Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography

  • B. Michael Kelm
  • Sushil Mittal
  • Yefeng Zheng
  • Alexey Tsymbal
  • Dominik Bernhardt
  • Fernando Vega-Higuera
  • S. Kevin Zhou
  • Peter Meer
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

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 cross-section 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 approaches.

Keywords

Random Forest Leave Anterior Descend Negative Predictive Value Coronary Compute Tomography Angiography Right Coronary Artery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Achenbach, S.: Cardiac CT: State of the art for the detection of coronary arterial stenosis. J. Cardiovasc. Comput. Tomogr. 1(1), 3–20 (2007)CrossRefGoogle Scholar
  2. 2.
    Achenbach, S., Anders, K., Kalender, W.: Dual-source cardiac computed tomography: Image quality and dose considerations. Eur. Radiol. 18, 1188–1198 (2008)CrossRefGoogle Scholar
  3. 3.
    Anders, K., Petit, I., Achenbach, S., Pflederer, T.: Diagnostic utility of automated stenosis detection in dual source CT coronary angiography as a stand alone or add-on tool. In: Proc. SCCT (2010)Google Scholar
  4. 4.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Gallagher, M.J., Raff, G.L.: Use of multislice CT for the evaluation of emergency room patients with chest pain: the so-called “triple rule-out”. Cath. and Cardiovasc. Interv. 71(1), 92–99 (2008)CrossRefGoogle Scholar
  6. 6.
    Gülsün, M.A., Tek, H.: Robust vessel tree modeling. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 602–611. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Halpern, E.J., Halpern, D.J.: Diagnosis of coronary stenosis with CT angiography comparison of automated computer diagnosis with expert readings. Acad. Radiol. 18, 324–333 (2011)CrossRefGoogle Scholar
  8. 8.
    Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009)CrossRefGoogle Scholar
  9. 9.
    Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal surface segmentation in volumetric images–a graph-theoretic approach. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006)CrossRefGoogle Scholar
  10. 10.
    Metz, C., Schaap, M., van Walsum, T., van der Giessen, A., Weustink, A., Mollet, N., Krestin, G., Niessen, W.: 3D segmentation in the clinic: A grand challenge II – coronary artery tracking. In: MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge (2008)Google Scholar
  11. 11.
    Mittal, S., Zheng, Y., Georgescu, B., Vega-Higuera, F., Zhou, S., Meer, P., Comaniciu, D.: Fast automatic detection of calcified coronary lesions in 3D cardiac CT images. In: Proc. MICCAI Workshop on Machine Learning in Medical Imaging (2010)Google Scholar
  12. 12.
    Pugliese, F., Hunink, M.G.M., Gruszczynska, K., Alberghina, F., Malag, R., van Pelt, N., Mollet, N.R., Cademartiri, F., Weustink, A.C., Meijboom, W.B., Witteman, C.L.M., de Feyter, P.J., Krestin, G.P.: Learning Curve for Coronary CT Angiography: What Constitutes Sufficient Training? Radiol. 251(2), 359–368 (2009)CrossRefGoogle Scholar
  13. 13.
    Rinck, D., Krüger, S., Reimann, A., Scheuering, M.: Shape-based segmentation and visualization techniques for evaluation of atherosclerotic plaques in coronary artery disease. In: Proc. SPIE Int. Soc. Opt. Eng., vol. 6141, pp. 61410G–9 (2006)Google Scholar
  14. 14.
    Teßmann, M., Vega-Higuera, F., Fritz, D.: Learning-based detection of stenotic lesions in coronary CT data. In: Proc. of Vision, Modeling, and Visualization, pp. 189–198 (2008)Google Scholar
  15. 15.
    Wesarg, S., Khan, M.F., Firle, E.: Localizing calcifications in cardiac CT data sets using a new vessel segmentation approach. J. of Dig. Imag. 19(3), 249–257 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • B. Michael Kelm
    • 1
  • Sushil Mittal
    • 2
    • 3
  • Yefeng Zheng
    • 2
  • Alexey Tsymbal
    • 1
  • Dominik Bernhardt
    • 4
  • Fernando Vega-Higuera
    • 4
  • S. Kevin Zhou
    • 2
  • Peter Meer
    • 3
  • Dorin Comaniciu
    • 2
  1. 1.Image Analytics and Informatics, Corporate TechnologySiemens AGErlangenGermany
  2. 2.Image Analytics and InformaticsSiemens Corporate ResearchPrincetonUSA
  3. 3.Electrical and Computer EngineeringRutgers UniversityUSA
  4. 4.Computed Tomography, Healthcare SectorSiemens AGForchheimGermany

Personalised recommendations