Segmenting the Papillary Muscles and the Trabeculae from High Resolution Cardiac CT through Restoration of Topological Handles

  • Mingchen Gao
  • Chao Chen
  • Shaoting Zhang
  • Zhen Qian
  • Dimitris Metaxas
  • Leon Axel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


We introduce a novel algorithm for segmenting the high resolution CT images of the left ventricle (LV), particularly the papillary muscles and the trabeculae. High quality segmentations of these structures are necessary in order to better understand the anatomical function and geometrical properties of LV. These fine structures, however, are extremely challenging to capture due to their delicate and complex nature in both geometry and topology. Our algorithm computes the potential missing topological structures of a given initial segmentation. Using techniques from computational topology, e.g. persistent homology, our algorithm find topological handles which are likely to be the true signal. To further increase accuracy, these proposals are measured by the saliency and confidence from a trained classifier. Handles with high scores are restored in the final segmentation, leading to high quality segmentation results of the complex structures.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)Google Scholar
  2. 2.
    Chen, C., Freedman, D., Lampert, C.H.: Enforcing topological constraints in random field image segmentation. In: CVPR, pp. 2089–2096 (2011)Google Scholar
  3. 3.
    Chen, T., Metaxas, D., Axel, L.: 3D cardiac anatomy reconstruction using high resolution CT data. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 411–418. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Cohen-Steiner, D., Edelsbrunner, H., Harer, J.: Stability of persistence diagrams. Discrete & Computational Geometry 37(1), 103–120 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Ecabert, O., Peters, J., Schramm, H., Lorenz, C., von Berg, J., Walker, M., Vembar, M., Olszewski, M., Subramanyan, K., Lavi, G., Weese, J.: Automatic model-based segmentation of the heart in CT images. TMI 27(9), 1189–1201 (2008)Google Scholar
  6. 6.
    Edelsbrunner, H., Harer, J.: Computational topology: an introduction. Amer. Mathematical Society (2010)Google Scholar
  7. 7.
    Funka-Lea, G., Boykov, Y., Florin, C., Jolly, M.-P., Moreau-Gobard, R., Ramaraj, R., Rinck, D.: Automatic heart isolation for CT coronary visualization using graph-cuts. In: ISBI, pp. 614–617 (April 2006)Google Scholar
  8. 8.
    Gao, M., Huang, J., Zhang, S., Qian, Z., Voros, S., Metaxas, D., Axel, L.: 4D cardiac reconstruction using high resolution CT images. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 153–160. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Gray, H.: Anatomy of the human body. Lea & Febiger (1918)Google Scholar
  10. 10.
    Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M., van Ginneken, B.: Multi-atlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. TMI 28(7), 1000–1010 (2009)Google Scholar
  11. 11.
    Jolly, M.-P.: Automatic segmentation of the left ventricle in cardiac MR and CT images. IJCV 70, 151–163 (2006)CrossRefGoogle Scholar
  12. 12.
    Kulp, S., Gao, M., Zhang, S., Qian, Z., Voros, S., Metaxas, D., Axel, L.: Using high resolution cardiac CT data to model and visualize patient-specific interactions between trabeculae and blood flow. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 468–475. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Lorenz, C., Berg, J.: A comprehensive shape model of the heart. Medical Image Analysis 10(4), 657–670 (2006)CrossRefGoogle Scholar
  14. 14.
    Nowozin, S., Lampert, C.: Global connectivity potentials for random field models. In: CVPR, pp. 818–825 (2009)Google Scholar
  15. 15.
    Schoenhagen, P., Stillman, A., Halliburton, S., White, R.: CT of the heart: principles, advances, clinical uses. Cleveland Clinic Journal of Medicine 72(2), 127–138 (2005)CrossRefGoogle Scholar
  16. 16.
    Ségonne, F., Pacheco, J., Fischl, B.: Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. TMI 26(4), 518–529 (2007)Google Scholar
  17. 17.
    Shen, D., Herskovits, E., Davatzikos, C.: An adaptive-focus statistical shape model for segmentation and shape modeling of 3D brain structures. TMI 20(4), 257–270 (2001)Google Scholar
  18. 18.
    Spreeuwers, L., Bangma, S., Meerwaldt, R., Vonken, E., Breeuwer, M.: Detection of trabeculae and papillary muscles in cardiac MR images. Computers in Cardiology, 415–418 (September 2005)Google Scholar
  19. 19.
    Sundaramoorthi, G., Yezzi, A.: Global regularizing flows with topology preservation for active contours and polygons. TIP 16(3), 803–812 (2007)MathSciNetGoogle Scholar
  20. 20.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. TMI 27(11), 1668–1681 (2008)Google Scholar
  21. 21.
    Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. PAMI 18(9), 884–900 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mingchen Gao
    • 1
  • Chao Chen
    • 1
  • Shaoting Zhang
    • 1
  • Zhen Qian
    • 2
  • Dimitris Metaxas
    • 1
  • Leon Axel
    • 3
  1. 1.CBIM CenterRutgers UniversityPiscatawayUSA
  2. 2.2 Piedmont Heart InstituteAtlantaUSA
  3. 3.New York UniversityNew YorkUSA

Personalised recommendations