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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 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:
 [1];  [2];  [3];  [4];  [5];  [3];  [6];  [7];  [8]
  1. Technische Univ. Munchen (Germany); ETH Zurich (Switzerland)
  2. ETH Zurich (Switzerland); Univ. of Zurich (Switzerland)
  3. Univ. and ETH Zurich (Switzerland)
  4. Argonne National Lab. (ANL), Argonne, IL (United States)
  5. Northwestern Univ., Chicago, IL (United States)
  6. Computer Vision Lab., ETH Zurich (Switzerland)
  7. Max Planck Institute for Informatics, Saarbrucken (Germany)
  8. 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}
}

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Cited by: 15 works
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Works referencing / citing this record:

Machine learning analysis of whole mouse brain vasculature
journal, March 2020

  • Todorov, Mihail Ivilinov; Paetzold, Johannes Christian; Schoppe, Oliver
  • Nature Methods, Vol. 17, Issue 4
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Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
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Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach
journal, September 2018