Data-Dependent Higher-Order Clique Selection for Artery–Vein Segmentation by Energy Minimization

Yoshiro Kitamura*, Yuanzhong Li, Wataru Ito, Hiroshi Ishikawa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


We propose a novel segmentation method based on energy minimization of higher-order potentials. We introduce higher-order terms into the energy to incorporate prior knowledge on the shape of the segments. The terms encourage certain sets of pixels to be entirely in one segment or the other. The sets can for instance be smooth curves in order to help delineate pulmonary vessels, which are known to run in almost straight lines. The higher-order terms can be converted to submodular first-order terms by adding auxiliary variables, which can then be globally minimized using graph cuts. We also determine the weight of these terms, or the degree of the aforementioned encouragement, in a principled way by learning from training data with the ground truth. We demonstrate the effectiveness of the method in a real-world application in fully-automatic pulmonary artery–vein segmentation in CT images.

Original languageEnglish
Pages (from-to)142-158
Number of pages17
JournalInternational Journal of Computer Vision
Issue number2
Publication statusPublished - 2016 Apr 1


  • Artery–vein segmentation
  • Higher-order energy
  • Segmentation
  • Surgery simulation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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