Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies

Anders Nymark Christensen, Christian Thode Larsen, Camilla Maria Mandrup Jensen, Martin Bæk Petersen, Rasmus Larsen, Knut Conradsen, Vedrana Andersen Dahl

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


For many clinical studies changes in the abdominal distribution of fat is an important measure. However, the segmentation of abdominal fat in MRI scans is both difficult and time consuming using manual methods. We present here an automatic and flexible software package, that performs both bias field correction and segmentation of the fat into superficial and deep subcutaneous fat as well as visceral fat with the spinal compartment removed. Assessment when comparing to the gold standard - CT-scans - shows a correlation and bias comparable to manual segmentation. The method is flexible by tuning the image-derived energies used for the segmentation, allowing the method to be applied to other body parts, such as the thighs.
Original languageEnglish
Title of host publicationImage Analysis
Publication date2017
ISBN (Print)9783319591285
Publication statusPublished - 2017
Event20th Scandinavian Conference on Image Analysis - Tromsø, Norway
Duration: 12 Jun 201714 Jun 2017


Conference20th Scandinavian Conference on Image Analysis
SeriesLecture Notes in Computer Science


  • Computer Science
  • Image Processing and Computer Vision
  • Pattern Recognition
  • Artificial Intelligence (incl. Robotics)
  • Computer Graphics
  • Data Mining and Knowledge Discovery

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