Detection, Grading and Classification of Coronary
Stenoses in Computed Tomography Angiography.
B. Michael Kelm(1), Sushil Mittal(2;3), Yefeng Zheng(2),
Alexey Tsymbal(1), Dominik Bernhardt(4), Fernando Vega-Higuera(4),
Shaohua Kevin Zhou(2), Peter Meer(3), Dorin Comaniciu(2)
(1) Corporate Technology, Siemens AG, Erlangen, Germany
(2) Siemens Corporate Research, Princeton, NJ 08540, USA
(3)Department of Electrical and Computer Engineering.
Rutgers University, Piscataway, NJ 08854, USA
(4) Healthcare Sector, Siemens AG, Forchheim, Germany
Recently conducted clinical studies prove the utility of Coronary
Computed Tomography Angiography (CCTA) as a viable alternative to
invasive angiography for the detection of Coronary Artery Disease (CAD).
This has lead to the development of several algorithms for automatic
detection and grading of coronary stenoses. However, most of these
methods focus on detecting calcified plaques only. A few methods that
can also detect and grade non-calcified plaques require substantial user
involvement. In this paper, we propose a fast and fully automatic system
that is capable of detecting, grading and classifying coronary
stenoses in CCTA caused by all types of plaques. We propose a
four-step approach including a learning-based centerline verification
step and a lumen crosssection estimation step using random regression
forests.We show state-of-the-art performance of our method in
experiments conducted on a set of 229 CCTA volumes.
With an average processing time of 1.8 seconds per case after
centerline extraction, our method is significantly faster than competing
MICCAI 2011, the 14th International Conference on Medical
Image Computing and Computer Assisted Intervention,
Toronto, Canada, September 2011, vol. 6893, 25-32.