Abstract
In this paper we present a real-time intra-operative reconstruction system for laparoscopic surgery. The system builds upon a surgical robot for laparoscopy that has previously been developed by us. Such a system is valuable for surgeons, who can get a three dimensional visualization of the scene online, without having to postprocess data. We gain a significant speed increase over existing such systems by carefully parallelizing tasks and using the GPU for computationally expensive sub-tasks, making real-time reconstruction and visualization possible. Our implementation is also robust with respect to outliers and can potentially be extended to be used with non-robotic surgery. We demonstrate the performance of our system on ex-vivo samples and compare it to alternative implementations.
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References
Cgal, Computational Geometry Algorithms Library, http://www.cgal.org
Bay, H., Tuytelaars, T., Gool, L.J.V.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Bouguet, J.Y.: Pyramidal implementation of the lucas kanade feature tracker, description of the algorithm. Tech. rep., Intel Corporation Microprocessor Research Labs (2000) (from the OpenCV documentation)
Engels, C., Stewénius, H., Nistér, D.: Bundle adjustment rules. In: Proceedings of Photogrammetric Computer Vision. The International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences (2006)
Klippenstein, J., Zhang, H.: Quantitative evaluation of feature extractors for visual slam. In: Proceedings of the Fourth Canadian Conference on Computer and Robot Vision (2007)
Koppel, D., Wang, Y.F., Lee, H.: Image-based rendering and modeling in video-endoscopy. In: Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, April 2004, pp. 269–272. IEEE, Arlington (2004)
Koppel, D., Wang, Y.F., Lee, H.: Robust and real-time image stabilization and rectification. In: Proceedings of the Seventh IEEE Workshop on Applications of Computer Vision/IEEE Workshop on Motion and Video Computing, vol. 1, pp. 320–355. IEEE Computer Society, Los Alamitos (2005)
Lourakis, M.I.A., Argyros, A.A.: The design and implementation of a generic sparse bundle adjustment software package based on the levenberg-marquardt algorithm. Tech. Rep. 340, Institute of Computer Science - FORTH, Heraklion, Crete, Greece (August 2004), http://www.ics.forth.gr/~lourakis/sba
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mouragnon, E., Lhuillier, M., Dhome, M., Dekeyser, F., Sayd, P.: 3d reconstruction of complex structures with bundle adjustment: an incremental approach. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, May 2006, pp. 3055–3061. Orlando, Florida (2006)
Salzmann, M., Hartley, R., Fua, P.: Convex optimization for deformable surface 3-d tracking. In: Proceedings of the 2007 IEEE International Conference on Computer Vision (2007)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), June 1994, pp. 593–600 (1994)
Wengert, C.: Quantitative Endoscopy. Ph.D. thesis, ETH Zürich (2008)
Zach, C., Gallup, D., Frahm, J.M.: Fast gain-adaptive klt tracking on the gpu. In: Proceedings of Computer Vision and Pattern Recognition Workshops, pp. 1–7 (2008)
Zinßer, T., Gräßl, C., Niemann, H.: Efficient feature tracking for long video sequences. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 326–333. Springer, Heidelberg (2004)
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Moll, M., Tang, HW., Van Gool, L. (2010). GPU-Accelerated Robotic Intra-operative Laparoscopic 3D Reconstruction. In: Navab, N., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2010. Lecture Notes in Computer Science, vol 6135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13711-2_9
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DOI: https://doi.org/10.1007/978-3-642-13711-2_9
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