title: Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA. alexander.valentinitsch@meduniwien.ac.at
authors.forename: Alexander
authors.initials: A
authors.lastname: Valentinitsch
authors.forename: Dimitrios C
authors.initials: DC
authors.lastname: Karampinos
authors.forename: Hamza
authors.initials: H
authors.lastname: Alizai
authors.forename: Karupppasamy
authors.initials: K
authors.lastname: Subburaj
authors.forename: Deepak
authors.initials: D
authors.lastname: Kumar
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Link
authors.forename: Sharmila
authors.initials: S
authors.lastname: Majumdar
mesh_terms: D000273:Adipose Tissue / Q000473:pathology*; D000368:Aged; D000465:Algorithms; D050218:Body Fat Distribution / Q000379:methods*; D015992:Body Mass Index; D016000:Cluster Analysis; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000473:pathology*; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D012016:Reference Values; D050151:Subcutaneous Fat / Q000473:pathology*