title: Automated and reproducible segmentation of visceral and subcutaneous adipose tissue from abdominal MRI.
abstract: OBJECTIVE
  (1) To develop a fully automated algorithm for segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), excluding intermuscular adipose tissue (IMAT) and bone marrow (BM), from axial abdominal magnetic resonance imaging (MRI) data. (2) To evaluate the algorithm accuracy and total method reproducibility using a semi-automatically segmented reference and data from repeated measurements.
  
  
  BACKGROUND
  MRI is a widely used in adipose tissue (AT) assessment. Manual analysis of MRI data is time consuming and biased by the operator. Automated analysis spares resources and increase reproducibility. Fully automated algorithms have been presented. However, reproducibility analysis has not been performed nor has methods for exclusion of IMAT and BM been presented.
  
  
  METHODS
  In total, 49 data sets from 31 subjects were acquired using a clinical 1.5 T MRI scanner. Thirteen data sets were used in the derivation of the automated algorithm and 36 were used in the validation. Common image analysis tools such as thresholding, morphological operations and geometrical models were used to segment VAT and SAT. Accuracy was assessed using a semi-automatically created reference. Reproducibility was assessed from repeated measurements.
  
  
  RESULTS
  Resulting AT volumes from the automated analysis and the reference were not found to differ significantly (2.0+/-14% and 0.84+/-2.7%, given as mean+/-s.d., for VAT and SAT, respectively). The automated analysis of the repeated measurements data significantly increased the reproducibility of the VAT measurements. One athletic subject with very small amounts of AT was considered to be an outlier.
  
  
  CONCLUSIONS
  An automated method for segmentation of VAT and SAT and exclusion of IMAT and BM from abdominal MRI data has been reported. The accuracy and reproducibility of the method has also been demonstrated using a semi-automatically segmented reference and analysis of repeated acquisitions. The accuracy of the method is limited in lean subjects.
authors.affiliation: Department of Oncology, Radiology and Clinical Immunology, Uppsala University Hospital, Uppsala, Sweden. joel.kullberg@radiol.uu.se
authors.forename: J
authors.initials: J
authors.lastname: Kullberg
authors.forename: H
authors.initials: H
authors.lastname: Ahlström
authors.forename: L
authors.initials: L
authors.lastname: Johansson
authors.forename: H
authors.initials: H
authors.lastname: Frimmel
mesh_terms: D000368:Aged; D050218:Body Fat Distribution; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000592:standards; D050152:Intra-Abdominal Fat / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D050151:Subcutaneous Fat / Q000033:anatomy & histology*