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

Springer Nature,

Volume 10270, 2017

DOI:10.1007/978-3-319-59129-2_10, Dimensions: pub.1086113610,



  1. (1) Technical University of Denmark, grid.5170.3, DTU
  2. (2) University of Copenhagen, grid.5254.6, KU






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.

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Technical University of Denmark

University of Copenhagen

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2017: Unused

Research area: Science & Technology

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2017: Level 1

Research area: Science & Technology

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Times Cited: 2

Field Citation Ratio (FCR): 1.1