Skip to main content

Advertisement

SpringerLink
Log in

Preoperative liver registration for augmented monocular laparoscopy using backward–forward biomechanical simulation

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Augmented reality for monocular laparoscopy from a preoperative volume such as CT is achieved in two steps. The first step is to segment the organ in the preoperative volume and reconstruct its 3D model. The second step is to register the preoperative 3D model to an initial intraoperative laparoscopy image. To date, there does not exist an automatic initial registration method to solve the second step for the liver in the de facto operating room conditions of monocular laparoscopy. Existing methods attempt to solve for both deformation and pose simultaneously, leading to nonconvex problems with no optimal solution algorithms.

Methods

We propose in contrast to break the problem down into two parts, solving for (i) deformation and (ii) pose. Part (i) simulates biomechanical deformations from the preoperative to the intraoperative state to predict the liver’s unknown intraoperative shape by modeling gravity, the abdominopelvic cavity’s pressure and boundary conditions. Part (ii) rigidly registers the simulated shape to the laparoscopy image using contour cues.

Results

Our formulation leads to a well-posed problem, contrary to existing methods. This is because it exploits strong environment priors to complement the weak laparoscopic visual cues.

Conclusion

Quantitative results with in silico and phantom experiments and qualitative results with laparosurgery images for two patients show that our method outperforms the state-of-the-art in accuracy and registration time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Wolf I, Vetter M, Wegner I, Böttger T, Nolden M, Schöbinger M, Hastenteufel M, Kunert T, Meinzer HP (2005) The medical imaging interaction toolkit. Med Image Anal 9(6):594–604

    Article  PubMed  Google Scholar 

  2. Nicolau S, Soler L, Mutter D, Marescaux J (2011) Augmented reality in laparoscopic surgical oncology. Surg Oncol 20(3):189–201

    Article  PubMed  Google Scholar 

  3. Bernhardt S, Nicolau SA, Soler L, Doignon C (2017) The status of augmented reality in laparoscopic surgery as of 2016. Med Imaging Anal 37:66–90

    Article  Google Scholar 

  4. Koo B, Ozgur E, Le Roy B, Buc E, Bartoli A (2017) Deformable registration of a preoperative 3D liver volume to a laparoscopy image using contour and shading cues. In: MICCAI

  5. Adagolodjo Y, Trivisonne R, Haouchine N, Cotin S, Courtecuisse H (2017) Silhouette-based pose estimation for deformable organs application to surgical augmented reality. In: IROS

  6. Johnsen SF, Thompson S, Clarkson MJ, Modat M, Song Y, Totz J, Gurusamy K, Davidson B, Taylor ZA, Hawkes DJ, Ourselin S (2015) Database-based estimation of liver deformation under pneumoperitoneum for surgical image-guidance and simulation. In: MICCAI

  7. Bano J, Hostettler A, Nicolau SA, Doignon C, Wu HS, Huang MH, Soler L, Marescaux J (2012) Simulation of the abdominal wall and its arteries after pneumoperitoneum for guidance of port positioning in laparoscopic surgery. In: ISVC

  8. Nimura Y, Qu JD, Hayashi Y, Oda M, Kitasaka T, Hashizume M, Misawa K, Mori K (2015) Pneumoperitoneum simulation based on mass-spring-damper models for laparoscopic surgical planning. J Med Imaging 2(4):044004

    Article  Google Scholar 

  9. Bano J, Hostettler A, Nicolau SA, Cotin S, Doignon C, Wu HS, Huang MH, Soler L, Marescaux J (2012) Simulation of pneumoperitoneum for laparoscopic surgery planning. In: MICCAI

  10. Collins T, Pizarro D, Bartoli A, Bourdel N, Canis M (2014) Computer-aided laparoscopic myomectomy by augmenting the uterus with pre-operative MRI data. In: ISMAR

  11. Bernhardt S, Nicolau S, Bartoli A, Agnus V, Soler L, Doignon C (2015) Using shading to register an intraoperative CT scan to a laparoscopic image. Workshop CARE at MICCAI

