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

  • Anders Nymark Christensen
  • Christian Thode Larsen
  • Camilla Maria Mandrup
  • Martin Bæk Petersen
  • Rasmus Larsen
  • Knut Conradsen
  • Vedrana Andersen Dahl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10270)


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.


Automatic Segmentation Manual Segmentation Bias Field Surface Cost Deep Compartment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anders Nymark Christensen
    • 1
  • Christian Thode Larsen
    • 1
  • Camilla Maria Mandrup
    • 2
  • Martin Bæk Petersen
    • 2
  • Rasmus Larsen
    • 1
  • Knut Conradsen
    • 1
  • Vedrana Andersen Dahl
    • 1
  1. 1.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
  2. 2.Section of Systems Biology ResearchCopenhagen UniversityCopenhagenDenmark

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