Skip to main content
Log in

Abstract

Purpose Optimal display of surgical planning data in the operating room is challenging. In liver surgery, an expressive and effective intraoperative visualization of 3D planning models is still a pressing need. The objective of this work is to visualize surgical planning information using a map display.

Methods An approach for risk analysis and visualization of planning models is presented which provides relevant information at a glance without the need for user interaction. Therefore, we present methods for the identification and classification of critical anatomical structures in the proximity of a preoperatively planned resection surface. Shadow-like distance indicators are introduced to encode the distance from the resection surface to these critical structures on a risk map. The work is demonstrated with examples in liver resection surgery and evaluated within two user studies.

Results The results of the performed user studies show that the proposed visualization techniques facilitate the process of risk assessment in liver resection surgery and might be a valuable extension to surgical navigations system.

Conclusion The approach provides a new and objective basis for the assessment of risks during liver surgery and has the potential to improve the outcome of surgical interventions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Cash DM, Miga MI, Glasgow SC, Dawant BM, Clements LW, Cao Z, Galloway RL, Chapman WC (2007) Concepts and preliminary data toward the realization of image-guided liver surgery. J Gastrointest Surg 11(7):844–859

    Google Scholar 

  2. Beller S, Eulenstein S, Lange T, Hünerbein M, Schlag PM (2009) Upgrade of an optical navigation system with a permanent electromagnetic position control: a first step towards “navigated control” for liver surgery. J Hepatobiliary Pancreat Surg 16(2):165–170

    Google Scholar 

  3. Peterhans M, vom Berg A, Dagon B, Inderbitzin D, Baur C, Candinas D, Weber S (2011) A navigation system for open liver surgery: design, workflow and first clinical applications. Int J Med Robot 7(1):7–16

    Article  PubMed  CAS  Google Scholar 

  4. Wein W, Brunke S, Khamene A, Callstrom MR, Navab N (2008) Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12(5):577–885

    Article  PubMed  Google Scholar 

  5. Lange T, Papenberg N, Heldmann S, Modersitzki J, Fischer B, Lamecker H, Schlag PM (2009) 3D ultrasound-CT registration of the liver using combined landmark-intensity information. Int J Comput Assist Radiol Surg 4(1):79–88

    Article  PubMed  Google Scholar 

  6. Clements LW, Chapman WC, Dawant BM, Galloway RL, Miga MI (2008) Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation. Med Phys 35(6):2528–2540

    Article  PubMed  Google Scholar 

  7. Maier-Hein L, Schmidt M, Franz AM, dos Santos TR, Seitel A, Jähne B, Fitzpatrick JM, Meinzer HP (2010) Accounting for anisotropic noise in fine registration of time-of-flight range data with high-resolution surface data. Med Image Comput Comput Assist Interv 13(Pt 1):251–258

    PubMed  CAS  Google Scholar 

  8. Hurdal MK, Stephenson K (2009) Discrete conformal methods for cortical brain flattening. Neuroimage 45:86–98

    Article  Google Scholar 

  9. Yao J, Chowdhury AS, Aman J, Summers RM (2010) Reversible projection technique for colon unfolding. IEEE Trans Biomed Eng 57:2861–2869

    Article  PubMed  Google Scholar 

  10. Zeng W, Marino J, Kaufman A, Gu XD (2011) Volumetric colon wall unfolding using harmonic differentials. Comput Graph 35:726–732

    Article  PubMed  Google Scholar 

  11. Navkar NV, Tsekos NV, Stafford JR, Weinberg JS, Deng Z (2010) Visualization and planning of neurosurgical interventions with straight access. In: Proceedings of the 1st international conference on information processing in computer-assisted interventions. Springer, Berlin, pp 1–11

  12. Termeer M, Olivan Bescos J (2007) CoViCAD: comprehensive visualization of coronary artery disease. IEEE Trans Vis Comput Graph 13:1632–1639

    Article  PubMed  Google Scholar 

  13. Rieder C, Weihusen A, Schumann C, Zidowitz S, Peitgen H-O (2010) Visual support for interactive post-interventional assessment of radiofrequency ablation therapy. Comput Graph Forum (Spec Issue Eurographics Symp Visual) 29(3):1093–1102

    Article  Google Scholar 

  14. Neugebauer M, Gasteiger R, Beuing O, Diehl V, Skalej M, Preim B (2009) Map displays for the analysis of scalar data on cerebral aneurysm surfaces. Comput Graph Forum (EuroVis) 28(3):895–902

    Article  Google Scholar 

  15. Lamata P, Jalote-Parmar A, Lamata F, Declerck J (2008) The resection map, a proposal for intraoperative hepatectomy guidance. Int J Comput Assist Radiol Surg 3(3–4):299

    Article  Google Scholar 

  16. Lamata P, Lamata F, Sojar V, Makowski P, Massoptier L, Casciaro S, Ali W et al (2010) Use of the resection map system as guidance during hepatectomy. Surg Endosc 24(9):2327–2337

    Article  PubMed  Google Scholar 

  17. Hansen C, Zidowitz S, Schenk A, Oldhafer K, Lang H, Peitgen H-O (2010) Risk maps for navigation in liver surgery. In: Proceedings of SPIE medical, imaging. 7625, pp \(\text{762528}\_\text{1}\)-8

  18. Ritter F, Boskamp T, Homeyer A, Laue H, Schwier M, Link F, Peitgen HO (2011) Medical image analysis. IEEE Pulse 2:60–70

    Article  PubMed  Google Scholar 

  19. Beller S, Eulenstein S, Lange T, Niederstrasser M, Hünerbein M, Schlag PM (2009a) A new measure to assess the diffculty of liver resection. Eur J Surg Oncol 35(1):59–64

    Article  PubMed  CAS  Google Scholar 

  20. van den Broek MAJ, Olde Damink SWM, Dejong CHC, Lang H, Malago M, Jalan R, Saner FH (2008) Liver failure after partial hepatic resection: definition, pathophysiology, risk factors and treatment. Liver Inter Off J Inter Assoc Study Liver 28(6):767–780

    Article  Google Scholar 

  21. Hansen C, Zidowitz S, Hindennach M, Schenk A, Hahn H, Peitgen H-O (2009) Interactive determination of robust safety margins for oncologic liver surgery. Int J Comput Assist Radiol Surg 4(5):469–474

    Article  PubMed  Google Scholar 

  22. Black D (2010) Auditory display for liver surgery. MA thesis. Department of Computer Science, University of Applied Sciences Bremen, Germany

Download references

Acknowledgments

The authors express gratitude to all involved physicians, in particular Nicholas Wendt (Sankt Josef Hospital, Bremen, Germany), Gregor Stavrou, Human Honarpisheh (Asklepios Hospital Barmbek, Hamburg, Germany), Marcello Donati (University of Catania, Italy), and Hauke Lang (University Hospital Mainz, Germany) for the fruitful discussions.

Conflict of interest

There is no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christian Hansen.

Additional information

This paper won the NDI Best Paper Student Award at the CARS 2012 Congress in Pisa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hansen, C., Zidowitz, S., Ritter, F. et al. Risk maps for liver surgery. Int J CARS 8, 419–428 (2013). https://doi.org/10.1007/s11548-012-0790-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-012-0790-6

Keywords

Navigation