• Joey Sing Yee TanEmail author
  • Amandeep S. Sidhu
Part of the Studies in Computational Intelligence book series (SCI, volume 832)


Over the past few years, surgical simulation has emerged as an alternative medical training or pre-operation planning method in the medical field. During the early stage of surgical training, novice surgeons used to practice via animals, cadavers and real patient. Each of these methods face challenges in terms of cost, availability, ethical restriction and realistic. The animals’ organs or soft tissues do not accurately represent the human anatomy, particularly the measurement of the organs’ sizes.


  1. Aebersold, M., Tschannen, D., & Bathish, M. (2012). Innovative simulation strategies in education. Nursing Research and Practice, 7.Google Scholar
  2. Ahn, B., & Kim, J. (2010). Measurement and characterization of soft tissue behavior with surface deformation and force response under large deformations. Medical Image Analysis, 14(2), 138–148.CrossRefGoogle Scholar
  3. Basafa, E., & Farahmand, F. (2011). Real-time simulation of the nonlinear visco-elastic deformations of soft tissues. International Journal of Computer Asisted Radiology and Surgery, 6(3), 297–307.Google Scholar
  4. Bianchi, G., Harders, M., & Szekely, G. (2003). Mesh topology identification for mass-spring models. In MICCAI 2003 (Vol. 1, pp. 50–58).Google Scholar
  5. Choi, K. S. (2010). Toward realistic virtual surgical simulation: Using heuristically parametrized anisotropic mass-spring model to simulate tissue mechanical responses. In 2010 2nd International Conference on Education Technology and Computer (ICETC) (pp. V1-446–V1-450), 22–24 June 2010.Google Scholar
  6. Cotin, S., Delingette, H., & Ayache, N. (1999). Real-time elastic deformations of soft tissues for surgery simulation. IEEE Transactions on Visualization and Computer Graphics, 5:62–73.Google Scholar
  7. Courtecuisse, H., & Jung, H. (2010). GPU-based real-time soft tissue deformation with cutting and haptic feedback. Progress in Biophysics and Molecular Biology, 3, 159–168.CrossRefGoogle Scholar
  8. De, S., & Srinivasan, M.A. (1999). Thin walled models for haptic and graphical rendering of soft tissues in surgical simulations. Medicine Meets Virtual Reality, J.D. Westwood et al. (Eds.), IOS Press, 1999, (pp. 94–99).Google Scholar
  9. Delingette, H., & Ayache, N. (2004). Soft tissue modeling for surgery simulation. Computational Models for the Human Body, 12, 453–550.MathSciNetCrossRefGoogle Scholar
  10. Delp, S., Loan, P., Basdogan, C., & Rosen, J. (1997). Surgical simulation: An emerging technology for training in emergency medicine. Teleoperators and Virtual Environments, 6(2), 147–159.CrossRefGoogle Scholar
  11. Deussen, O., Kobbelt, L., & Tücke, P. (1995). Using simulated annealing to obtain good approximations of deformable bodies. In D. Terzopoulost & D. Thalmann (Eds.), Proceedings of the EuroGraphics Workshop Computer Animation and Simulation. New York: Springer.Google Scholar
  12. Er, M. J., & Sun, Y. L. (2001). Hybrid fuzzy proportional integral plus conventional derivative control of linear and nonlinear system. IEEE Transactions on Industrial Electronics, 48(6), 1109–1117.Google Scholar
  13. Etheredge, C. E. (2011). A parallel mass-spring model for soft tissue simulation with haptic rendering in CUDA. In 15th Twente Student Conference on IT, 15.Google Scholar
  14. Fung, Y. C. (1993). Biomechanics: Mechanical properties of living tissues. New York, NY, USA: Springer.CrossRefGoogle Scholar
  15. Georgii, J., & Westermann, R. (2005). Mass-spring systems on the GPU. Simulation Modelling Practice and Theory 13(8) (11//2005), 693–702.Google Scholar
  16. Halic, T., Kockara, S., Bayrak, C., Rowe, R., & Chen, B. (2009). Soft tissue deformation and optimized data structures for mass spring methods. International Conference on Bioinformatics and BioEngineering, 45–52.Google Scholar
  17. Hu, T. (2006). Reality-based soft tissue probing: Experiments and computational model for application to minimally invasive surgery. Dissertation for Degree of Doctor of Philosophy. Drexel University.Google Scholar
  18. Indelicato, D. (1995). Virtual reality in surgical training, 21–24.Google Scholar
  19. Leon, C. A. D., Eliuk, S., & Gomez, H. T. (2010). Simulating soft tissues using a GPU approach of the mass-spring model. Paper presented at the Virtual Reality Conference (VR), March 20–24, 2010. IEEE.Google Scholar
