Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle

J Magn Reson Imaging. 2013 Apr;37(4):917-27. doi: 10.1002/jmri.23884. Epub 2012 Oct 23.


Purpose: To introduce and validate an automated unsupervised multi-parametric method for segmentation of the subcutaneous fat and muscle regions to determine subcutaneous adipose tissue (SAT) and intermuscular adipose tissue (IMAT) areas based on data from a quantitative chemical shift-based water-fat separation approach.

Materials and methods: Unsupervised standard k-means clustering was used to define sets of similar features (k = 2) within the whole multi-modal image after the water-fat separation. The automated image processing chain was composed of three primary stages: tissue, muscle, and bone region segmentation. The algorithm was applied on calf and thigh datasets to compute SAT and IMAT areas and was compared with a manual segmentation.

Results: The IMAT area using the automatic segmentation had excellent agreement with the IMAT area using the manual segmentation for all the cases in the thigh (R(2): 0.96) and for cases with up to moderate IMAT area in the calf (R(2): 0.92). The group with the highest grade of muscle fat infiltration in the calf had the highest error in the inner SAT contour calculation.

Conclusion: The proposed multi-parametric segmentation approach combined with quantitative water-fat imaging provides an accurate and reliable method for an automated calculation of the SAT and IMAT areas reducing considerably the total postprocessing time.

Publication types

  • Research Support, N.I.H., Extramural
  • Validation Study

MeSH terms

  • Adipose Tissue / pathology*
  • Aged
  • Algorithms*
  • Body Fat Distribution / methods*
  • Body Mass Index
  • Cluster Analysis
  • Diabetes Mellitus, Type 2 / diagnosis
  • Diabetes Mellitus, Type 2 / pathology*
  • Female
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Middle Aged
  • Muscle, Skeletal / pathology*
  • Pattern Recognition, Automated*
  • Predictive Value of Tests
  • Reference Values
  • Subcutaneous Fat / pathology*