title: Computer-aided assessment of regional abdominal fat with food residue removal in CT.
abstract: OBJECTIVE
  Separate quantification of abdominal subcutaneous and visceral fat regions is essential to understand the role of regional adiposity as risk factor in epidemiological studies. Fat quantification is often based on computed tomography (CT) because fat density is distinct from other tissue densities in the abdomen. However, the presence of intestinal food residues with densities similar to fat may reduce fat quantification accuracy. We introduce an abdominal fat quantification method in CT with interest in food residue removal.
  
  
  METHODS
  Total fat was identified in the feature space of Hounsfield units and divided into subcutaneous and visceral components using model-based segmentation. Regions of food residues were identified and removed from visceral fat using a machine learning method integrating intensity, texture, and spatial information. Cost-weighting and bagging techniques were investigated to address class imbalance.
  
  
  RESULTS
  We validated our automated food residue removal technique against semimanual quantifications. Our feature selection experiments indicated that joint intensity and texture features produce the highest classification accuracy at 95%. We explored generalization capability using k-fold cross-validation and receiver operating characteristic (ROC) analysis with variable k. Losses in accuracy and area under ROC curve between maximum and minimum k were limited to 0.1% and 0.3%. We validated tissue segmentation against reference semimanual delineations. The Dice similarity scores were as high as 93.1 for subcutaneous fat and 85.6 for visceral fat.
  
  
  CONCLUSIONS
  Computer-aided regional abdominal fat quantification is a reliable computational tool for large-scale epidemiological studies. Our proposed intestinal food residue reduction scheme is an original contribution of this work. Validation experiments indicate very good accuracy and generalization capability.
authors.affiliation: Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, 3001 South Hanover St., 5th Fl. Baltimore, MD 21225; Department of Mathematical Sciences, Delaware State University, Dover, DE 19901. Electronic address: makrogianniss@mail.nih.gov.
authors.forename: Sokratis
authors.initials: S
authors.lastname: Makrogiannis
authors.forename: Giorgio
authors.initials: G
authors.lastname: Caturegli
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
authors.forename: Luigi
authors.initials: L
authors.lastname: Ferrucci
mesh_terms: D050153:Abdominal Fat / Q000000981:diagnostic imaging*; D000465:Algorithms; D001823:Body Composition; D005502:Food; D006801:Humans; D008137:Longitudinal Studies; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D013382:Subtraction Technique; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Body composition assessment; false positive reduction