  20. Maciel, A., Boulic, R., & Thalmann, D. (2003). Deformable tissue parameterized by properties of real biological tissue. In Proceedings of the International Symposium on Surgery Simulation and Soft Tissue Modelling (pp. 74–87).Google Scholar
  21. Marchesseau, S., Heimann, T., Chatelin, S., Willinger, R., & Delingette, H. (2010). Fast porous visco-hyperelastic soft tissue model for surgery simulation: Application to liver surgery. Progress in Biophysics and Molecular Biology, 103(2–3), 185–196.CrossRefGoogle Scholar
  22. Natsupakpong, S. (2010). Physically based modeling and simulation for virtual environment. Dissertation for Degree of Doctor of Philosophy. Case Western Reserve University.Google Scholar
  23. Natsupakpong, S., & Cavusoglu, M. C. (2008). Comparison of numerical integration methods for simulation of physically-based deformable object models in surgical simulation. In National Symposium on Computational Science and Engineering (pp. 27–29).Google Scholar
  24. Nesme, M., Faure, F., & Yohan, F. (2009). Accurate interactive animation of deformable models at arbitrary resolution. International Journal of Image and Graphics, 10(13), 1–20.MathSciNetGoogle Scholar
  25. Rasmusson, A., Mosegaard, J., & Sangild, T. (2008). Exploring parallel algorithms for volumetric mass-spring-damper models in CUDA. In Proceedings of the 4th international symposium on Biomedical Simulation (pp. 49–58). London, UK: Springer.Google Scholar
  26. Rosen, K. R. (2008). The history of medical simulation. Journal of Critical Care, 23, 157–166.CrossRefGoogle Scholar
  27. Sala, A., Turini, G., Ferrari, M., Mosca, F., & Ferrari, V. (2011a). Integration of biomechanical parameters in tetrahedral mass-spring models for virtual surgery simulation. In Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference (pp. 4550–4554).Google Scholar
  28. San Vincent, O. (2011). Designing deformable models of soft tissue for virtual surgery planning and simulation using the mass-spring model. Dissertation for Degree of Doctor of Philosophy, University of Navarra.Google Scholar
  29. Schaverien, M. V. (2010). Development of expertise in surgical training. Journal of Surgical Education, 67(1), 37–43.CrossRefGoogle Scholar
  30. Tejada, E., & Ertl, T. (2005). Large steps in GPU-based deformable bodies simulation. In Simulation Modelling Practice and Theory, 13(8), 703–15. Elsevier.Google Scholar
  31. Terzopoulus, D., & Witkin, A. P. (1988). Deformable models. IEEE CGA, 8(6), 41–51.Google Scholar
  32. Teschner, M., Heidelberger, B., Muller, M. & Gross, M. (2004). A versatile and robust model for geometrically complex deformable solids. Proceedings of the Computer Graphics International, 2004. (pp. 312–319). IEEE.Google Scholar
  33. Van Gelder, A. (1998). Approximate simulation of elastic membranes by triangulated spring meshes. Journal of Graphics Tools, 3, 21–42.CrossRefGoogle Scholar
  34. Vollinger, U., Setier, H., Priesnitz, J., & Krause, F. L. (2009). Evolutionary optimization of mass-spring models. Journal of Manufacturing Science and Technology, 1(3), 137–141.CrossRefGoogle Scholar
  35. Xu, S., Liu, X. P., Member, S., Zhang, H., & Hu, L. (2011). A nonlinear viscoelastic tensor mass visual model for surgery simulation. IEEE Transactions on Instrumentation and Measurement, 60(1), 14–20.CrossRefGoogle Scholar
  36. Xu, S., Liu, X. P., Member, S., Zhang, H., Member, S., & Hu, L. (2010). An improved realistic mass-spring model for surgery simulation. In International Conference on Automation and Logistics (pp. 1–6).Google Scholar
  37. Yamamoto, T. (2011). Applying tissue models in teleoperated robot-assisted surgery. Dissertation for Degree of Doctor of Philosophy. Johns Hopkins University.Google Scholar
  38. Zhang, Y., Zhao, J., Yuan, Z., Ding, Y., Long, C., & Xiong, L. (2010). CUDA based GPU programming to simulate 3D tissue deformation. International Conference on Biomedical Engineering and Computer Science, 1, 1–5.Google Scholar
  39. Zhang, J., Zhou, J., Huang, W., Qin, J., Yang, T., Liu, J., Su, Y., Chui, C.K., & Chang, S. (2013). GPU-friendly gallbladder modeling in laparoscopic cholecystectomy surgical training system. Computers & Electrical Engineering, 39(1) (1//2013), 122–29. 2MSM LT 4-7 (2013).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Complexity InstituteNanyang Technological UniversitySingaporeSingapore
  2. 2.Biological Mapping Research Institute (BIOMAP)PerthAustralia

Personalised recommendations