Abstract
For surgeons, the precise anatomy structure and its dynamics are important in the surgery interaction, which is critical for generating the immersive experience in VR based surgical training applications. Presently, a normal therapeutic scheme might not be able to be straightforwardly applied to a specific patient, because the diagnostic results are based on averages, which result in a rough solution. Patient Specific Modeling (PSM), using patient-specific medical image data (e.g. CT, MRI, or Ultrasound), could deliver a computational anatomical model. It provides the potential for surgeons to practice the operation procedures for a particular patient, which will improve the accuracy of diagnosis and treatment, thus enhance the prophetic ability of VR simulation framework and raise the patient care. This paper presents a general review based on existing literature of patient specific surgical simulation on data acquisition, medical image segmentation, computational mesh generation, and soft tissue real time simulation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)
Alliez, P., Cohen-Steiner, D., Yvinec, M., Desbrun, M.: Variational tetrahedral meshing. ACM Trans. Graph. (TOG) 24, 617–625 (2005). ACM
Antiga, L., Piccinelli, M., Botti, L., Ene-Iordache, B., Remuzzi, A., Steinman, D.A.: An image-based modeling framework for patient-specific computational hemodynamics. Med. Biol. Eng. Comput. 46(11), 1097 (2008)
Badash, I., Burtt, K., Solorzano, C.A., Carey, J.N.: Innovations in surgery simulation: a review of past, current and future techniques. Ann. Transl. Med. 4(23), 453 (2016)
Baraff, D., Witkin, A.: Large steps in cloth simulation. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, pp. 43–54. ACM (1998)
Barratt, D.C., Chan, C.S., Edwards, P.J., Penney, G.P., Slomczykowski, M., Carter, T.J., Hawkes, D.J.: Instantiation and registration of statistical shape models of the femur and pelvis using 3d ultrasound imaging. Med. Image Anal. 12(3), 358–374 (2008)
Bender, J., Koschier, D., Charrier, P., Weber, D.: Position-based simulation of continuous materials. Comput. Graph. 44, 1–10 (2014)
Boltcheva, D., Yvinec, M., Boissonnat, J.D.: Mesh generation from 3d multi-material images. In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, pp. 283–290 (2009)
Bonet, J., Burton, A.: A simple average nodal pressure tetrahedral element for incompressible and nearly incompressible dynamic explicit applications. Int. J. Numer. Meth. Biomed. Eng. 14(5), 437–449 (1998)
Bouaziz, S., Martin, S., Liu, T., Kavan, L., Pauly, M.: Projective dynamics: fusing constraint projections for fast simulation. ACM Trans. Graph. (TOG) 33(4), 154 (2014)
Bryson, S.: Virtual reality in scientific visualization. Commun. ACM 39(5), 62–71 (1996)
Cevidanes, L.H., Tucker, S., Styner, M., Kim, H., Chapuis, J., Reyes, M., Proffit, W., Turvey, T., Jaskolka, M.: Three-dimensional surgical simulation. Am. J. Orthod. Dentofac. Orthop. 138(3), 361–371 (2010)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998). doi:10.1007/BFb0054760
Cootes, T.F., Taylor, C.J.: Active shape models-‘smart snakes’. In: Hogg, D., Boyle, R. (eds.) BMVC 1992, pp. 266–275. Springer, London (1992)
Davis, J.E.: The use of simulation in causal analysis of sentinel events in healthcare. Ph.D. thesis, University of Pennsylvania (2016)
Duffy, A., Hogle, N., McCarthy, H., Lew, J., Egan, A., Christos, P., Fowler, D.: Construct validity for the LapSim laparoscopic surgical simulator. Surg. Endosc. Interv. Tech. 19(3), 401–405 (2005)
Endo, K., Sata, N., Ishiguro, Y., Miki, A., Sasanuma, H., Sakuma, Y., Shimizu, A., Hyodo, M., Lefor, A., Yasuda, Y.: A patient-specific surgical simulator using preoperative imaging data: an interactive simulator using a three-dimensional tactile mouse. J. Comput. Surg. 1(1), 10 (2014)
Eschweiler, J., Stromps, J.P., Fischer, M., Schick, F., Rath, B., Pallua, N., Radermacher, K.: A biomechanical model of the wrist joint for patient-specific model guided surgical therapy: part 2. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 230(4), 326–334 (2016)
