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Proceedings Paper

Fast automatic algorithm for bifurcation detection in vascular CTA scans
Author(s): Matthias Brozio; Vladlena Gorbunova; Christian Godenschwager; Thomas Beck; Dominik Bernhardt
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Paper Abstract

Endovascular imaging aims at identifying vessels and their branches. Automatic vessel segmentation and bifurcation detection eases both clinical research and routine work. In this article a state of the art bifurcation detection algorithm is developed and applied on vascular computed tomography angiography (CTA) scans to mark the common iliac artery and its branches, the internal and external iliacs. In contrast to other methods our algorithm does not rely on a complete segmentation of a vessel in the 3D volume, but evaluates the cross-sections of the vessel slice by slice. Candidates for vessels are obtained by thresholding, following by 2D connected component labeling and prefiltering by size and position. The remaining candidates are connected in a squared distanced weighted graph. With Dijkstra algorithm the graph is traversed to get candidates for the arteries. We use another set of features considering length and shape of the paths to determine the best candidate and detect the bifurcation. The method was tested on 119 datasets acquired with different CT scanners and varying protocols. Both easy to evaluate datasets with high resolution and no apparent clinical diseases and difficult ones with low resolution, major calcifications, stents or poor contrast between the vessel and surrounding tissue were included. The presented results are promising, in 75.7% of the cases the bifurcation was labeled correctly, and in 82.7% the common artery and one of its branches were assigned correctly. The computation time was on average 0.49 s ± 0.28 s, close to human interaction time, which makes the algorithm applicable for time-critical applications.

Paper Details

Date Published: 14 February 2012
PDF: 9 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142U (14 February 2012); doi: 10.1117/12.911329
Show Author Affiliations
Matthias Brozio, Siemens Healthcare (Germany)
Vladlena Gorbunova, Siemens Healthcare (Germany)
Christian Godenschwager, Siemens Healthcare (Germany)
Thomas Beck, Siemens Healthcare (Germany)
Karlsruhe Institute of Technology (Germany)
Dominik Bernhardt, Siemens Healthcare (Germany)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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