Uncertainty Estimation in Vascular Networks

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.