Annals of Biomedical Engineering

, Volume 44, Issue 1, pp 139–153 | Cite as

Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery

  • Rosalie Plantefève
  • Igor Peterlik
  • Nazim Haouchine
  • Stéphane Cotin
Computational Biomechanics for Patient-Specific Applications


During the minimally-invasive liver surgery, only the partial surface view of the liver is usually provided to the surgeon via the laparoscopic camera. Therefore, it is necessary to estimate the actual position of the internal structures such as tumors and vessels from the pre-operative images. Nevertheless, such task can be highly challenging since during the intervention, the abdominal organs undergo important deformations due to the pneumoperitoneum, respiratory and cardiac motion and the interaction with the surgical tools. Therefore, a reliable automatic system for intra-operative guidance requires fast and reliable registration of the pre- and intra-operative data. In this paper we present a complete pipeline for the registration of pre-operative patient-specific image data to the sparse and incomplete intra-operative data. While the intra-operative data is represented by a point cloud extracted from the stereo-endoscopic images, the pre-operative data is used to reconstruct a biomechanical model which is necessary for accurate estimation of the position of the internal structures, considering the actual deformations. This model takes into account the patient-specific liver anatomy composed of parenchyma, vascularization and capsule, and is enriched with anatomical boundary conditions transferred from an atlas. The registration process employs the iterative closest point technique together with a penalty-based method. We perform a quantitative assessment based on the evaluation of the target registration error on synthetic data as well as a qualitative assessment on real patient data. We demonstrate that the proposed registration method provides good results in terms of both accuracy and robustness w.r.t. the quality of the intra-operative data.


Patient-specific modeling Non-rigid registration Minimally-invasive surgery Real-time simulation 

Supplementary material

Supplementary material 1 (.mov 3281 kb)


