Reconstructing vascular networks is a challenging task in medical image processing as automated methods have to deal with large variations in vessel shape and image quality. Recent methods have addressed this problem as constrained maximum a posteriori (MAP) inference in a graphical model, formulated over an overcomplete network graph. Manual control and adjustments are often desired in practice and strongly benefit from indicating the uncertainties in the reconstruction or presenting alternative solutions. In this paper, we examine two different methods to sample vessel network graphs, a perturbation and a Gibbs sampler, and thereby estimate marginals. We quantitatively validate the accuracy of the approximated marginals using true marginals, computed by enumeration.



With the support of the Technische Universität München – Institute for Advanced Study, funded by the German Excellence Initiative (and the European Union Seventh Framework Programme under grant agreement n\(^{\circ }\) 291763).

Supplementary material

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Supplementary material 1 (pdf 97 KB)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Markus Rempfler
    • 1
    • 2
  • Bjoern Andres
    • 3
  • Bjoern H. Menze
    • 1
    • 2
  1. 1.Institute for Advanced StudyTechnical University of MunichMunichGermany
  2. 2.Department of InformaticsTechnical University of MunichMunichGermany
  3. 3.Bosch Center for Artificial Intelligence (BCAI)RenningenGermany

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