Precise Lumen Segmentation in Coronary Computed Tomography Angiography

  • Felix Lugauer
  • Yefeng Zheng
  • Joachim Hornegger
  • B. Michael Kelm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8848)

Abstract

Coronary computed tomography angiography (CCTA) allows for non-invasive identification and grading of stenoses by evaluating the degree of narrowing of the blood-filled vessel lumen. Recently, methods have been proposed that simulate coronary blood flow using computational fluid dynamics (CFD) to compute the fractional flow reserve non-invasively. Both grading and CFD rely on a precise segmentation of the vessel lumen from CCTA. We propose a novel, model-guided segmentation approach based on a Markov random field formulation with convex priors which assures the preservation of the tubular structure of the coronary lumen. Allowing for various robust smoothness terms, the approach yields very accurate lumen segmentations even in the presence of calcified and non-calcified plaques. Evaluations on the public Rotterdam segmentation challenge demonstrate the robustness and accuracy of our method: on standardized tests with multi-vendor CCTA from 30 symptomatic patients, we achieve superior accuracies as compared to both state-of-the-art methods and medical experts.

Keywords

CCTA Lumen segmentation Markov random field Tubular surface 

References

  1. 1.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  2. 2.
    Go, A., et al.: Heart disease and stroke statistics-2014 update a report from the american heart association. Circulation 129(3), e28–e292 (2014)CrossRefGoogle Scholar
  3. 3.
    Ishikawa, H.: Exact optimization for Markov random fields with convex priors. IEEE PAMI 25(10), 1333–1336 (2003)CrossRefGoogle Scholar
  4. 4.
    Kirişli, H., Schaap, M., Metz, C., Dharampal, A., Meijboom, W., et al.: Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med. Image Anal. 17(8), 859–876 (2013)CrossRefGoogle Scholar
  5. 5.
    Lesage, D., Angelini, E., 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
  6. 6.
    Li, K., Wu, X., Chen, D., Sonka, L.: Optimal surface segmentation in volumetric images-a graph-theoretic approach. IEEE PAMI 28(1), 119–134 (2006)CrossRefGoogle Scholar
  7. 7.
    Lugauer, F., Zhang, J., Zheng, Y., Hornegger, J., Kelm, B.: Improving accuracy in coronary lumen segmentation via explicit calcium exclusion, learning-based ray detection and surface optimization. In: Proceedings of the SPIE Conference Medical Imaging (2014)Google Scholar
  8. 8.
    Meijs, M., et al.: CT fractional flow reserve: the next level in non-invasive cardiac imaging. Neth. Heart J. 20(10), 410–418 (2012)CrossRefGoogle Scholar
  9. 9.
    Mohr, B., Masood, S., Plakas, C.: Accurate lumen segmentation and stenosis detection and quantification in coronary CTA. In: Proceedings of 3D Cardiovascular Imaging: A MICCAI Segmentation Challenge Workshop (2012)Google Scholar
  10. 10.
    Shahzad, R., Kirişli, H., Metz, C., Tang, H., Schaap, M., van Vliet, L., Niessen, W., van Walsum, T.: Automatic segmentation, detection and quantification of coronary artery stenoses on CTA. Int. J. Cardiovasc. Imaging 29(8), 1847–1859 (2013)CrossRefGoogle Scholar
  11. 11.
    Tu, Z.: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: Tenth IEEE International Conference on Computer Vision, ICCV’05, vol. 2, pp. 1589–1596. IEEE (2005)Google Scholar
  12. 12.
    Wang, C., Moreno, R., Smedby, Ö.: Vessel segmentation using implicit model-guided level sets. In: Proceedings of 3D Cardiovascular Imaging: A MICCAI Segmentation Challenge Workshop (2012)Google Scholar
  13. 13.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imaging 27(11), 1668–1681 (2008)CrossRefGoogle Scholar
  14. 14.
    Zheng, Y., Tek, H., Funka-Lea, G.: Robust and accurate coronary artery centerline extraction in CTA by combining model-driven and data-driven approaches. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 74–81. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Felix Lugauer
    • 1
    • 2
  • Yefeng Zheng
    • 3
  • Joachim Hornegger
    • 1
  • B. Michael Kelm
    • 2
  1. 1.Pattern Recognition LabUniversity of Erlangen-NurembergErlangenGermany
  2. 2.Imaging and Computer Vision, Siemens AG, Corporate TechnologyErlangenGermany
  3. 3.Imaging and Computer Vision, Siemens Corporate ResearchPrincetonUSA

Personalised recommendations