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Forschungsdatenbank PMU-SQQUID

Validation of an active shape model-based semi-automated segmentation algorithm for the analysis of thigh muscle and adipose tissue cross-sectional areas.
Kemnitz, J; Eckstein, F; Culvenor, AG; Ruhdorfer, A; Dannhauer, T; Ring-Dimitriou, S; Sänger, AM; Wirth, W;
MAGMA. 2017; 30(5):489-503
Originalarbeiten (Zeitschrift)


Culvenor Adam
Dannhauer Torben
Eckstein Felix
Kemnitz Jana
Ruhdorfer Anja
Wirth Wolfgang


To validate a semi-automated method for thigh muscle and adipose tissue cross-sectional area (CSA) segmentation from MRI.
An active shape model (ASM) was trained using 113 MRI CSAs from the Osteoarthritis Initiative (OAI) and combined with an active contour model and thresholding-based post-processing steps. This method was applied to 20 other MRIs from the OAI and to baseline and follow-up MRIs from a 12-week lower-limb strengthening or endurance training intervention (n = 35 females). The agreement of semi-automated vs. previous manual segmentation was assessed using the Dice similarity coefficient and Bland-Altman analyses. Longitudinal changes observed in the training intervention were compared between semi-automated and manual segmentations.
High agreement was observed between manual and semi-automated segmentations for subcutaneous fat, quadriceps and hamstring CSAs. With strength training, both the semi-automated and manual segmentation method detected a significant reduction in adipose tissue CSA and a significant gain in quadriceps, hamstring and adductor CSAs. With endurance training, a significant reduction in adipose tissue CSAs was observed with both methods.
The semi-automated approach showed high agreement with manual segmentation of thigh muscle and adipose tissue CSAs and showed longitudinal training effects similar to that observed using manual segmentation.

Find related publications in this database (Keywords)

Statistical shape model
Thigh muscle
Training intervention
Magnetic resonance imaging