title: Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI.
abstract: OBJECTIVE
  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.
  
  
  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).
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089-2564, USA. ajoshi@sipi.usc.edu
authors.forename: Anand A
authors.initials: AA
authors.lastname: Joshi
authors.forename: Houchun H
authors.initials: HH
authors.lastname: Hu
authors.forename: Richard M
authors.initials: RM
authors.lastname: Leahy
authors.forename: Michael I
authors.initials: MI
authors.lastname: Goran
authors.forename: Krishna S
authors.initials: KS
authors.lastname: Nayak
mesh_terms: D050153:Abdominal Fat / Q000033:anatomy & histology*; D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D019518:Postprandial Period; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014867:Water / Q000032:analysis*
chemical_list: D014867:Water