An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions
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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.
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.
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.
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.
KeywordsEvaluation dataset Deformable registration Augmented reality
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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.
- 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.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.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
- 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.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.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.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.Eberly D (2001) Intersection of convex objects: the method of separating axes. Geometric Tools, LLC. http://www.geometrictools.com (1998–2008)
- 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.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.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.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
- 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.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.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.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
- 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
- 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