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23 February 2012Automated quantification of adipose and skeletal muscle tissue in whole-body MRI data for epidemiological studies
The ratio between the amount of adipose and skeletal muscle tissue is an important determinant of metabolic
health. Recent developments in MRI technology allow whole body scans to be performed for accurate assessment
of body composition. In the present study, a total of 194 participants underwent a 2-point Dixon MRI sequence
of the whole body. A fully automated image segmentation method quantifies the amount of adipose and skeletal
muscle tissue by applying standard image processing techniques including thresholding, region growing and
morphological operators. The adipose tissue is further divided into subcutaneous and visceral adipose tissue by
using statistical shape models. All images were visually inspected. The quantitative analysis was performed
on 44 whole-body MRI data using manual segmentations as ground truth data. We achieved 3.3% and 6.3%
of relative volume difference between the manual and automated segmentation of subcutaneous and visceral
adipose tissue, respectively. The validation of skeletal muscle tissue segmentation resulted in a relative volume
difference of 7.8 ± 4.2% and a volumetric overlap error of 6.4 ± 2.3 %. To our knowledge, we are first to present
a fully automated method which quantifies adipose and skeletal muscle tissue in whole-body MRI data. Due to
the fully automated approach, results are deterministic and free of user bias. Hence, the software can be used in
large epidemiological studies for assessing body fat distribution and the ratio of adipose to skeletal muscle tissue
in relation to metabolic disease risk.
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Diana Wald, Birgit Teucher, Julien Dinkel, Rudolf Kaaks, Stefan Delorme, Hans-Peter Meinzer, Tobias Heimann, "Automated quantification of adipose and skeletal muscle tissue in whole-body MRI data for epidemiological studies," Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831519 (23 February 2012); https://doi.org/10.1117/12.911290