Abstract
This paper presents an extension to the standard Bayesian image analysis paradigm to explicitly incorporate a multiscale approach. This new technique is demonstrated by applying it to the problem of compensating for soft tissue deformation of pre-segmented surfaces for image-guided surgery using 3-D ultrasound. The solution is regularised using knowledge of the mean and Gaussian curvatures of the surface estimate. Results are presented from testing the method on ultrasound data acquired from a volunteer’s liver. Two structures were segmented from an MR scan of the volunteer: the liver surface and the portal vein. Accurate estimates of the deformed surfaces were successfully computed using the algorithm, based on prior probabilities defined using a minimal amount of human intervention. With a more accurate prior model, this technique has the possibility to completely automate the process of compensating for intraoperative deformation in image-guided surgery.
Acknowledgements
We thank the U.K. EPSRC for funding this project. We are also grateful to the radiology and radiography staff at Guy’s Hospital for their assistance.
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King, A.P., Batchelor, P.G., Penney, G.P., Blackall, J.M., Hill, D.L., Hawkes, D.J. (2001). Estimating Sparse Deformation Fields Using Multiscale Bayesian Priors and 3-D Ultrasound. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_14
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DOI: https://doi.org/10.1007/3-540-45729-1_14
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