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Title: Reconstructing cerebrovascular networks under local physiological constraints by integer programming

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.
 [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:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Medical Image Analysis
Additional Journal Information:
Journal Volume: 25; Journal Issue: 1; Journal ID: ISSN 1361-8415
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING cerebrovascular networks; vessel segmentation; integer programming; vascular network extraction; vessel tracking