Skip to main content
SpringerLink
Log in
Book cover

Computational Biomechanics for Medicine pp 87–99Cite as

Objective Evaluation of Accuracy of Intra-Operative Neuroimage Registration

  • Conference paper
  • First Online:

Abstract

Pre-operative brain images that are registered onto relevant intra-operative images can enhance navigation during image-guided neurosurgery. One of the crucial steps in the process of image registration is assessment of its accuracy. The accuracy of an image registration procedure was evaluated in one of our previous studies, for five cases of neurosurgery, using a manual segmentation-based method that is subjective and prone to human errors. The aim of this study is to develop an evaluation method that is objective and automatic. An edge-based Hausdorff Distance (HD) metric based on Canny edges was developed for evaluation. Subsequently, the accuracy of non-rigid registration (NRR) results was evaluated using intra-operative images as ground truth and compared with those from the previous study. The obtained results compared well despite the differences in the methods employed. The edge-based HD metric provides an objective measure for image registration accuracy evaluation.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Black, P.: Management of malignant glioma: role of surgery in relation to multimodality therapy. J. Neurovirol. 4, 227–236 (1998)

    Article  Google Scholar 

  2. Nakamura, M., Konishi, N., Tsunoda, S., et al.: Analysis of prognostic and survival factors related to treatment of low-grade astrocytomas in adults. Oncology 58, 108–116 (2000)

    Article  Google Scholar 

  3. Luyken, C., Blümcke, I., Fimmers, R., et al.: The spectrum of long-term epilepsy-associated tumours: long-term seizure and tumour outcome and neurosurgical aspects. Epilepsia 44, 822–830 (2003)

    Article  Google Scholar 

  4. Schiffbauer, H., Ferrari, P., Rowley, H.A., et al.: Functional activity within brain tumours: a magnetic source imaging study. Neurosurgery 49, 1313–1320 (2001)

    Article  Google Scholar 

  5. Miga, M.I., Paulsen, K.D., Lemery, J.M., et al.: Model-updated image guidance: Initial clinical experiences with gravity-induced brain deformation. IEEE Trans. Med. Imag. 18, 866–874 (1999)

    Article  Google Scholar 

  6. Nabavi, A., Black, P.M., Gering, D.T., et al.: Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 48, 787–797 (2001)

    Google Scholar 

  7. Hu, J., Jin, X., Lee, J.B., et al.: Intraoperative brain shift prediction using a 3D inhomogeneous patient-specific finite element model. J. Neurosurg. 106, 164–169 (2007)

    Article  Google Scholar 

  8. Joldes, G.R., Wittek, A., Miller, K.: Computation of intra-operative brain shift using dynamic relaxation. Comput. Meth. Appl. Mech. Eng. 198, 3313–3320 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  9. Joldes, G.R., Wittek, A., Miller, K.: Real-time nonlinear finite element computations on GPU - Application to neurosurgical simulation. Comput. Meth. Appl. Mech. Eng. 199, 3305–3314 (2010)

    Article  MATH  Google Scholar 

  10. Joldes, G.R., Wittek, A., Miller, K.: Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation. Med. Image Anal. 13, 912–919 (2009)

    Article  Google Scholar 

  11. Miller, K., Wittek, A., Joldes, G.: Biomechanical modelling of the brain for computer-assisted neurosurgery. In: Miller, K. (ed.) Biomechanics of the Brain, pp. 111–136. Springer, New York (2011)

    Chapter  Google Scholar 

  12. Miller, K., Wittek, A., Joldes, G., et al.: Modelling brain deformations for computer-integrated neurosurgery. Int. J. Numer. Method Biomed. Eng. 26, 117–138 (2010)

    Article  MATH  Google Scholar 

  13. Wittek, A., Joldes, G., Couton, M., et al.: Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time; application to non-rigid neuroimage registration. Prog. Biophys. Mol. Biol. 103, 292–303 (2010)

    Article  Google Scholar 

  14. Wittek, A., Miller, K., Kikinis, R., et al.: Patient-specific model of brain deformation: application to medical image registration. J. Biomech. 40, 919–929 (2007)

