Drs. Joshi and Hu contributed equally to this work.
Automatic intra-subject registration-based segmentation of abdominal fat from water–fat MRI
Article first published online: 25 SEP 2012
Copyright © 2012 Wiley Periodicals, Inc.
Journal of Magnetic Resonance Imaging
Volume 37, Issue 2, pages 423–430, February 2013
How to Cite
Joshi, A. A., Hu, H. H., Leahy, R. M., Goran, M. I. and Nayak, K. S. (2013), Automatic intra-subject registration-based segmentation of abdominal fat from water–fat MRI. J. Magn. Reson. Imaging, 37: 423–430. doi: 10.1002/jmri.23813
- Issue published online: 24 JAN 2013
- Article first published online: 25 SEP 2012
- Manuscript Accepted: 7 AUG 2012
- Manuscript Received: 2 JUN 2010
- NIH. Grant Numbers: R21-DK081173, ; Contract grant number: K25-DK087931
- adipose tissue;
- fat volume;
- fat fraction;
- fat quantification
To develop an automatic registration-based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from three-dimensional (3D) water–fat MRI data, and to evaluate its performance against manual segmentation.
Materials and Methods:
Data were obtained from 11 subjects at two time points with intermediate repositioning, and from four subjects before and after a meal with repositioning. Imaging was performed on a 3 Tesla MRI, using the IDEAL chemical-shift water–fat pulse sequence. Adipose tissue (subcutaneous—SAT, visceral—VAT) and organs (liver, pancreas) were manually segmented twice for each scan by a single trained observer. Automated segmentations of each subject's second scan were generated using a nonrigid volume registration algorithm for water–fat MRI images that used a b-spline basis for deformation and minimized image dissimilarity after the deformation. Manual and automated segmentations were compared using Dice coefficients and linear regression of SAT and VAT volumes, organ volumes, and hepatic and pancreatic fat fractions (HFF, PFF).
Manual segmentations from the 11 repositioned subjects exhibited strong repeatability and set performance benchmarks. The average Dice coefficients were 0.9747 (SAT), 0.9424 (VAT), 0.9404 (liver), and 0.8205 (pancreas); the linear correlation coefficients were 0.9994 (SAT volume), 0.9974 (VAT volume), 0.9885 (liver volume), 0.9782 (pancreas volume), 0.9996 (HFF), and 0.9660 (PFF). When comparing manual and automated segmentations, the average Dice coefficients were 0.9043 (SAT volume), 0.8235 (VAT), 0.8942 (liver), and 0.7168 (pancreas); the linear correlation coefficients were 0.9493 (SAT volume), 0.9982 (VAT volume), 0.9326 (liver volume), 0.8876 (pancreas volume), 0.9972 (HFF), and 0.8617 (PFF). In the four pre- and post-prandial subjects, the Dice coefficients were 0.9024 (SAT), 0.7781 (VAT), 0.8799 (liver), and 0.5179 (pancreas); the linear correlation coefficients were 0.9889, 0.9902 (SAT, and VAT volume), 0.9523 (liver volume), 0.8760 (pancreas volume), 0.9991 (HFF), and 0.6338 (PFF).
Automated intra-subject registration-based segmentation is potentially suitable for the quantification of abdominal and organ fat and achieves comparable quantitative endpoints with respect to manual segmentation. J. Magn. Reson. Imaging 2013;37:423–430. © 2012 Wiley Periodicals, Inc.