  12. Saito A, Nakao M, Uranishi Y, Matsuda T (2015) Deformation estimation of elastic bodies using multiple silhouette images for endoscopic image augmentation. In: ISMAR

  13. Haouchine N, Roy F, Untereiner L, Cotin S (2016) Using contours as boundary conditions for elastic registration during minimally invasive hepatic surgery. In: IROS

  14. Suwelack S, Röhl S, Bodenstedt S, Reichard D, Dillmann R, Thiago S, Maier-Hein L, Wagner M, Wünscher J, Kenngott H, Müller BP, Spiedel S (2014) Physics-based shape matching for intraoperative image guidance. Med Phys 41(11):111901

    Article  PubMed  Google Scholar 

  15. Bender J, Koschier D, Charrier P, Weber D (2014) Position-based simulation of continuous materials. Comput Graph 44:1–10

    Article  Google Scholar 

  16. Toledo M, Ribeiro PC (2009) Radiological evaluation of a liver simulator in comparison to a human real liver. In: International Nuclear Atlantic Conference, INAC

  17. Mourcou Q, Fleury A, Franco C, Klopcic F, Vuillerme N (2015) Performance evaluation of smartphone inertial sensors measurement for range of motion. Sensors 15:23168–23187

    Article  PubMed  Google Scholar 

  18. Whiteley J (2005) The solution of inverse non-linear elasticity problems that arise when locating breast tumours. J Theor Med 6(3):143–149

    Article  Google Scholar 

  19. Carter T, Tanner C, Beechey-Newman N, Barratt D, Hawkes D (2008) MR navigated breast surgery: method and initial clinical experience. In: MICCAI

  20. Eiben B, Han L, Hipwell J, Mertzanidou T, Kabus S, Buelow T, Lorenz C, Newstead GM, Abe H, Keshtgar M, Ourselin S, Hawkes DJ (2013) Biomechanically guided prone-to-supine image registration of breast MRI using an estimated reference state. In: ISBI

  21. Sellier M (2011) An iterative method for the inverse elasto-static problem. J Fluids Struct 27:1461–1470

    Article  Google Scholar 

  22. Morin F, Courtecuisse H, Chabanas M, Payan Y (2015) Rest shape computation for highly deformable model of brain. Comput Methods Biomech Biomed Eng 18:2006–2007

    Article  Google Scholar 

  23. Eiben B, Vavourakis V, Hipwell J, Kabus S, Lorenz C, Buelow T, Hawkes DJ (2014) Breast deformation modelling: comparison of methods to obtain a patient specific unloaded configuration. In: Conference on medical imaging—image-guided procedures, robotic interventions, and modeling

  24. Mackling M, Müller M, Chentanez N (2016) XPBD: position-based simulation of compliant constrained dynamics. In: MIG

  25. Lepetit V, Moreno-Noguer F, Fua P (2008) EPnP: an accurate O(n) solution to the PnP problem. Int J Comput Vis 81(2):155–166

    Article  Google Scholar 

  26. Bosman J, Haouchine N, Dequidt J, Peterlik I (2014) The role of ligaments: patient-specific or scenario-specific? In: ISBMS

Download references

Author information

Authors and Affiliations

  1. EnCoV, IP, UMR 6602 CNRS, Universitè Clermont Auvergne, SIGMA, Aubière, France

    Erol Özgür, Bongjin Koo, Bertrand Le Roy, Emmanuel Buc & Adrien Bartoli

Authors
  1. Erol Özgür
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bongjin Koo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bertrand Le Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emmanuel Buc
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adrien Bartoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erol Özgür.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from the patients included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Özgür, E., Koo, B., Le Roy, B. et al. Preoperative liver registration for augmented monocular laparoscopy using backward–forward biomechanical simulation. Int J CARS 13, 1629–1640 (2018). https://doi.org/10.1007/s11548-018-1842-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-018-1842-3

Keywords

Search

Navigation