Advertisement

An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions

  • Richard ModrzejewskiEmail author
  • Toby Collins
  • Barbara Seeliger
  • Adrien Bartoli
  • Alexandre Hostettler
  • Jacques Marescaux
Original Article
  • 27 Downloads

Abstract

Purpose

The registration of preoperative 3D images to intra-operative laparoscopic 2D images is one of the main concerns for augmented reality in computer-assisted surgery. For laparoscopic liver surgery, while several algorithms have been proposed, there is neither a public dataset nor a systematic evaluation methodology to quantitatively evaluate registration accuracy.

Method

Our main contribution is to provide such a dataset with an in vivo porcine model. It is used to evaluate a state-of-the-art registration algorithm that is capable of simultaneous registration and soft-body collision reasoning.

Results

The dataset consists of 13 deformed liver states, with corresponding exploration videos and interventional CT acquisitions with 60 small artificial fiducials located on the surface of the liver and distributed within the parenchyma, where a precise registration is crucial for augmented reality. This dataset will be made public. Using this dataset, we show that collision reasoning improves performance of registration for strong deformation and independent lobe motion.

Conclusion

This dataset addresses the lack of public datasets in this field. As an example of use, we present and evaluate a state-of-the-art energy-based approach and a novel extension that handles self-collisions.

Keywords

Evaluation dataset Deformable registration Augmented reality 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national, and institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the institution at which the studies were conducted.

