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Proceedings Paper

Statistical modeling of the arterial vascular tree
Author(s): Thomas Beck; Christian Godenschwager; Miriam Bauer; Dominik Bernhardt; Rüdiger Dillmann
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Paper Abstract

Automatic examination of medical images becomes increasingly important due to the rising amount of data. Therefore automated methods are required which combine anatomical knowledge and robust segmentation to examine the structure of interest. We propose a statistical model of the vascular tree based on vascular landmarks and unbranched vessel sections. An undirected graph provides anatomical topology, semantics, existing landmarks and attached vessel sections. The atlas was built using semi-automatically generated geometric models of various body regions ranging from carotid arteries to the lower legs. Geometric models contain vessel centerlines as well as orthogonal cross-sections in equidistant intervals with the vessel contour having the form of a polygon path. The geometric vascular model is supplemented by anatomical landmarks which are not necessarily related to the vascular system. These anatomical landmarks define point correspondences which are used for registration with a Thin-Plate-Spline interpolation. After the registration process, the models were merged to form the statistical model which can be mapped to unseen images based on a subset of anatomical landmarks. This approach provides probability distributions for the location of landmarks, vessel-specific geometric properties including shape, expected radii and branching points and vascular topology. The applications of this statistical model include model-based extraction of the vascular tree which greatly benefits from vessel-specific geometry description and variation ranges. Furthermore, the statistical model can be applied as a basis for computer aided diagnosis systems as indicator for pathologically deformed vessels and the interaction with the geometric model is significantly more user friendly for physicians through anatomical names.

Paper Details

Date Published: 15 March 2011
PDF: 6 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796249 (15 March 2011); doi: 10.1117/12.878175
Show Author Affiliations
Thomas Beck, Karlsruher Institut für Technologie (Germany)
Siemens AG (Germany)
Christian Godenschwager, Karlsruher Institut für Technologie (Germany)
Siemens AG (Germany)
Miriam Bauer, Karlsruher Institut für Technologie (Germany)
Siemens AG (Germany)
Dominik Bernhardt, Siemens AG (Germany)
Rüdiger Dillmann, Karlsruher Institut für Technologie (Germany)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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