Advertisement

A Composite of Features for Learning-Based Coronary Artery Segmentation on Cardiac CT Angiography

  • Yanling ChiEmail author
  • Weimin Huang
  • Jiayin Zhou
  • Liang Zhong
  • Swee Yaw Tan
  • Keng Yung Jih Felix
  • Low Choon Seng Sheon
  • Ru San Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

Coronary artery segmentation is important in quantitative coronary angiography. In this work, a novel method is proposed for coronary artery segmentation. It integrates coronary artery features of density, local shape and global structure into the learning framework. The density feature is the vessel’s relative density estimated by means of Gaussian mixture models and is able to suppress individual variances. The local tube shape of the vessel is measured with the advantages of the 3-dimensional multi-scale Hessian filter and is able to enhance the small vessels. The global structure feature is predicted from a support vector regression in terms of vessel’s spatial position and emphasizes the geometric morphometric attribute of the coronary artery tree running across the surface of the heart. The features are fed into a support vector classifier for vessel segmentation. The proposed methodology was tested on ten 3D cardiac computed tomography angiography datasets. It obtained a sensitivity of 81%, a specificity of 99%, and Dice coefficient of 84%. The performance is good.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kitslaar, P., Frenay, M., Oost, E., Dijkstra, J., Stoel, B., Reiber, J.: Connected component and morphology based extraction of arterial centerlines of the heart (CocomoBeach). In: MICCAI Workshop S4 (2008)Google Scholar
  2. 2.
    Wang, C., Smedby, O.: An automatic seeding method for coronary artery segmentation and skeletonization in CTA. In: MICCAI Workshop S4 (2008)Google Scholar
  3. 3.
    Li, Z., Zhang, Y., Liu, G., Shao, H., Li, W.: A robust coronary artery identification and centerline extraction method in angiographies. Biomed. Signal Proce. 16, 1–8 (2015)CrossRefGoogle Scholar
  4. 4.
    Zhou, C., Chan, H., Chughtai, C., Patel, S., Hadijiiski, L., Wei, J., Kazerooni, E.: Automated coronary artery tree extraction in coronary CT angiography using a multi-scale enhancement and dynamic balloon tracking (MSCAR-DBT) method. Comput. Med. Imag. Grap. 36, 1–10 (2012)CrossRefGoogle Scholar
  5. 5.
    Yang, G., Kitslaar, P., Frenay, M., Broersen, A., Boogers, M., Bax, J., Reiber, J., Dijkstra, J.: Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography. Int. J. Card. Imaging 28(4), 921–933 (2012)CrossRefGoogle Scholar
  6. 6.
    Zambal, S., Hladuvka, J., Kanitsar, A., Buhler, K.: Shape and appearance models for automatic coronary artery tracking. In: MICCAI Workshop S4 (2008)Google Scholar
  7. 7.
    Schaap, M., Walum, T., Neefjes, L., Metz, C., Capuano, E., Bruijne, M., Niessen, W.: Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA. IEEE Trans. Med. Imaging 30(11), 1974–1986 (2011)CrossRefGoogle Scholar
  8. 8.
    Wong, W., So, R., Chung, A.: Principal curves for lumen center extraction and flow channel width estimation in 3-D arterial networks: theory, algorithm, and validation. IEEE Trans. Image Process. 21(4), 1847–1862 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Schneider, M., Hirsch, S., Weber, B., Szekely, G., Menze, B.: Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters. Med Image Anal 19, 220–249 (2015)CrossRefGoogle Scholar
  10. 10.
    Friman, O., Hindennach, M., Kuhnel, C., Peitgen, H.: Multiple hypothesis template tracking of small 3D vessel structures. Med. Image Anal. 14, 160–171 (2010)CrossRefGoogle Scholar
  11. 11.
    Kitamura Y., Li Y., and Ito W.: Automatic coronary extraction by supervised detection and shape matching. In: Proc. of ISBI, pp. 234-237 (2012)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Bishop, C.: Pattern Recognition and Machine Learning, pp. 78–124. Springer Science Business Media, Heidelberg (2006)zbMATHGoogle Scholar
  14. 14.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum-likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Ser. B. 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. Image Anal. 2(2), 143–168 (1998)CrossRefGoogle Scholar
  16. 16.
    Dodge, J., Brown, B., Bolson, E., Dodge, H.: Lumen diameter of normal human coronary arteries. Influence of age, sex, anatomic variation, and left ventricular hypertrophy or dilation. Circulation 86, 232–246 (1992)CrossRefGoogle Scholar
  17. 17.
    Smola, A., Scholkopf, B.: A tutorial on support vector regression. Stat. Comp. 14, 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Joachims T.: Estimating the generalization performance of an SVM efficiently. In: Proc. ICML, pp. 431-438 (2000)Google Scholar
  19. 19.
    Mao, S., Ahmadi, N., Shah, B., Beckmann, D., Chen, A., Ngo, L., Flores, F., Gao, Y., Budoff, M.: Normal thoracic aorta diameter on cardiac computed tomography in healthy asymptomatic adult; impact of age and gender. Acad Radiol 15(7), 827–834 (2008)CrossRefGoogle Scholar
  20. 20.
    Hazel, R., Pollack, S., Reichek, N.: Investigation of the relationship between age and the angle of aortic insertion on the left ventricle using 3D MRI. J. Cardiov. Magn. Resonance 14, 77–78 (2012)CrossRefGoogle Scholar
  21. 21.
    Schaap, M., Metz, C., Walsum, T., Giessen, A., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med. Image Anal. 13, 701–714 (2009)CrossRefGoogle Scholar
  22. 22.
    Lee, T., Kashyap, R., Chu, C.: Building skeleton models via 3-D medical surface/axis thinning algorithms. Graph. Model and Im. Proc. 56(6), 462–478 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Yanling Chi
    • 1
    Email author
  • Weimin Huang
    • 1
  • Jiayin Zhou
    • 1
  • Liang Zhong
    • 2
  • Swee Yaw Tan
    • 2
  • Keng Yung Jih Felix
    • 2
  • Low Choon Seng Sheon
    • 3
  • Ru San Tan
    • 2
  1. 1.Institute for Infocomm Research, A*STARSingaporeSingapore
  2. 2.National Heart Center SingaporeSingaporeSingapore
  3. 3.Department of Diagnostic RadiologySingapore General HospitalSingaporeSingapore

Personalised recommendations