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

Journal Article · · Medical Image Analysis
 [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)
+ Show Author Affiliations

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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1245820
Alternate ID(s):
OSTI ID: 1251304
Journal Information:
Medical Image Analysis, Vol. 25, Issue 1; ISSN 1361-8415
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

References (16)

Network motifs: theory and experimental approaches journal June 2007
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Morphological and Pathological Evolution of the Brain Microcirculation in Aging and Alzheimer’s Disease journal May 2012
Vectorization of optically sectioned brain microvasculature: Learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments journal August 2012
A review of vessel extraction techniques and algorithms journal June 2004
Contrast-Enhanced Magnetic Resonance Microangiography Reveals Remodeling of the Cerebral Microvasculature in Transgenic ArcA  Mice journal February 2012
A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes journal December 2009
Integer programming approaches to the travelling salesman problem journal December 1976
Network Motifs: Simple Building Blocks of Complex Networks journal October 2002
The Physiological Principle of Minimum Work: I. The Vascular System and the Cost of Blood Volume journal March 1926
Distance-Ordered Homotopic Thinning: A Skeletonization Algorithm for 3D Digital Images journal December 1998
Segmentation of image ensembles via latent atlases journal October 2010
Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters journal January 2015

Cited By (3)

Machine learning analysis of whole mouse brain vasculature journal March 2020
Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation
  • Balsiger, Fabian; Soom, Yannick; Scheidegger, Olivier
  • Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II, p. 273-281 https://doi.org/10.1007/978-3-030-32245-8_31
book October 2019
Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach journal September 2018

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
97 MATHEMATICS AND COMPUTING
cerebrovascular networks
vessel segmentation
integer programming
vascular network extraction
vessel tracking