points was 0.92 ± 0.01, ranging between 0.48 and 0.99. Bland-Altman analysis showed that the 95% limits of agreement for MD, FA and λ3 ranged between 0.5% and 3.0% for the transversal propagation and between 0.7% and 3.0% for the longitudinal propagations. Similarly, regression analysis showed good correlation for MD, FA and λ3 (r = 0.99, p < 60; 0.0001). In conclusion, the supervised semi-automatic segmentation framework successfully quantified DTI indices in the upper-leg muscles compared with manual segmentation while only requiring manual input of 30% of the slices, resulting in a threefold reduction in segmentation time.
KW - applications
KW - diffusion tensor imaging (DTI)
KW - methods and engineering
KW - muscle
KW - musculoskeletal
KW - post-acquisition processing
KW - quantitation
UR - http://www.scopus.com/inward/record.url?scp=85091733077&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/nbm.4406
DO - https://doi.org/10.1002/nbm.4406
M3 - Article
C2 - 33001508
SN - 0952-3480
VL - 34
JO - NMR in biomedicine
JF - NMR in biomedicine
IS - 1
M1 - e4406
ER -