1. Automatic quantification of subcutaneous and visceral adipose tissue from whole-body magnetic resonance images suitable for large cohort studies.
- Author
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Wald D, Teucher B, Dinkel J, Kaaks R, Delorme S, Boeing H, Seidensaal K, Meinzer HP, and Heimann T
- Subjects
- Cohort Studies, Europe epidemiology, Female, Humans, Male, Middle Aged, Organ Size, Reproducibility of Results, Sensitivity and Specificity, Algorithms, Imaging, Three-Dimensional statistics & numerical data, Intra-Abdominal Fat anatomy & histology, Magnetic Resonance Imaging statistics & numerical data, Pattern Recognition, Automated methods, Subcutaneous Fat anatomy & histology, Whole Body Imaging statistics & numerical data
- Abstract
Purpose: To develop an automated method with which to distinguish metabolically different adipose tissues in a large number of subjects using whole-body magnetic resonance imaging (MRI) datasets for improving the understanding of chronic disease risk predictions associated with distinct adipose tissue compartments., Materials and Methods: In all, 314 participants were scanned using a 1.5T MRI-scanner with a 2-point Dixon whole-body sequence. Image segmentation was automated using standard image processing techniques and knowledge-based methods. Abdominal adipose tissue was separated into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) by statistical shape models. Bone marrow was removed to provide a more accurate measurement of adipose tissue. To assess segmentation accuracy, ground-truth segmentations in 52 images were performed manually by one operator. Due to the high effort of manual delineation, manual segmentation was limited to seven slices per volume., Results: Volumetric differences were 3.30 ± 2.97% and 6.22 ± 5.28% for SAT and VAT, respectively. The systematic error shows an overestimation of 4.22 ± 7.01% for VAT and 0.37 ± 4.45% for SAT. Coefficients-of-variation from repeated measurements were: 3.50 ± 2.93% for VAT and 0.35 ± 0.26% for SAT. The approach of removing bone marrow worked well in most body regions. Only occasionally the method failed for knees and/or shinbone, which resulted in an overestimation of SAT by 3.14 ± 1.45%., Conclusion: We developed a fully automatic process to assess SAT and VAT in whole-body MRI data. The method can support epidemiological studies investigating the relationship between excess body fat and chronic diseases., (Copyright © 2012 Wiley Periodicals, Inc.)
- Published
- 2012
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