    Article  Google Scholar 

  15. Joldes, GR., Wittek, A., Warfield, SK., et al.: Performing brain image warping using the deformation field predicted by a biomechanical model. In: Nielsen, P.M.F., Miller, K., Wittek, A. (eds.) Computational Biomechanics for Medicine VI Workshop, MICCAI. pp. 89–96. Springer, Toronto (2011)

    Google Scholar 

  16. Warfield, S.K., Talos, F., Tei, A., et al.: Real-time registration of volumetric brain MRI by biomechanical simulation of deformation during image guided neurosurgery. Comput. Visual Sci. 5, 3–11 (2002)

    Article  MATH  Google Scholar 

  17. Warfield, S.K., Haker, S.J., Talos, I.F., et al.: Capturing intraoperative deformations: research experience at Brigham and womens’s hospital. Med. Image Anal. 9, 145–162 (2005)

    Article  Google Scholar 

  18. Fedorov, A., Billet, E., Prastawa, M., et al.: Evaluation of brain MRI alignment with the robust Hausdorff distance measures. In: 4th international symposium on advances in visual computing. pp. 594–603. Springer, Las Vegas (2008)

    Google Scholar 

  19. Klein, A., Andersson, J., Ardekani, B.A., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46, 786–802 (2009)

    Article  Google Scholar 

  20. Rogelj, P., Kovacic, S., Gee, J.C.: Validation of a nonrigid registration algorithm for multimodal data. In: Sonka, M., Fitzpatrick, J.M. (eds.) Medical Imaging 2002. Proceedings of SPIE, pp. 299–307, San Diego (2002)

    Google Scholar 

  21. Wei, Y., Christensen, G.E., Song, J.H., et al.: Evaluation of five non-rigid image registration algorithms using the NIREP framework. In: Dawant, B.M., Haynor, D.R. (eds.) Medical Imaging 2010. Proceedings of the SPIE, pp. 76232L-76232L-10, San Diego (2010)

    Google Scholar 

  22. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)

    Article  Google Scholar 

Download references

Acknowledgements

The first author is a recipient of the SIRF scholarship and gratefully acknowledges financial support of The University of Western Australia (Research Collaboration Award). The financial support of National Health and Medical Research Council (Grant No. APP1006031), National Institute of Health (Grants R01 EB008015 and R01 LM010033) and Children’s Hospital Boston Translational Research Program is gratefully acknowledged. In addition, the authors also gratefully acknowledge the financial support of Neuroimage Analysis Center (NIH P41 EB015902), National Center for Image Guided Therapy (NIH U41 RR019703) and the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.

Author information

Authors and Affiliations

  1. Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia

    Revanth Reddy Garlapati, Grand Roman Joldes, Adam Wittek, Jonathan Lam & Karol Miller

  2. Computational Radiology Lab, Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Neil Weisenfeld, Arne Hans & Simon K. Warfield

  3. Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Ron Kikinis

  4. Institute of Mechanics and Advanced Materials, Cardiff School of Engineering, Cardiff University, Wales, UK

    Karol Miller

Authors
  1. Revanth Reddy Garlapati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Grand Roman Joldes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adam Wittek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jonathan Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Neil Weisenfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Arne Hans
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Simon K. Warfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ron Kikinis
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Karol Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karol Miller .

Editor information

Editors and Affiliations

  1. School of Mechanical & Chem. Engineering, Intelligent Systems for Medicine Lab., The University of Western Australia, Stirling Highway 35, Crawley-Perth, 6009, West Australia, Australia

    Adam Wittek

  2. School of Mechanical & Chem. Engineering, The University of Western Australia, Stirling Highway 35, Crawley-Perth, 6009, West Australia, Australia

    Karol Miller

  3. , Auckland Bioengineering Institute, The University of Auckland, Symonds Street 70, Auckland, 1010, New Zealand

    Poul M.F. Nielsen

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this paper

Cite this paper

Garlapati, R.R. et al. (2013). Objective Evaluation of Accuracy of Intra-Operative Neuroimage Registration. In: Wittek, A., Miller, K., Nielsen, P. (eds) Computational Biomechanics for Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6351-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-6351-1_9

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6350-4

  • Online ISBN: 978-1-4614-6351-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation