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 the probabilistic model. Starting from an over-connected 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. As a result, we 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.
- Authors:
-
- Technische Univ. Munchen (Germany); ETH Zurich (Switzerland)
- ETH Zurich (Switzerland); Univ. of Zurich (Switzerland)
- Univ. and ETH Zurich (Switzerland)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Northwestern Univ., Chicago, IL (United States)
- Computer Vision Lab., ETH Zurich (Switzerland)
- Max Planck Institute for Informatics, Saarbrucken (Germany)
- Technische Univ. Munchen (Germany)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1245820
- Alternate Identifier(s):
- OSTI ID: 1251304
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Medical Image Analysis
- Additional Journal Information:
- Journal Volume: 25; Journal Issue: 1; Journal ID: ISSN 1361-8415
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; cerebrovascular networks; vessel segmentation; integer programming; vascular network extraction; vessel tracking
Citation Formats
Rempfler, Markus, Schneider, Matthias, Ielacqua, Giovanna D., Xiao, Xianghui, Stock, Stuart R., Klohs, Jan, Szekely, Gabor, Andres, Bjoern, and Menze, Bjoern H. Reconstructing cerebrovascular networks under local physiological constraints by integer programming. United States: N. p., 2015.
Web. doi:10.1016/j.media.2015.03.008.
Rempfler, Markus, Schneider, Matthias, Ielacqua, Giovanna D., Xiao, Xianghui, Stock, Stuart R., Klohs, Jan, Szekely, Gabor, Andres, Bjoern, & Menze, Bjoern H. Reconstructing cerebrovascular networks under local physiological constraints by integer programming. United States. https://doi.org/10.1016/j.media.2015.03.008
Rempfler, Markus, Schneider, Matthias, Ielacqua, Giovanna D., Xiao, Xianghui, Stock, Stuart R., Klohs, Jan, Szekely, Gabor, Andres, Bjoern, and Menze, Bjoern H. Thu .
"Reconstructing cerebrovascular networks under local physiological constraints by integer programming". United States. https://doi.org/10.1016/j.media.2015.03.008. https://www.osti.gov/servlets/purl/1245820.
@article{osti_1245820,
title = {Reconstructing cerebrovascular networks under local physiological constraints by integer programming},
author = {Rempfler, Markus and Schneider, Matthias and Ielacqua, Giovanna D. and Xiao, Xianghui and Stock, Stuart R. and Klohs, Jan and Szekely, Gabor and Andres, Bjoern and Menze, Bjoern H.},
abstractNote = {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 the probabilistic model. Starting from an over-connected 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. As a result, we 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.},
doi = {10.1016/j.media.2015.03.008},
journal = {Medical Image Analysis},
number = 1,
volume = 25,
place = {United States},
year = {Thu Apr 23 00:00:00 EDT 2015},
month = {Thu Apr 23 00:00:00 EDT 2015}
}
Web of Science
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