Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning

  • Mehmet A. GülsünEmail author
  • Gareth Funka-Lea
  • Puneet Sharma
  • Saikiran Rapaka
  • Yefeng Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


We present a novel method for the automated extraction of blood vessel centerlines. There are two major contributions. First, in order to avoid the shortcuts to which minimal path methods are prone, we find optimal paths in a computed flow field. We solve for a steady state porous media flow inside a region of interest and trace centerlines as maximum flow paths. We explain how to estimate anisotropic orientation tensors which are used as permeability tensors in our flow field computation. Second, we introduce a convolutional neural network (CNN) classifier for removing extraneous paths in the detected centerlines. We apply our method to the extraction of coronary artery centerlines found in Computed Tomography Angiography (CTA). The robustness and stability of our method are enhanced by using a model-based detection of coronary specific territories and main branches to constrain the search space [15]. Validation against 20 comprehensively annotated datasets had a sensitivity and specificity at or above 90 %. Validation against 106 clinically annotated coronary arteries showed a sensitivity above 97 %.



The authors thank Adriaan Coenen, MD at Erasmus Univ. Medical Center for processing and making available the clinical data set.


  1. 1.
    Bekkers, E., Duits, R., Berendschot, T., Ter Haar Romeny, B.: A multi-orientation analysis approach to retinal vessel tracking. J. Math. Imaging Vis. 49(3), 583–610 (2014)CrossRefGoogle Scholar
  2. 2.
    Bouix, S., Siddiqi, K., Tannenbaum, A.: Flux driven automatic centerline extraction. MedIA 9(3), 209–221 (2005)Google Scholar
  3. 3.
    Breitenreicher, D., Sofka, M., Britzen, S., Zhou, S.K.: Hierarchical discriminative framework for detecting tubular structures in 3D images. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 328–339. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Cetin, S., Demir, A., Yezzi, A., Degertekin, M., Unal, G.: Vessel tractography using an intensity based tensor model with branch detection. IEEE T-MI. 32(2), 348–363 (2013)Google Scholar
  5. 5.
    Coenen, A., et al.: Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. Radiology 274(3), 674–683 (2015)CrossRefGoogle Scholar
  6. 6.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, p. 130. Springer, Heidelberg (1998). doi: 10.1007/BFb0056195CrossRefGoogle Scholar
  7. 7.
    Friman, O., Hindennach, M., Kühnel, C., Peitgen, H.-O.: Multiple hypothesis template tracking of small 3D vessel structures. MedIA 14(2), 160–171 (2010)Google Scholar
  8. 8.
    Law, M., Chung, A.: Efficient implementation for spherical flux computation and its application to vascular segmentation. IEEE T-IP 18(3), 596–612 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lesage, D., Angelini, E., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen segmentation techniques: models, featuers and extraction schemes. MedIA 13(6), 819–845 (2009)Google Scholar
  10. 10.
    Pollack, M.: The maximum capacity through a network. Oper. Res. 8(5), 733–736 (1960)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Rouchdy, Y., Cohen, L.: Geodesic voting for the automatic extraction of tree structures. Methods and applications. CVIU 117, 1453–1467 (2013)Google Scholar
  12. 12.
    Schaap, M., et al.: Standardized evaluation methodology and reference database for evaulating coronary artery centerline extraction algorithms. MedIA 13(5), 701–714 (2009)Google Scholar
  13. 13.
    Türetken, E., Benmansour, F. Fua, P.: Automated reconstruction of tree structures using path classifiers and mixed integer programming. In: CVPR, pp. 566–573, June 2012Google Scholar
  14. 14.
    Zheng, Y., Loziczonek, M., Georgescu, B., Zhou, S., Vega-Higuera, F. Comaniciu, D.: Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes. In: SPIE Medical Imaging (2011)Google Scholar
  15. 15.
    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

Copyright information

© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, 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

  • Mehmet A. Gülsün
    • 1
    Email author
  • Gareth Funka-Lea
    • 1
  • Puneet Sharma
    • 1
  • Saikiran Rapaka
    • 1
  • Yefeng Zheng
    • 1
  1. 1.Medical Imaging Technologies, Siemens Medical Solutions USA, Inc.PrincetonUSA

Personalised recommendations