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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. Reconstructing cerebrovascular networks under local physiological constraints by integer programming. Medical Image Analysis, 25(1):86-94, 2015. Special Issue on the 2014 Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014).

Medical Image Analysis-MICCAI'15 Best Paper Award

We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to a probabilistic model. Starting from an overconnected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (uCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of our probabilistic model and we perform experiments on in-vivo magnetic resonance microangiography (uMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.
@Article{Rempfler2015a, title = {Reconstructing cerebrovascular networks under local physiological constraints by integer programming}, author = {Markus Rempfler and Matthias Schneider and Giovanna D. Ielacqua and Xianghui Xiao and Stuart R. Stock and Jan Klohs and G\'{a}bor Sz\'{e}kely and Bjoern Andres and Bjoern H. Menze}, journal = {Medical Image Analysis}, year = {2015}, note = {Special Issue on the 2014 Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014).}, number = {1}, pages = {86--94}, volume = {25}, abstract = {We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to a probabilistic model. Starting from an overconnected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (uCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of our probabilistic model and we perform experiments on in-vivo magnetic resonance microangiography (uMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.}, doi = {10.1016/j.media.2015.03.008}, issn = {1361-8415}, keywords = {vascular network extraction, vessel segmentation, vessel tracking, cerebrovascular networks, integer programming, structured prediction}, url = {http://www.sciencedirect.com/science/article/pii/S1361841515000420} }