Paper Info
Title
Automated quantification of adipose and skeletal muscle tissue in whole-body MRI data for epidemiological studies
Abstract
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.
Year
DOI
Venue
2012
10.1117/12.911290
Proceedings of SPIE
Keywords
Field
DocType
whole-body MRI,segmentation,statistical shape models,subcutaneous adipose tissue,visceral adipose tissue,skeletal muscle tissue,obesity,chronic diseases
Biomedical engineering,Computer vision,Segmentation,Image processing,Image segmentation,Skeletal Muscle Tissue,Artificial intelligence,Adipose tissue,Region growing,Thresholding,Physics,Magnetic resonance imaging
Conference
Volume
ISSN
Citations 
8315
0277-786X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Diana Wald19311.14
Birgit Teucher211.03
Julien Dinkel361.87
Rudolf Kaaks421.44
stefan delorme5225.67
H.P. Meinzer645471.14
Tobias Heimann789352.62