  1. 1.
    Antiga, L. and B. Ene-Iordache. Centerline computation and geometric analysis of branching tubular surfaces with application to blood vessel modeling. WSCG, 2003.Google Scholar
  2. 2.
    Bano, J. et al. Simulation of pneumoperitoneum for laparoscopic surgery planning. In: Proceedings of the 15th MICCAI: Part I, pp. 91–98, 2012.Google Scholar
  3. 3.
    Baraff, D. and A. Witkin. Large steps in cloth simulation. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’98, New York, NY: ACM, pp. 43–54, 1998.Google Scholar
  4. 4.
    Boltcheva, D., M. Yvinec, and J.-D. Boissonnat. Mesh generation from 3d multi-material images. In: Proceedings of the 12th MICCAI—Volume Part II, Berlin: Springer, pp. 283–290, 2009.Google Scholar
  5. 5.
    Bouguet, J.Y. Pyramidal implementation of the Lucas Kanade feature tracker: description of the algorithm, 2002.Google Scholar
  6. 6.
    Clements, L.W. et al. Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation. Med. Phys. 35(6):2528–2540, 2008.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Courtecuisse, H., J. Allard, P. Kerfriden, S.P. Bordas, S. Cotin, and C. Duriez. Real-time simulation of contact and cutting of heterogeneous soft-tissues. Med. Image Anal. 18(2):394–410, 2014.PubMedCrossRefGoogle Scholar
  8. 8.
    Dos Santos, T.R., A. Seitel, T. Kilgus, S. Suwelack, A.-L. Wekerle, H. Kenngott, S. Speidel, H.-P. Schlemmer, H.-P. Meinzer, and T. Heimann, et al. Pose-independent surface matching for intra-operative soft-tissue marker-less registration. Med. Image Anal. 18(7):1101–1114, 2014.PubMedCrossRefGoogle Scholar
  9. 9.
    Duriez, C., S. Cotin, J. Lenoir, and P. Neumann. New approaches to catheter navigation for interventional radiology simulation 1. Comput. Aided Surg. 11(6):300–308, 2006.PubMedCrossRefGoogle Scholar
  10. 10.
    Elhawary, H. and A. Popovic. Robust feature tracking on the beating heart for a robotic-guided endoscope. Int J Med Robot. 7:459–468, 2010.Google Scholar
  11. 11.
    Felippa, C.A. A study of optimal membrane triangles with drilling freedoms. CMAME 192(16–18):2125–2168, 2003.Google Scholar
  12. 12.
    Gauglitz, S., T. Hllerer, and M. Turk. Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vis. 94(3):335–360, 2011.CrossRefGoogle Scholar
  13. 13.
    Gower, J.C. Generalised procrustes analysis. Psychometrika 40:33–51, 1975.CrossRefGoogle Scholar
  14. 14.
    Haouchine, N., J. Dequidt, I. Peterlik, E. Kerrien, M.-O. Berger, and S. Cotin. Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery. In ISMAR 2013, pp. 199–208, 2013.Google Scholar
  15. 15.
    Haouchine, N., J. Dequidt, I. Peterlik, E. Kerrien, M.-O. Berger, and S. Cotin. Towards an accurate tracking of liver tumors for augmented reality in robotic assisted surgery. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4121–4126, 2014.Google Scholar
  16. 16.
    Haouchine, N., I. Peterlik, J. Dequidt, M. Sanz-Lopez, E. Kerrien, M.-O. Berger, and S. Cotin. Impact of soft tissue heterogeneity on augmented reality for liver surgery. IEEE TVCG 21:584– 597, 2015, accepted for publication.Google Scholar
  17. 17.
    Hartley, R.I. and A. Zisserman. Multiple view geometry in computer vision, 2nd edn. Cambridge: Cambridge University Press, ISBN: 0521540518, 2004.Google Scholar
  18. 18.
    Kerdok, A.E., M.P. Ottensmeyer, and R.D. Howe. Effects of perfusion on the viscoelastic characteristics of liver. J. Biomech. 39:2221–2231, 2006.PubMedCrossRefGoogle Scholar
  19. 19.
    Maier-Hein, L., P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov. Optical techniques for 3d surface reconstruction in computer-assisted laparoscopic surgery. Med. Image Anal. 17:974–996, 2013.PubMedCrossRefGoogle Scholar
  20. 20.
    Mazza, E., A. Nava, D. Hahnloser, W. Jochum, and M. Bajka. The mechanical response of human liver and its relation to histology: An in vivo study. Med. Image Anal. 11(6):663–672, 2007.PubMedCrossRefGoogle Scholar
  21. 21.
    Mikolajczyk, K. and C. Schmid. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10):1615–1630, 2005.PubMedCrossRefGoogle Scholar
  22. 22.
    Nesme, M., Y. Payan, and F. Faure. Efficient, physically plausible finite elements. In: Eurographics 2005, Short papers, August, 2005, edited by J. Dingliana and F. Ganovelli, Trinity College, Dublin, pp. 77–80, 2005.Google Scholar
  23. 23.
    Nguyen, B., T. Yang, F. Leong, S. Chang, and S. Ong. Patient specific biomechanical modeling of hepatic vasculature for augmented reality surgery. In: Proceedings of MIAR2008, pp. 50–57, 2008.Google Scholar
  24. 24.
    Nicolau, S., L. Soler, D. Mutter, and J. Marescaux. Augmented reality in laparoscopic surgical oncology. Surg. Oncol. 20(3):189–201, 2011.PubMedCrossRefGoogle Scholar
  25. 25.
    Oktay, O. et al. Biomechanically driven registration of pre- to intra-operative 3d images for laparoscopic surgery. In: Proceedings of the 16th MICCAI: Part II, pp. 1–9, 2013.Google Scholar
