TY - GEN
T1 - Extracting vascular networks under physiological constraints via integer programming
AU - Rempfler, Markus
AU - Schneider, Matthias
AU - Ielacqua, Giovanna D.
AU - Xiao, Xianghui
AU - Stock, Stuart R.
AU - Klohs, Jan
AU - Székely, Gábor
AU - Andres, Bjoern
AU - Menze, Bjoern H.
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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 high-resolution micro computed tomography (μCT) dataset of a cerebrovascular corrosion cast to obtain a reference network, perform experiments on micro magnetic resonance angiography(μMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.
AB - 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 high-resolution micro computed tomography (μCT) dataset of a cerebrovascular corrosion cast to obtain a reference network, perform experiments on micro magnetic resonance angiography(μMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.
UR - http://www.scopus.com/inward/record.url?scp=84906987270&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-10470-6_63
DO - 10.1007/978-3-319-10470-6_63
M3 - Conference contribution
C2 - 25485417
AN - SCOPUS:84906987270
SN - 9783319104690
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 505
EP - 512
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PB - Springer Verlag
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Y2 - 14 September 2014 through 18 September 2014
ER -