Eschweiler, J., Stromps, J.P., Fischer, M., Schick, F., Rath, B., Pallua, N., Radermacher, K.: Development of a biomechanical model of the wrist joint for patient-specific model guided surgical therapy planning: part 1. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 230(4), 310–325 (2016)
Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., et al.: 3d slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imag. 30(9), 1323–1341 (2012)
Gallagher, A.G., Ritter, E.M., Champion, H., Higgins, G., Fried, M.P., Moses, G., Smith, C.D., Satava, R.M.: Virtual reality simulation for the operating room: proficiency-based training as a paradigm shift in surgical skills training. Ann. Surg. 241(2), 364–372 (2005)
Goel, V.R., Greenberg, R.K., Greenberg, D.P.: Mathematical analysis of DICOM CT datasets: can endograft sizing be automated for complex anatomy? J. Vasc. Surg. 47(6), 1306–1312 (2008)
Gonzalez, R., Wintz, P.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (1977)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Royal Stat. Soc. Ser. C (Applied Statistics) 28(1), 100–108 (1979)
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Heimann, T., Meinzer, H.P.: Statistical shape models for 3d medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)
Indira, S., Ramesh, A.: Image segmentation using artificial neural network and genetic algorithm: a comparative analysis. In: 2011 International Conference on Process Automation, Control and Computing (PACC), pp. 1–6. IEEE (2011)
Iwamoto, N., Shum, H.P., Yang, L., Morishima, S.: Multi-layer lattice model for real-time dynamic character deformation. Comput. Graph. Forum 34, 99–109 (2015). Wiley Online Library
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision, vol. 5. McGraw-Hill, New York (1995)
Jamin, C., Alliez, P., Yvinec, M., Boissonnat, J.D.: CGALmesh: a generic framework for delaunay mesh generation. ACM Trans. Math. Softw. (TOMS) 41(4), 23 (2015)
Johnson, C.: Biomedical visual computing: case studies and challenges. Comput. Sci. Eng. 14(1), 12–21 (2012)
Kent, D.M., Hayward, R.A.: Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA 298(10), 1209–1212 (2007)
Lai, J.Y., Essomba, T., Lee, P.Y., et al.: Algorithm for segmentation and reduction of fractured bones in computer-aided preoperative surgery. In: Proceedings of the 3rd International Conference on Biomedical and Bioinformatics Engineering, pp. 12–18. ACM (2016)
Leea, C.K., Mihaib, L.A., Halec, J.S., Kerfridena, P., Bordasc, S.P.: Strain smoothing for compressible and nearly-incompressible finite elasticity. Comput. Struct. 182, 540–555 (2016)
Lei, T., Sewchand, W.: Statistical approach to X-ray CT imaging and its applications in image analysis. II. A new stochastic model-based image segmentation technique for X-ray CT image. IEEE Trans. Med. Imag. 11(1), 62–69 (1992)
Liu, T., Bargteil, A.W., O’Brien, J.F., Kavan, L.: Fast simulation of mass-spring systems. ACM Trans. Graph. (TOG) 32(6), 214 (2013)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. ACM SIGGRAPH Comput. Graph. 21, 163–169 (1987). ACM
Makiyama, K., Nagasaka, M., Inuiya, T., Takanami, K., Ogata, M., Kubota, Y.: Development of a patient-specific simulator for laparoscopic renal surgery. Int. J. Urol. 19(9), 829–835 (2012)
Marr, D., Hildreth, E.: Theory of edge detection. Proc. Royal Soc. Lond. B Biol. Sci. 207(1167), 187–217 (1980)
Mihalef, V., Ionasec, R.I., Sharma, P., Georgescu, B., Voigt, I., Suehling, M., Comaniciu, D.: Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four-dimensional cardiac CT images. Interface Focus 1(3), 286–296 (2011)
Miller, K.: Biomechanics of Brain for Computer Integrated Surgery. Warsaw University of Technology Publishing House, Warsaw (2002)
Mohamed, A., Davatzikos, C.: Finite element mesh generation and remeshing from segmented medical images. In: 2004 IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 420–423. IEEE (2004)
