TY - GEN
T1 - Uncertainty estimation in vascular networks
AU - Rempfler, Markus
AU - Andres, Bjoern
AU - Menze, Bjoern H.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85029754729
U2 - 10.1007/978-3-319-67675-3_5
DO - 10.1007/978-3-319-67675-3_5
M3 - Conference contribution
AN - SCOPUS:85029754729
SN - 9783319676746
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 42
EP - 52
BT - Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings
A2 - Cardoso, M. Jorge
A2 - Arbel, Tal
PB - Springer Verlag
T2 - 1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 10 September 2017 through 14 September 2017
ER -