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Markus Rempfler, Matthias Schneider, Giovanna D. Ielacqua, Xianghui Xiao, Stuart R. Stock, Jan Klohs, Gábor Székely, Bjoern Andres, and Bjoern H. Menze. Extracting Vascular Networks under Physiological Constraints via Integer Programming. In Polina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, and Robert Howe, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, volume 8674 of LNCS, pages 506-513. Springer Berlin/Heidelberg, 2014.

We introduce an integer programming-based approach to vessel network extraction that enforces global physiological constraints on the vessel structure and learn this prior from a high-resolution reference network. The method accounts for both image evidence and geometric relationships between vessels by formulating and solving an integer programming problem. Starting from an over-connected network, it is pruning vessel stumps and spurious connections by evaluating bifurcation angle and connectivity of the graph. We utilize a highresolution micro computed tomography (uCT) dataset of a cerebrovascular corrosion cast to obtain a reference network, perform experiments on micro magnetic resonance angiography (uMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.
@InProceedings{Rempfler2014, title = {Extracting Vascular Networks under Physiological Constraints via Integer Programming}, author = {Rempfler, Markus and Schneider, Matthias and Ielacqua, Giovanna D. and Xiao, Xianghui and Stock, Stuart R. and Klohs, Jan and Sz\'{e}kely, G\'{a}bor and Andres, Bjoern and Menze, Bjoern H.}, booktitle = {Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2014}, year = {2014}, editor = {Golland, Polina and Hata, Nobuhiko and Barillot, Christian and Hornegger, Joachim and Howe, Robert}, number = {2}, pages = {506--513}, publisher = {Springer Berlin/Heidelberg}, series = {LNCS}, volume = {8674}, abstract = {We introduce an integer programming-based approach to vessel network extraction that enforces global physiological constraints on the vessel structure and learn this prior from a high-resolution reference network. The method accounts for both image evidence and geometric relationships between vessels by formulating and solving an integer programming problem. Starting from an over-connected network, it is pruning vessel stumps and spurious connections by evaluating bifurcation angle and connectivity of the graph. We utilize a highresolution micro computed tomography (uCT) dataset of a cerebrovascular corrosion cast to obtain a reference network, perform experiments on micro magnetic resonance angiography (uMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.}, doi = {10.1007/978-3-319-10470-6_63}, isbn = {978-3-319-10469-0} }