Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm

  • Seth D. Billings
  • Ayushi Sinha
  • Austin Reiter
  • Simon Leonard
  • Masaru Ishii
  • Gregory D. Hager
  • Russell H. Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features. Our algorithm is robust in the presence of outliers. We also test our algorithm on simulated and in-vivo data, and test its accuracy against degrading initializations.

References

  1. 1.
    Slavin, R.G., Spector, S.L., Bernstein, I.L., Kaliner, M.A., Kennedy, D.W., Virant, F.S., Wald, E.R., Khan, D.A., Blessing-Moore, J., Lang, D.M., Nicklas, R.A., Oppenheimer, J.J., Portnoy, J.M., Schuller, D.E., Tilles, S.A., Borish, L., Nathan, R.A., Smart, B.A., Vandewalker, M.L.: The diagnosis and management of sinusitis: a practice parameter update. JACI 116(6, Suppl.), S13–S47 (2005)Google Scholar
  2. 2.
    Bhattacharyya, N.: Ambulatory sinus and nasal surgery in the United States: demographics and perioperative outcomes. Laryngoscope 120, 635–638 (2010)CrossRefGoogle Scholar
  3. 3.
    Dalziel, K., Stein, K., Round, A., Garside, R., Royle, P.: Endoscopic sinus surgery for the excision of nasal polyps: a systematic review of safety and effectiveness. Am. J. Rhinol. 20(5), 506–519 (2006)CrossRefGoogle Scholar
  4. 4.
    Otake, Y., Leonard, S., Reiter, A., Rajan, P., Siewerdsen, J.H., Gallia, G.L., Ishii, M., Taylor, R.H., Hager, G.D.: Rendering-based video-CT registration with physical constraints for image-guided endoscopic sinus surgery. In: Proceedings of SPIE, vol. 9415, MI, IGPRIM, p. 94150A (2015)Google Scholar
  5. 5.
    Mirota, D.J., Hanzi, W., Taylor, R.H., Ishii, M., Gallia, G.L., Hager, G.D.: A system for video-based navigation for endoscopic endonasal skull base surgery. IEEE TMI 31(4), 963–976 (2012)Google Scholar
  6. 6.
    Leonard, S., Reiter, A., Sinha, A., Ishii, M., Taylor, R.H., Hager, G.D.: Image-based navigation for functional endoscopic sinus surgery using structure from motion. In: Proceedings of SPIE, vol. 9784, MI, IP, p. 97840V (2016)Google Scholar
  7. 7.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. PAMI 14, 239–256 (1992)CrossRefGoogle Scholar
  8. 8.
    Chetverikov, D., Svirko, D., Stepanov, D., Krsek, P.: The trimmed iterative closest point algorithm. ICPR 3, 545–548 (2002)Google Scholar
  9. 9.
    Mirota, D., Wang, H., Taylor, R.H., Ishii, M., Hager, G.D.: Toward video-based navigation for endoscopic endonasal skull base surgery. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 91–99. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04268-3_12 CrossRefGoogle Scholar
  10. 10.
    Billings, S.D., Boctor, E.M., Taylor, R.H.: Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment. PLoS ONE 10(3), e0117688 (2015)CrossRefGoogle Scholar
  11. 11.
    Kainz, J., Stammberger, H.: The roof of the anterior ethmoid: A place of least resistance in the skull base. Am. J. Rhinol. 3(4), 191–199 (1989)CrossRefGoogle Scholar
  12. 12.
    Mardia, K.V., Jupp, P.E.: Directional Statistics. Wiley Series in Probability and Statistics. Wiley, West Sussex (2000)MATHGoogle Scholar
  13. 13.
    Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3), 2033–2044 (2011)CrossRefGoogle Scholar
  14. 14.
    Sinha, A., Leonard, S., Reiter, A., Ishii, M., Taylor, R.H., Hager, G.D.: Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations. In: Proceedings of SPIE, vol. 9784, MI, IP, p. 97840D (2016)Google Scholar
  15. 15.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE TPAMI 33(5), 98–916 (2011)CrossRefGoogle Scholar
  16. 16.
    Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. IROS, pp. 2149–2154 (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Seth D. Billings
    • 1
  • Ayushi Sinha
    • 1
  • Austin Reiter
    • 1
  • Simon Leonard
    • 1
  • Masaru Ishii
    • 2
  • Gregory D. Hager
    • 1
  • Russell H. Taylor
    • 1
  1. 1.The Johns Hopkins UniversityBaltimoreUSA
  2. 2.Johns Hopkins Medical InstitutionsBaltimoreUSA

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