title: Reconstructing cerebrovascular networks under local physiological constraints by integer programming.
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 (μCT) 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 (μMRA) images of mouse brains. We finally discuss properties of the networks obtained under different tracking and pruning approaches.
authors.affiliation: Department of Computer Science, Technische Universität München, Germany; Computer Vision Laboratory, ETH Zürich, Switzerland. Electronic address: markurem@vision.ee.ethz.ch.
authors.forename: Markus
authors.initials: M
authors.lastname: Rempfler
authors.affiliation: Computer Vision Laboratory, ETH Zürich, Switzerland; Institute of Pharmacology and Toxicology, University of Zürich, Switzerland.
authors.forename: Matthias
authors.initials: M
authors.lastname: Schneider
authors.affiliation: Institute for Biomedical Engineering, University and ETH Zürich, Switzerland.
authors.forename: Giovanna D
authors.initials: GD
authors.lastname: Ielacqua
authors.affiliation: Advanced Photon Source, Argonne National Laboratory, Lemont, IL, USA.
authors.forename: Xianghui
authors.initials: X
authors.lastname: Xiao
authors.affiliation: Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: Stuart R
authors.initials: SR
authors.lastname: Stock
authors.affiliation: Institute for Biomedical Engineering, University and ETH Zürich, Switzerland.
authors.forename: Jan
authors.initials: J
authors.lastname: Klohs
authors.affiliation: Computer Vision Laboratory, ETH Zürich, Switzerland.
authors.forename: Gábor
authors.initials: G
authors.lastname: Székely
authors.affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany.
authors.forename: Bjoern
authors.initials: B
authors.lastname: Andres
authors.affiliation: Department of Computer Science, Technische Universität München, Germany; Institute for Advanced Studies, Technische Universität München, Germany.
authors.forename: Bjoern H
authors.initials: BH
authors.lastname: Menze
mesh_terms: D000465:Algorithms; D000818:Animals; D002533:Cerebral Angiography / Q000379:methods*; D002536:Cerebral Arteries / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D051379:Mice; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055114:X-Ray Microtomography / Q000379:methods*
keywords: Cerebrovascular networks; Integer programming; Vascular network extraction; Vessel segmentation; Vessel tracking