  26. 26.
    Peterlik, I., C. Duriez, and S. Cotin. Modeling and real-time simulation of a vascularized liver tissue. In: Proceedings of the 15th MICCAI—Volume Part I, Berlin: Springer, pp. 50–57, 2012.Google Scholar
  27. 27.
    Peterlík, I., T. Golembiovský, C. Duriez, and S. Cotin. Complete real-time liver model including glissons capsule, vascularization and parenchyma. Medicine Meets Virtual Reality 21: NextMed/MMVR21,196:312–319, 2014.Google Scholar
  28. 28.
    Plantefeve, R. et al. Automatic alignment of pre and intraoperative data using anatomical landmarks for augmented laparoscopic liver surgery. In Biomedical Simulation, edited by F. Bello and S. Cotin, Lecture Notes in Computer Science, vol. 8789. Berlin: Springer, pp. 58–66, 2014a.Google Scholar
  29. 29.
    Plantefeve, R., I. Peterlik, H. Courtecuisse, R. Trivisonne, J.-P. Radoux, and S. Cotin. Atlas-based transfer of boundary conditions for biomechanical simulation. In: Proceedings of the 17th MICCAI: Part III, Berlin: Springer, pp. 33–40, 2014b.Google Scholar
  30. 30.
    Pratt, P., D. Stoyanov, M. Visentini-Scarzanella, and G.-Z. Yang. Dynamic guidance for robotic surgery using image- constrained biomechanical models. In: Proceedings of the 13th MICCAI: Part I, MICCAI’10, Berlin: Springer, pp. 77–85, 2010.Google Scholar
  31. 31.
    Puerto-Souza, G. and G. Mariottini. Toward long-term and accurate augmented-reality display for minimally-invasive surgery. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 5384–5389, 2013.Google Scholar
  32. 32.
    Schaerer, J., C. Casta, J. Pousin, and P. Clarysse. A dynamic elastic model for segmentation and tracking of the heart in mr image sequences. Med. Image Anal. 14(6):738–749, 2010.PubMedCrossRefGoogle Scholar
  33. 33.
    Schneider, P.J. An algorithm for automatically fitting digitized curves. In: Graphics Gems, edited by A. S. Glassner, Academic Press Professional, Inc., pp. 612–626, 1990Google Scholar
  34. 34.
    Shewchuk, J.R. An introduction to the conjugate gradient method without the agonizing pain. Technical Report, 1994.Google Scholar
  35. 35.
    Speidel, S., S. Roehl, S. Suwelack, R. Dillmann, H. Kenngott, and B. Mueller-Stich. Intraoperative surface reconstruction and biomechanical modeling for soft tissue registration. In: Proceedings of Joint Workshop on New Technologies for Computer/Robot Assisted Surgery, 2011.Google Scholar
  36. 36.
    Stoyanov, D. Surgical vision. Ann. Biomed. Eng. 40(2):332–345, 2012.PubMedCrossRefGoogle Scholar
  37. 37.
    Su, L.-M., B.P. Vagvolgyi, R. Agarwal, C.E. Reiley, R.H. Taylor, and G D. Hager. Augmented reality during robot-assisted laparoscopic partial nephrectomy: toward real-time 3d-ct to stereoscopic video registration. Urology, 73(4):896–900, 2009.PubMedCrossRefGoogle Scholar
  38. 38.
    Suwelack, S., S. Röhl, S. Bodenstedt, D. Reichard, R. Dillmann, T. dos Santos, L. Maier-Hein, M. Wagner, J. Wünscher, H. Kenngott, et al. Physics-based shape matching for intraoperative image guidance. Med. Phys. 41(11):111901, 2014.PubMedCrossRefGoogle Scholar
  39. 39.
    Umale, S. Characterization and modeling of abdominal organs. PhD thesis, Strasbourg, 2012.Google Scholar
  40. 40.
    Umale, S., S. Chatelin, N. Bourdet, C. Deck, M. Diana, P. Dhumane, L. Soler, J. Marescaux, and R. Willinger. Experimental in vitro mechanical characterization of porcine Glisson’s capsule and hepatic veins. J. Biomech. 44(9):1678–1683, 2011.PubMedCrossRefGoogle Scholar
  41. 41.
    Verscheure, L., L. Peyrodie, A.-S. Dewalle, N. Reyns, N. Betrouni, S. Mordon, and M. Vermandel. Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results. Int. J. Comput. Assist. Radiol. Surg. 8(2):233–246, 2013.PubMedCrossRefGoogle Scholar
  42. 42.
    Wittek, A., T. Hawkins, and K. Miller. On the unimportance of constitutive models in computing brain deformation for image-guided surgery. Biomech. Model. Mechanobiol. 8(1):77–84, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Yeh, W.-C., P.-C. Li, Y.-M. Jeng, H.-C. Hsu, P.-L. Kuo, M.-L. Li, P.-M. Yang, and P. H. Lee. Elastic modulus measurements of human liver and correlation with pathology. Ultrasound Med. Biol. 28(4):467–474, 2002.PubMedCrossRefGoogle Scholar
  44. 44.
    Yushkevich, P. A., J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig. User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128, 2006.PubMedCrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2015

Authors and Affiliations

  • Rosalie Plantefève
    • 1
  • Igor Peterlik
    • 2
  • Nazim Haouchine
    • 3
  • Stéphane Cotin
    • 3
  1. 1.Altran and Inria (Mimesis Team)StrasbourgFrance
  2. 2.Institute of Computer ScienceMasaryk UniversityBrnoCzech Republic
  3. 3.Inria (Mimesis Team)StrasbourgFrance

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