Robustness Analysis of Coronary Arteries Segmentation

  • Roman PryamonosovEmail author
  • Alexander Danilov
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 133)


Segmentation of medical scans is the first and fundamental stage of numerical modeling of the human cardiovascular system. In this chapter, we analyze the results of coronary arteries segmentation using our approach for ten contrast-enhanced Computer Tomography Angiography datasets with different image quality and contrast phases. The segmentation is also affected by the patient anatomy, the shape and the scope of images. Our results show that the contrast phase timing is crucial for successful automatic segmentation. These factors form restrictions on the input data for automatic segmentation algorithms. Nevertheless, user guidance such as manual seeding and setting of thresholds can be used to significantly improve segmentation results and weaken the input restrictions.


Image segmentation Coronary arteries Contrast enhanced Computed tomography Cardiovascular applications Personalized medicine 



The authors acknowledge Kopylov and Gognieva of the Sechenov University and Fuyou of the Shanghai Jiao Tong University for anonymized patient data, and two reviewers for valuable comments and suggestions. This work has been supported by the Russian Science Foundation (RSF), grant 14-31-00024.


  1. 1.
    Formaggia, L., Quarteroni, A., Veneziani, A.: Cardiovascular Mathematics—Modeling and Simulation of the Circulatory System. Springer, Verlag Milan (2009)zbMATHGoogle Scholar
  2. 2.
    Quarteroni, A., Veneziani, A., Vergara, C.: Geometric multiscale modeling of the cardiovascular system, between theory and practice. Comput. Methods Appl. Mech. Eng. 302, 193–252 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Taylor, C.A., Figueroa, C.A.: Patient-specific modeling of cardiovascular mechanics. Annu. Rev. Biomed. Eng. 11, 109–134 (2009)CrossRefGoogle Scholar
  4. 4.
    Zarins, C.K., Taylor, C.A., Min, J.K.: Computed fractional flow reserve (FFTCT) derived from coronary CT angiography. J. Cardiovasc. Transl. Res. 6(5), 708–714 (2013)CrossRefGoogle Scholar
  5. 5.
    Morris, P.D., Ryan, D., Morton, A.C., Lycett, R., Lawford, P.V., Hose, D.R., Gunn, J.P.: Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study. JACC: Cardiovasc. Interv. 6(2), 149–157 (2013)Google Scholar
  6. 6.
    Vassilevski Yu. V., Danilov A.A., Gamilov T.M., Ivanov, Yu.A., Pryamonosov, R.A., Simakov, S.S.: Patient-specific anatomical models in human physiology. Russ. J. Numer. Anal. Math. Model. 30, 185–201 (2015)Google Scholar
  7. 7.
    Bae, K.T.: Intravenous contrast medium administration and scan timing at CT: considerations and approaches. Radiology 256(1), 32–61 (2010)CrossRefGoogle Scholar
  8. 8.
    Wintersperger, B.J., Nikolaou, K., von Ziegler, F., Johnson, T., Rist, C., Leber, A., Flohr, T., Knez, A., Reiser, M.F., Becker, C.R.: Image quality, motion artifacts, and reconstruction timing of 64-slice coronary computed tomography angiography with 0.33-second rotation speed. Invest. Radiol. 41(5), 436–442 (2006)CrossRefGoogle Scholar
  9. 9.
    Yang, G., Kitslaar, P., Frenay, M., Broersen, A., Boogers, M.J., Bax, J.J., Reiber, J.H., Dijkstra, J.: Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography. Int. J. Cardiovasc. Imaging 28(4), 921–933 (2012)CrossRefGoogle Scholar
  10. 10.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI’98, LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Kumar, R.P., Albregtsen, F., Reimers, M., Edwin, B., Lang, T., Elle, O.J.: Blood vessel segmentation and centerline tracking using local structure analysis. In: Lackovi, I., Vasic, D. (eds.) 6th European Conference of the International Federation for Medical and Biological Engineering, IFMBE Proceedings, vol. 45, pp. 122–125. Springer, Cham. (2015)Google Scholar
  12. 12.
    Gulsun, M.A., Tek, H.: Robust vessel tree modeling. In: Metaxas, D., Axel, L., Fichtinger, G., Szkely, G. (eds.) Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008), LNCS, vol. 5241, pp. 602–611. Springer, Berlin, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Kjerland, O.: Segmentation of Coronary Arteries from CT-scans of the heart using Deep Learning. NTNU, (2017).
  14. 14.
    Danilov, A.A., Pryamonosov, R.P., Yurova, A.S.: Image segmentation for cardiovascular biomedical applications at different scales. Computation 2016 4(3), 35 (2016)CrossRefGoogle Scholar
  15. 15.
    Grady, L.: Fast, quality, segmentation of large volumes—isoperimetric distance trees. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision—ECCV 2006, Image Processing, Computer Vision, Image Recognition and Graphics, vol. 3951, pp. 449–462. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Danilov, A.A., Ivanov, Y., Pryamonosov, R., Vassilevski, Y.: Methods of graph network reconstruction in personalized medicine. Int. J. Numer. Meth. Biomed. Eng. 32(8), 20 (2006)CrossRefGoogle Scholar
  17. 17.
    Yoo, T.S., Ackerman, M.J., Lorensen, W.E., Schroeder, W., Chalana, V., Aylward, S., Metaxas, D., Whitaker, R.: Engineering and algorithm design for an image processing API: A technical report on ITK—The Insight Toolkit. In: Westwood, J.D., Hoffman, H.M., Robb, R.A., Stredney, D. (eds.) Proceedings of Medicine Meets Virtual Reality, vol. 85, pp. 586–592, IOS Press Amsterdam (2002)Google Scholar
  18. 18.
    GammaMed MultiVox. Last accessed 17 Oct 2018
  19. 19.
    DOT language and GraphViz open source software. Last accessed 17 Oct 2018

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Authors and Affiliations

  1. 1.Marchuk Institute of Numerical Mathematics of the RASMoscowRussian Federation
  2. 2.Moscow Institute of Physics and Technology (MIPT)Dolgoprudny, Moscow RegionRussian Federation
  3. 3.Sechenov UniversityMoscowRussian Federation

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