Tracking Cortical Surface Deformation Using Stereovision

  • Songbai Ji
  • Xiaoyao Fan
  • David W. Roberts
  • Alex Hartov
  • Keith D. Paulsen
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Tracking brain deformation is important for the understanding of brain biomechanical properties. However, accurate deformation tracking may be challenging especially in vivo. We present a completely noninvasive technique to track cortical surface deformation using intraoperative stereovision during open cranial neurosurgery. A sequence of stereo images was acquired to capture motion of the exposed cortical surface due to blood pressure pulsation during multiple respiration cycles for patients undergoing brain tumor resection surgery. Rigid registration was performed between the first and subsequent frames using features outside the craniotomy to compensate for accidental motion of the surgical microscope. A nonrigid registration based on optical flow was performed next between the first and subsequent frames. A reference image was then generated using pixel displacements averaged across an integer multiple of respiration cycles to serve as a reference state based on which a dense displacement field was determined for each image frame. The resulting displacement field was then locally smoothed to minimize noise, and was further spatially differentiated to compute in-plane surface strain using deformation gradient. The technique offers an effective approach to track deformation of soft tissue surface as long as sufficient tracking features are available, which is useful for soft tissue biomechanical characterization.


Cortical surface deformation Cortical surface strain Stereovision Optical flow 



This work was supported in part by National Institutes of Health grant number R01 CA159324–01 awarded by the National Cancer Institute.


  1. 1.
    Fan X, Ji S, Hartov A, Roberts D, Paulsen K (2012) Registering stereovision surface with preoperative magnetic resonance images for brain shift compensation In: Holmes DR III, Wong KH (eds) Medical imaging 2012: image-guided procedures, robotic interventions, and modeling. Proceedings of SPIE, vol 8316 (SPIE, Bellingham, WA 2012) 83161CGoogle Scholar
  2. 2.
    Sun H, Lunn KE, Farid H, Wu Z, Roberts DW, Hartov A, Paulsen KD (2005) Stereopsis-guided brain shift compensation. IEEE Trans Med Imag 24(8):1039–1052CrossRefGoogle Scholar
  3. 3.
    Paul P, Morandi X, Jannin P (2009) A surface registration method for quantification of intraoperative brain deformations in image-guided neurosurgery. IEEE Trans Info Tech Biomed 13(6):976–983CrossRefGoogle Scholar
  4. 4.
    Ji S, Hartov A, Roberts DW, Paulsen KD (2009) Data assimilation using a gradient descent method for estimation of intraoperative brain deformation. Med Image Anal 13(5):744–756CrossRefGoogle Scholar
  5. 5.
    Ji S, Fan X, Roberts DW, Paulsen KD (2011) Cortical surface strain estimation using stereovision. In: Fichtinger G, Martel A, Peters T (eds) MICCAI 2011, Part I, LNCS 6891, pp 412–419Google Scholar
  6. 6.
    Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203CrossRefGoogle Scholar
  7. 7.
    Lucas BD, Kanade T (1981) An Iterative Image Registration Technique with an Application to Stereo Vision (DARPA), Proceedings of the 1981 DARPA Image Understanding Workshop, April 1981, pp. 121–130Google Scholar
  8. 8.
    Brox T, Bruhn A, Papenberg N, Weickert J (2004) High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36Google Scholar
  9. 9.
    Black MJ, Anandan P (1996) The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput Vis Image Underst 63(1):75–104CrossRefGoogle Scholar
  10. 10.
    Meer P (2004) Robust techniques for computer vision. Emerging Topics in Computer Vision, Gerard Medioni and Sing Bing Kang (Eds.), Prentice Hall, 107–190Google Scholar
  11. 11.
    Liu C (2009) Beyond pixels: exploring new representations and applications for motion analysis. Doctoral thesis, Massachusetts Institute of Technology, May 2009Google Scholar
  12. 12.
    Brigham EO (2002) The fast Fourier Transform. Prentice-Hall, New YorkGoogle Scholar
  13. 13.
    Fredric H (1978) On the use of windows for harmonic analysis with the Discrete Fourier Transform. Proc IEEE 66(1):51–83CrossRefGoogle Scholar
  14. 14.
    Pan B, Qian K, Xie H, Asundi A (2009) Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Meas Sci Technol 20:062001CrossRefGoogle Scholar
  15. 15.
    Lai WM, Rubin D, Krempl E (1993) Introduction to continuum mechanics. Pergamon, OxfordGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2013

Authors and Affiliations

  • Songbai Ji
    • 1
  • Xiaoyao Fan
    • 1
  • David W. Roberts
    • 2
    • 3
  • Alex Hartov
    • 1
  • Keith D. Paulsen
    • 1
    • 2
  1. 1.Thayer School of EngineeringDartmouth CollegeHanoverUSA
  2. 2.Norris Cotton Cancer CenterLebanonUSA
  3. 3.Dartmouth Hitchcock Medical CenterLebanonUSA

Personalised recommendations