References

  1. 1.
    Adagolodjo Y, Trivisonne R, Haouchine N, Cotin S, Courtecuisse H (2017) Silhouette-based pose estimation for deformable organs application to surgical augmented reality. In: Intelligent robots and systems (IROS). IEEE, pp 539–544Google Scholar
  2. 2.
    Allard J, Cotin S, Faure F, Bensoussan PJ, Poyer F, Duriez C, Delingette H, Grisoni L (2007) Sofa—an open source framework for medical simulation. In: MMVR 15-Medicine meets virtual reality, vol 125. IOP Press, pp 13–18Google Scholar
  3. 3.
    Amberg B, Romdhani S, Vetter T (2007) Optimal step nonrigid ICP algorithms for surface registration. In: IEEE conference on CVPR’07. IEEE, pp 1–8Google Scholar
  4. 4.
    Clements LW, Chapman WC, Dawant BM, Galloway RL Jr, Miga MI (2008) Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation. Med Phys 35(6Part1):2528–2540CrossRefGoogle Scholar
  5. 5.
    Collins JA, Weis JA, Heiselman JS, Clements LW, Simpson AL, Jarnagin WR, Miga MI (2017) Improving registration robustness for image-guided liver surgery in a novel human-to-phantom data framework. IEEE Trans Med Imaging 36(7):1502–1510CrossRefGoogle Scholar
  6. 6.
    Collins T, Bartoli A (2015) [POSTER] Realtime shape-from-template: system and applications. In: 2015 IEEE international symposium on ISMAR. IEEE, pp 116–119Google Scholar
  7. 7.
    Collins T, Bartoli A, Bourdel N, Canis M (2016) Robust, real-time, dense and deformable 3D organ tracking in laparoscopic videos. In: MICCAI. Springer, pp 404–412Google Scholar
  8. 8.
    Collins T, Chauvet P, Debize C, Pizarro D, Bartoli A, Canis M, Bourdel N (2016) A system for augmented reality guided laparoscopic tumour resection with quantitative ex-vivo user evaluation. In: CARE. Springer, pp 114–126Google Scholar
  9. 9.
    Cour T, Srinivasan P, Shi J (2006) Balanced graph matching. In: Proceedings of the 19th international conference on neural information processing systems. MIT Press, pp 313–320Google Scholar
  10. 10.
    Eberly D (2001) Intersection of convex objects: the method of separating axes. Geometric Tools, LLC. http://www.geometrictools.com (1998–2008)
  11. 11.
    Fabian S, Spinczyk D (2018) Target registration error minimization for minimally invasive interventions involving deformable organs. Comput Med Imaging Graph 65:4–10CrossRefGoogle Scholar
  12. 12.
    Fitzpatrick JM (2009) Fiducial registration error and target registration error are uncorrelated. In: Medical imaging 2009—visualization, image-guided procedures, and modeling, vol 7261. International Society for Optics and Photonics, p 726102Google Scholar
  13. 13.
    Haouchine N, Dequidt J, Peterlik I, Kerrien E, Berger MO, Cotin S (2013) Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery. In: 2013 IEEE international symposium on ISMAR. IEEEGoogle Scholar
  14. 14.
    Haouchine N, Roy F, Untereiner L, Cotin S (2016) Using contours as boundary conditions for elastic registration during minimally invasive hepatic surgery. In: 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEEGoogle Scholar
  15. 15.
    Heiselman JS, Collins JA, Clements LW, Weis JA, Simpson AL, Geevarghese SK, Kingham TP, Jarnagin WR, Miga MI (2018) Nonrigid registration for laparoscopic liver surgery using sparse intraoperative data. In: Medical imaging 2018—image-guided procedures, robotic interventions, and modeling, vol 10576. International Society for Optics and Photonics, p 105760DGoogle Scholar
  16. 16.
    Kerdok AE, Cotin SM, Ottensmeyer MP, Galea AM, Howe RD, Dawson SL (2003) Truth cube: establishing physical standards for soft tissue simulation. Med Image Anal 7(3):283–291CrossRefGoogle Scholar
  17. 17.
    Klosowski JT, Held M, Mitchell JS, Sowizral H, Zikan K (1998) Efficient collision detection using bounding volume hierarchies of k-DOPs. IEEE TVCG 4:21–36Google Scholar
  18. 18.
    Koo B, Özgür 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. Springer, pp 326–334Google Scholar
  19. 19.
    Mahmoud N, Cirauqui I, Hostettler A, Doignon C, Soler L, Marescaux J, Montiel J (2016) Orbslam-based endoscope tracking and 3D reconstruction. In: CARE. Springer, pp 72–83Google Scholar
  20. 20.
    Modrzejewski R, Collins T, Bartoli A, Hostettler A, Marescaux J (2018) Soft-body registration of pre-operative 3D models to intra-operative RGBD partial body scans. In: MICCAI. Springer, pp 39–46Google Scholar
  21. 21.
    Plantefeve R, Peterlik I, Haouchine N, Cotin S (2016) Patient-specific biomechanical modeling for guidance during minimally-invasive hepatic surgery. Ann Biomed Eng 44(1):139–153CrossRefGoogle Scholar
  22. 22.
    Rumman NA, Schaerf M, Bechmann D (2015) Collision detection for articulated deformable characters. In: Proceedings of the 8th ACM SIGGRAPH conference on motion in games. ACM, pp 215–220Google Scholar
  23. 23.
    Suwelack S, Röhl S, Bodenstedt S, Reichard D, Dillmann R, Santos T, Maier-Hein L, Wagner M, Wünscher J, Kenngott H (2014) Physics-based shape matching for intraoperative image guidance. Med Phys 41(11):111901CrossRefGoogle Scholar
  24. 24.
    Thompson S, Schneider C, Bosi M, Gurusamy K, Ourselin S, Davidson B, Hawkes D, Clarkson MJ (2018) In vivo estimation of target registration errors during augmented reality laparoscopic surgery. CARS 13(6):865–874CrossRefGoogle Scholar
  25. 25.
    Thompson S, Totz J, Song Y, Johnsen S, Stoyanov D, Ourselin S, Gurusamy K, Schneider C, Davidson B, Hawkes D (2015) Accuracy validation of an image guided laparoscopy system for liver resection. In: Medical imaging 2015—image-guided procedures, robotic interventions, and modeling, vol 9415. SPIE, p 941509Google Scholar
  26. 26.
    Zijlmans M, Langø T, Hofstad EF, Van Swol CF, Rethy A (2012) Navigated laparoscopy–liver shift and deformation due to pneumoperitoneum in an animal model. Minim Invasive Ther Allied Technol 21(3):241–248CrossRefGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.EnCoV, Institut Pascal, UMR 6602, CNRS/UBP/SIGMAClermont-FerrandFrance
  2. 2.IRCAD and IHU-StrasbourgStrasbourgFrance

Personalised recommendations