Deformable Registration of a Preoperative 3D Liver Volume to a Laparoscopy Image Using Contour and Shading Cues

  • Bongjin Koo
  • Erol ÖzgürEmail author
  • Bertrand Le Roy
  • Emmanuel Buc
  • Adrien Bartoli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


The deformable registration of a preoperative organ volume to an intraoperative laparoscopy image is required to achieve augmented reality in laparoscopy. This is an extremely challenging objective for the liver. This is because the preoperative volume is textureless, and the liver is deformed and only partially visible in the laparoscopy image. We solve this problem by modeling the preoperative volume as a Neo-Hookean elastic model, which we evolve under shading and contour cues. The contour cues combine the organ’s silhouette and a few curvilinear anatomical landmarks. The problem is difficult because the shading cue is highly nonconvex and the contour cues give curve-level (and not point-level) correspondences. We propose a convergent alternating projections algorithm, which achieves a \(4\%\) registration error.


  1. 1.
    Collins, T., Bartoli, A., Bourdel, N., Canis, M.: Dense, robust and real-time 3D tracking of deformable organs in monocular laparoscopy. In: Medical Image Computing and Computer Assisted Intervention, MICCAI 2016, Athens, Greece (2016)CrossRefGoogle Scholar
  2. 2.
    Puerto, G.A., Mariottini, G.-L.: A comparative study of correspondence-search algorithms in MIS images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 625–633. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33418-4_77CrossRefGoogle Scholar
  3. 3.
    Nicolau, S., Soler, L., Mutter, D., Marescaux, J.: Augmented reality in laparoscopic surgical oncology. Surg. Oncol. 20(3), 189–201 (2011)CrossRefGoogle Scholar
  4. 4.
    Haouchine, N., Roy, F., Untereiner, L., Cotin, S.: Using contours as boundary conditions for elastic registration during minimally invasive hepatic surgery. In: International Conference on Intelligent Robots and Systems, IROS 2016, South Korea (2016)Google Scholar
  5. 5.
    Collins, T., Pizarro, D., Bartoli, A., Bourdel, N., Canis, M.: Computer-aided laparoscopic myomectomy by augmenting the uterus with pre-operative MRI Data. In: IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2014, Munich, Germany (2014)Google Scholar
  6. 6.
    Bernhardt, S., Nicolau, S.A., Bartoli, A., Agnus, V., Soler, L., Doignon, C.: Using shading to register an intraoperative CT scan to a laparoscopic image. In: Luo, X., Reichl, T., Reiter, A., Mariottini, G.-L. (eds.) CARE 2015. LNCS, vol. 9515, pp. 59–68. Springer, Cham (2016). doi: 10.1007/978-3-319-29965-5_6CrossRefGoogle Scholar
  7. 7.
    Saito, A., Nakao, M., Uranishi, Y., Matsuda, T.: Deformation estimation of elastic bodies using multiple silhouette images for endoscopic image augmentation. In: IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2015, Fukuoka, Japan (2015)Google Scholar
  8. 8.
    Wolf, I., Vetter, M., Wegner, I., Nolden, M., Bottger, T., Hastenteufel, M., Schobinger, M., Kunert, T., Meinzer, H.P.: The medical imaging interaction toolkit (MITK).
  9. 9.
    Dubuisson, M.P., Jain, A.: A modified hausdorff distance for object matching. In: International Conference on Pattern Recognition, ICPR 1994, Jerusalem, Israel (1994)Google Scholar
  10. 10.
    Bender, J., Koschier, D., Charrier, P., Weber, D.: Position-based simulation of continuous materials. Comput. Graph. 44, 1–10 (2014)CrossRefGoogle Scholar
  11. 11.
    Nava, A., Mazza, E., Furrer, M., Villiger, P., Reinhart, W.H.: In vivo mechanical characterization of human liver. Med. Image Anal. 12, 203–216 (2008)CrossRefGoogle Scholar
  12. 12.
    Bauschke, H., Martin-Marquez, V., Moffat, S., Wang, X.: Compositions and convex combinations of asymptotically regular firmly nonexpansive mappings are also asymptotically regular. Fixed Point Theor. Appl. 2012, 53 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bongjin Koo
    • 1
  • Erol Özgür
    • 1
    Email author
  • Bertrand Le Roy
    • 1
  • Emmanuel Buc
    • 1
  • Adrien Bartoli
    • 1
  1. 1.EnCoV, IP, UMR 6602 CNRS, Université Clermont AuvergneClermont-FerrandFrance

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