TGIF: Topological Gap In-Fill for Vascular Networks

A Generative PhysiologicalModeling Approach
  • Matthias Schneider
  • Sven Hirsch
  • Bruno Weber
  • Gábor Székely
  • Bjoern H. Menze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


This paper describes a new approach for the reconstruction of complete 3-D arterial trees from partially incomplete image data. We utilize a physiologically motivated simulation framework to iteratively generate artificial, yet physiologically meaningful, vasculatures for the correction of vascular connectivity. The generative approach is guided by a simplified angiogenesis model, while at the same time topological and morphological evidence extracted from the image data is considered to form functionally adequate tree models. We evaluate the effectiveness of our method on four synthetic datasets using different metrics to assess topological and functional differences. Our experiments show that the proposed generative approach is superior to state-of-the-art approaches that only consider topology for vessel reconstruction and performs consistently well across different problem sizes and topologies.


vascular reconstruction vascular connectivity angiogenesis 


  1. 1.
    Blinder, P., et al.: The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow. Nature Neuroscience 16(7), 889–897 (2013)CrossRefGoogle Scholar
  2. 2.
    Jiang, Y., Zhuang, Z.W., Sinusas, A.J., Staib, L.H., Papademetris, X.: Vessel connectivity using Murray’s hypothesis. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 528–536. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Kaufhold, J.P., et al.: Vectorization of optically sectioned brain microvasculature: Learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments. Medical Image Analysis 16(6), 1241–1258 (2012)CrossRefGoogle Scholar
  4. 4.
    Lesage, D., et al.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Medical Image Analysis 13(6), 819–845 (2009)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lloyd, B.A., Hirsch, S., Székely, G.: Optimization of case-specific vascular tree models based on vessel size imaging. In: Bello, F., Cotin, S. (eds.) ISBMS 2010. LNCS, vol. 5958, pp. 38–48. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Mori, S., van Zijl, P.C.M.: Fiber tracking: principles and strategies – a technical review. NMR in Biomedicine 15(7-8), 468–480 (2002)CrossRefGoogle Scholar
  7. 7.
    Qin, D., et al.: Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors. In: CVPR 2011, pp. 777–784 (2011)Google Scholar
  8. 8.
    Reichold, J.: Cerebral blood flow modeling in realistic cortical microvascular networks. Ph.D. thesis, ETH Zurich, Zurich, Switzerland (2011)Google Scholar
  9. 9.
    Rempfler, M., Schneider, M., Ielacqua, G.D., Xiao, X., Stock, S.R., Klohs, J., Székely, G., Andres, B., Menze, B.H.: Extracting vascular networks under physiological constraints via integer programming. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 498–505. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Risser, L., Plouraboue, F., Descombes, X.: Gap filling of 3-D microvascular networks by tensor voting. IEEE Transactions on Medical Imaging 27(5), 674–687 (2008)CrossRefGoogle Scholar
  11. 11.
    Schneider, M., Hirsch, S., Székely, G., Weber, B., Menze, B.H.: Oblique random forests for 3-D vessel detection using steerable filters and orthogonal subspace filtering. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds.) MCV 2012. LNCS, vol. 7766, pp. 142–154. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Schneider, M., et al.: Tissue metabolism driven arterial tree generation. Medical Image Analysis 16(7), 1397–1414 (2012)CrossRefGoogle Scholar
  13. 13.
    Secomb, T.W., et al.: Angiogenesis: An adaptive dynamic biological patterning problem. PLoS Computational Biology 9(3), e1002983 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Matthias Schneider
    • 1
    • 2
  • Sven Hirsch
    • 1
  • Bruno Weber
    • 2
  • Gábor Székely
    • 1
  • Bjoern H. Menze
    • 1
    • 3
  1. 1.Computer Vision LaboratoryETH ZurichSwitzerland
  2. 2.Institute of Pharmacology and ToxicologyUniversity of ZurichSwitzerland
  3. 3.Institute for Advanced Study and Department of Computer ScienceTU MunichGermany

Personalised recommendations