Müller, M., Heidelberger, B., Hennix, M., Ratcliff, J.: Position based dynamics. J. Vis. Commun. Image Represent. 18(2), 109–118 (2007)
Neal, M.L., Kerckhoffs, R.: Current progress in patient-specific modeling. Briefings Bioinform. 11(1), 111–126 (2010)
Nolden, M., Zelzer, S., Seitel, A., Wald, D., Müller, M., Franz, A.M., Maleike, D., Fangerau, M., Baumhauer, M., Maier-Hein, L., et al.: The medical imaging interaction toolkit: challenges and advances. Int. J. Comput. Assist. Radiol. Surg. 8(4), 607–620 (2013)
de Oliveira, J.E., Giessler, P., Deserno, T.M.: Patient-specific anatomical modelling. In: E-Health and Bioengineering Conference (EHB), pp. 1–4. IEEE (2015)
O’Reilly, M.A., Whyne, C.M.: Comparison of computed tomography based parametric and patient-specific finite element models of the healthy and metastatic spine using a mesh-morphing algorithm. Spine 33(17), 1876–1881 (2008)
Otaduy, M.A., Bickel, B., Bradley, D., Wang, H.: Data-driven simulation methods in computer graphics: cloth, tissue and faces. In: ACM SIGGRAPH 2012 Courses, p. 12. ACM (2012)
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognit. 26(9), 1277–1294 (1993)
Pan, J.J., Chang, J., Yang, X., Liang, H., Zhang, J.J., Qureshi, T., Howell, R., Hickish, T.: Virtual reality training and assessment in laparoscopic rectum surgery. Int. J. Med. Rob. Comput. Assist. Surg. 11(2), 194–209 (2015)
Prewitt, J.M.: Object enhancement and extraction. Picture Process. Psychopictorics 10(1), 15–19 (1970)
Ricotta, J.J., Pagan, J., Xenos, M., Alemu, Y., Einav, S., Bluestein, D.: Cardiovascular disease management: the need for better diagnostics. Med. Biol. Eng. Comput. 46(11), 1059–1068 (2008)
Rineau, L., Yvinec, M.: Meshing 3d domains bounded by piecewise smooth surfaces. In: Brewer, M.L., Marcum, D. (eds.) Proceedings of the 16th International Meshing Roundtable, pp. 443–460. Springer, Heidelberg (2008)
Schöberl, J.: Netgen an advancing front 2d/3d-mesh generator based on abstract rules. Comput. Vis. Sci. 1(1), 41–52 (1997)
Sifakis, E., Barbic, J.: FEM simulation of 3d deformable solids: a practitioner’s guide to theory, discretization and model reduction. In: ACM SIGGRAPH 2012 Courses, p. 20. ACM (2012)
Viceconti, M., Davinelli, M., Taddei, F., Cappello, A.: Automatic generation of accurate subject-specific bone finite element models to be used in clinical studies. J. Biomech. 37(10), 1597–1605 (2004)
Weatherill, N.P., Hassan, O.: Efficient three-dimensional delaunay triangulation with automatic point creation and imposed boundary constraints. Int. J. Numer. Meth. Eng. 37(12), 2005–2039 (1994)
Zhang, A., Hünerbein, M., Dai, Y., Schlag, P.M., Beller, S.: Construct validity testing of a laparoscopic surgery simulator (lap mentor®). Surg. Endosc. 22(6), 1440–1444 (2008)
Zhang, Y., Hughes, T.J., Bajaj, C.L.: An automatic 3d mesh generation method for domains with multiple materials. Comput. Meth. Appl. Mech. Eng. 199(5), 405–415 (2010)
Zienkiewicz, O.C., Taylor, R.L.: The Finite Element Method for Solid and Structural Mechanics. Butterworth-Heinemann, Oxford (2005)
Acknowledgements
We would also like to thank the People Program (Marie Curie Actions) of the European Union’s Seventh Framework Program FP7/2007-2013/ under REA grant agreement n\(^{\circ }\) [612627] for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhang, J., Chang, J., Yang, X., Zhang, J.J. (2017). Virtual Reality Surgery Simulation: A Survey on Patient Specific Solution. In: Chang, J., Zhang, J., Magnenat Thalmann, N., Hu, SM., Tong, R., Wang, W. (eds) Next Generation Computer Animation Techniques. AniNex 2017. Lecture Notes in Computer Science(), vol 10582. Springer, Cham. https://doi.org/10.1007/978-3-319-69487-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-69487-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69486-3
Online ISBN: 978-3-319-69487-0
eBook Packages: Computer ScienceComputer Science (R0)