Computational Support for Intraoperative Imaging and IGT

  • Orçun Göksel
  • Gábor Székely


Technology and computers are omnipresent in our daily lives today, in which they play a decisive role, even if they are often invisible, seamlessly integrated into our surrounding and the objects we use every day. Image-guided therapy is certainly not an exception to this. Nowadays practically all imaging devices rely on computer support, commonly used for the purpose of controlling medical devices, for post-processing acquired raw data to turn them into images, and for transmitting resulting digital data for storage or further use. Furthermore, many therapeutic devices are equipped with sophisticated sensors and actuators, which are also controlled by computers. Indeed, software support today is an intrinsic component of all phases of therapy.

In this chapter, we provide an overview of tools and technologies that are used as the building blocks of almost every computational support system in image-guided therapy. The tools introduced in this chapter include segmentation, registration, localization, simulation, model generation, visualization, robotic tools, and man–machine interfaces. The use of such tools in preoperative planning and intraoperative surgical support is then described and exemplified on typical treatment scenarios.


Augmented Reality Imaging Device Volume Rendering Haptic Device Prostate Brachytherapy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bankman Isaac N, editor. Handbook of medical imaging: processing and analysis. 2nd ed. Amsterdam: Elsevier; 2009.Google Scholar
  2. 2.
    Milan S, Michael FJ, editors. Handbook of medical imaging, Medical image processing and analysis, vol. 2. Bellingham: SPIE; 2009.Google Scholar
  3. 3.
    Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng. 2000;2:315–37.PubMedCrossRefGoogle Scholar
  4. 4.
    McInerney T, Terzopoulos D. Deformable models in medical image analysis: a survey. Med Image Anal. 1996;1:91–108.PubMedCrossRefGoogle Scholar
  5. 5.
    Goksel O, Salcudean SE. Automatic prostate segmentation from transrectal ultrasound elastography images using geometric active contours. In: International conference on ultrasonic measurement and imaging of tissue elasticity (ITEC), Vlissingen. 2009; p. 34.Google Scholar
  6. 6.
    Tobias H, Hans-Peter M. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13:543–63.CrossRefGoogle Scholar
  7. 7.
    Maintz JB, Viergever MA. A survey of medical image registration. Med Image Anal. 1998;2:1–36.PubMedCrossRefGoogle Scholar
  8. 8.
    Crum WR, Hartkens T, Hill DLG. Non-rigid image registration: theory and practice. Br J Radiol. 2004;77:140–53.CrossRefGoogle Scholar
  9. 9.
    Kessler ML. Image registration and data fusion in radiation therapy. Br J Radiol. 2006;79:99–108.CrossRefGoogle Scholar
  10. 10.
    von Siebenthal M, Székely G, Lomax A, Cattin P. Systematic errors in respiratory gating due to intrafraction deformations of the liver. Med Phys. 2007;34:3620–9.CrossRefGoogle Scholar
  11. 11.
    Boykov Y, Funka-Lea G. Graph cuts and efficient N-D image segmentation. Int J Comput Vision. 2006;70:109–31.CrossRefGoogle Scholar
  12. 12.
    Pohl KM, Fisher J, Grimson WEL, Kikinis R, Wells WM. A Bayesian model for joint segmentation and registration. Neuroimage. 2006;31:228–39.PubMedCrossRefGoogle Scholar
  13. 13.
    Mascott CR. Comparison of magnetic tracking and optical tracking by simultaneous use of two independent frameless stereotactic systems. Neurosurgery. 2005;57:295–301.PubMedCrossRefGoogle Scholar
  14. 14.
    Goksel O, Salcudean SE. B-mode ultrasound image simulation in deformable 3-D medium. IEEE Trans Med Imaging. 2009;28:1657–69.PubMedCrossRefGoogle Scholar
  15. 15.
    The NSR Physiome Project. Accessed Aug 2013.
  16. 16.
    The Virtual Physiological Human. Accessed Aug 2013.
  17. 17.
    Metter RL, Beutel J, Kundel HL, editors. Handbook of medical imaging, Physics and psychophysics. Bellingham: SPIE; 2000.Google Scholar
  18. 18.
    Goksel O, Sapchuk K, Salcudean SE. Haptic simulator for prostate brachytherapy with simulated needle and probe interaction. IEEE Trans Haptics. 2011;4:188–98.CrossRefGoogle Scholar
  19. 19.
    Dodgson NA. Autostereoscopic 3D displays. IEEE Comput. 2005;38:31–6.CrossRefGoogle Scholar
  20. 20.
    Holliman NS. Three-dimensional display systems. In: Dakin JP, Brown RGW, editors. Handbook of optoelectronics, vol. 2. New York/London: Taylor & Francis; 2006. p. 1067–100.Google Scholar
  21. 21.
    Intuitive Surgical, Inc. Accessed Aug 2013.
  22. 22.
    Alan W. 3D computer graphics. 3rd ed. Harlow: Addison-Wesley; 2000.Google Scholar
  23. 23.
    Engel K, Hadwiger M, Kniss JM, Lefohn AE, Salama CR, Weiskopf D. Real-time volume graphics in ACM SIGGRAPH 2004 “Course Notes 28”. 2004.Google Scholar
  24. 24.
    van der Meijden OAJ, Schijven MP. The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training: a current review. Surg Endosc. 2009;23:1180–90.PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Robodoc. Accessed Aug 2013.
  26. 26.
    Moustris GP, Hiridis SC, Deliparaschos KM, Konstantinidis KM. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int J Med Robot. 2011;7(4):375–92.PubMedGoogle Scholar
  27. 27.
    Nicolau S, Soler L, Mutter D, Marescaux J. Augmented reality in laparoscopic surgical oncology. Surg Oncol. 2011;20:189–201. Special Issue: Education for Cancer.PubMedCrossRefGoogle Scholar
  28. 28.
    Fürnstahl P, Székely G, Gerber C, Hodler J, Snedeker JG, Harders M. Computer assisted reconstruction of complex proximal humerus fractures for preoperative planning. Med Image Anal. 2012;16(3):704–20.PubMedCrossRefGoogle Scholar
  29. 29.
    Maciunas RJ. Interactive image-guided neurosurgery. Park Ridge: American Association of Neurological Surgeons; 1993.Google Scholar
  30. 30.
    McBeth PB, Louw DF, Rizun PR, Sutherland GR. Robotics in neurosurgery. Am J Surg. 2004;188:68–75.CrossRefGoogle Scholar
  31. 31.
    Morel A. Stereotactic atlas of the human thalamus and basal ganglia. New York: Informa Healthcare; 2007.CrossRefGoogle Scholar
  32. 32.
    Krauth A, Blanc R, Poveda A, Jeanmonod D, Morel A, Székely G. A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage. 2010;49:2053–62.PubMedCrossRefGoogle Scholar
  33. 33.
    Alexander E, Maciunas RJ, editors. Advanced neurosurgical navigation. New York: Thieme; 1999.Google Scholar
  34. 34.
    Nolte LP, Beutler T. Basic principles of CAOS. Int J Care Injured. 2004;35:6–16.CrossRefGoogle Scholar
  35. 35.
    Mortele KJ, Tuncali K, Cantisani V, et al. MRI-guided abdominal intervention. Abdom Imaging. 2003;28:756–74.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Computer Vision Laboratory, ETH ZürichZürichSwitzerland

Personalised recommendations