Abstract
We present a novel method to visualize registration uncertainty and a simple study to motivate the use of uncertainty visualization in computer–assisted surgery. Our visualization method resulted in a statistically significant reduction in the number of attempts required to localize a target, and a statistically significant reduction in the number of targets that our subjects failed to localize. Most notably, our work addresses the existence of uncertainty in guidance and offers a first step towards helping surgeons make informed decisions in the presence of imperfect data.
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Laidlaw, D.H., et al.: Comparing 2D vector field visualization methods: A user study. IEEE Transactions on Visualization and Computer Graphics 11, 59–70 (2005)
Vellido, A., Lisboa, P.J.: Handling outliers in brain tumor MRS data analysis through robust topographic mapping. Computers in Biology and Medicine (to appear, 2006)
Karayiannia, N.B., et al.: Quantifying and visualizing uncertainty in EEG data of neonatal seizures. In: Proc of the 26th Annual International Conference of the IEEE EMBS (2004)
Jones, D.K.: Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI. Magnetic Resonance in Medicine 49, 7–12 (2003)
McCormick, T., et al.: Target volume uncertainty and a method to visualize its effect on the target dose prescription. International Journal of Radiation Oncology Biology Physics 60, 1580–1588 (2004)
Dahlin, D.C., Unni, K.: Bone Tumors: General Aspects and Data on 8,542 Cases, 4th edn. Thomas, Springfield, Ill (1986)
Ellis, R.E., Kerr, D., Rudan, J.F., Davidson, L.: Minimally invasive excision of deep bone tumors. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, Springer, Heidelberg (2001)
Ma, B., Ellis, R.E.: Surface-based registration with a particle filter. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 566–573. Springer, Heidelberg (2004)
Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)
Vlassis, N., Likas, A.: A greedy EM algorithm for Gaussian mixture learning. Neural Processing Letters 15, 77–87 (2002)
Volume Rendering Techniques. In: Fernando, R. (ed.) GPU Gems: Programming Techniques, Tips, and Tricks for Real-Time Graphics, Addison-Wesley, New York (2004)
Sheskin, D.J.: Handbook of Parametric and Nonparametric Tests, 3rd edn. Chapman & Hall, Boca Raton (2004)
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Simpson, A.L., Ma, B., Chen, E.C.S., Ellis, R.E., Stewart, A.J. (2006). Using Registration Uncertainty Visualization in a User Study of a Simple Surgical Task. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_49
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DOI: https://doi.org/10.1007/11866763_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44727-6
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