title: Clinical pilot study for the automatic segmentation and recognition of abdominal adipose tissue compartments from MRI data.
abstract: OBJECTIVE
  In the diagnosis and risk assessment of obesity, both the amount and distribution of adipose tissue compartments are critical factors. We present a hybrid method for the quantitative measurement of human body fat compartments.
  
  
  METHODS
  MRI imaging was performed on a 1.5 T scanner. In a pre-processing step, the images were corrected for bias field inhomogeneity. For segmentation and recognition a hybrid algorithm was developed to automatically differentiate between different adipose tissue compartments. The presented algorithm is designed with a combination of shape and intensity-based techniques. To incorporate the presented algorithm into the clinical routine, we developed a graphical user interface. Results from our methods were compared with the known volume of an adipose tissue phantom. To evaluate our method, we analyzed 40 clinical MRI scans of the abdominal region.
  
  
  RESULTS
  Relatively low segmentation errors were found for subcutaneous adipose tissue (3.56 %) and visceral adipose tissue (0.29 %) in phantom studies. The clinical results indicated high correlations between the distribution of adipose tissue compartments and obesity.
  
  
  CONCLUSIONS
  We present an approach that rapidly identifies and quantifies adipose tissue depots of interest. With this method examination and analysis can be performed in a clinically feasible timeframe.
authors.affiliation: Institut für Radiologie, Klinikum rechts der Isar, Technische Universität München. peter.noel@tum.de
authors.forename: P B
authors.initials: PB
authors.lastname: Noël
authors.forename: J S
authors.initials: JS
authors.lastname: Bauer
authors.forename: C
authors.initials: C
authors.lastname: Ganter
authors.forename: C
authors.initials: C
authors.lastname: Markus
authors.forename: E J
authors.initials: EJ
authors.lastname: Rummeny
authors.forename: H
authors.initials: H
authors.lastname: Hauner
authors.forename: H P
authors.initials: HP
authors.lastname: Engels
mesh_terms: D050153:Abdominal Fat / Q000473:pathology*; D000465:Algorithms; D016477:Artifacts; D050218:Body Fat Distribution; D015992:Body Mass Index; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D056128:Obesity, Abdominal / Q000175:diagnosis*; D009767:Obesity, Morbid / Q000175:diagnosis; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D012984:Software; D013223:Statistics as Topic; D014584:User-Computer Interface