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Abstract

The topologies of vascular trees embedded inside soft tissues carry important information which can be successfully exploited in the context of the computer-assisted planning and navigation. For example, topological matching of complete and/or partial hepatic trees provides important source of correspondences that can be employed straightforwardly by image registration algorithms. Therefore, robust and reliable extraction of vascular topologies from both pre- and intra-operative medical images is an important task performed in the context of surgical planning and navigation. In this paper, we propose an extension of an existing graph-based method where the vascular topology is constructed by computation of shortest paths in a minimum-cost spanning tree obtained from binary mask of the vascularization. We suppose that the binary mask is extracted from a 3D CT image using automatic segmentation and thus suffers from important artefacts and noise. When compared to the original algorithm, the proposed method (i) employs a new weighting measure which results in smoothing of extracted topology and (ii) introduces a set of tests based on various geometric criteria which are executed in order to detect and remove spurious branches. The method is evaluated on vascular trees extracted from abdominal contrast-enhanced CT scans and MR images. The method is quantitatively compared to the original version of the algorithm showing the importance of proposed modifications. Since the branch testing depends on parameters, the parametric study of the proposed method is presented in order to identify the optimal parametrization.

Keywords

Skeletonization Segmentation Computer-aided surgery Hepatic vascular structures 

References

  1. 1.
    World health organization. http://www.who.int. Accessed 12 Jun 2015
  2. 2.
    Mise, Y., Tani, K., Aoki, T., Sakamoto, Y., Hasegawa, K., Sugawara, Y., Kokudo, N.: Virtual liver resection: computer-assisted operation planning using a three-dimensional liver representation. J. Hepato-Biliary-Pancreat. Sci. 20(2), 157–164 (2013)CrossRefGoogle Scholar
  3. 3.
    Ambrosini, P., Ruijters, D., Niessen, W.J., Moelker, A., van Walsum, T.: Continuous roadmapping in liver tace procedures using 2D-3D catheter-based registration. Int. J. Comput. Assist. Radiol. Surg. 10(9), 1357–1370 (2015)CrossRefGoogle Scholar
  4. 4.
    Peterlík, I., Duriez, C., Cotin, S.: Modeling and real-time simulation of a vascularized liver tissue. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012. Springer 50–57 (2012)Google Scholar
  5. 5.
    Plantefève, R., Peterlik, I., Haouchine, N., Cotin, S.: Patient-specific biomechanical modeling for guidance during minimally-invasive hepatic surgery. Ann. Biomed. Eng. 44(1), 139–153 (2016)CrossRefGoogle Scholar
  6. 6.
    Lee, T.C., Kashyap, R.L., Chu, C.N.: Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP. Graph. Models Image Process. 56(6), 462–478 (1994)CrossRefGoogle Scholar
  7. 7.
    Piccinelli, M., Veneziani, A., Steinman, D.A., Remuzzi, A., Antiga, L.: A framework for geometric analysis of vascular structures: application to cerebral aneurysms. IEEE Trans. Med. Imaging 28(8), 1141–1155 (2009)CrossRefGoogle Scholar
  8. 8.
    Verscheure, L., Peyrodie, L., Dewalle, A.S., Reyns, N., Betrouni, N., Mordon, S., Vermandel, M.: Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results. Int. J. Comput. Assist. Radiol. Surg. 8(2), 233–246 (2013)CrossRefGoogle Scholar
  9. 9.
    Valencia, L.F., Pinzón, A.M., Richard, J.C., Hoyos, M.H., Orkisz, M.: Simultaneous skeletonization and graph description of airway trees in 3D CT images. In: XXVème Colloque GRETSI (2015)Google Scholar
  10. 10.
    Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar
  11. 11.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1988)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • R. Plantefève
    • 1
    • 2
  • S. Kadoury
    • 1
    • 2
  • A. Tang
    • 2
  • I. Peterlik
    • 3
    • 4
    Email author
  1. 1.Ecole Polytechnique de MontrèalMontrealCanada
  2. 2.CRCHUMMontrealCanada
  3. 3.InriaStrasbourgFrance
  4. 4.Institute of Computer ScienceMasaryk UniversityBrnoCzech Republic

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