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.
CITATION STYLE
King, A. P., Batchelor, P. G., Penney, G. P., Blackall, J. M., Hill, D. L. G., & Hawkes, D. J. (2001). Estimating sparse deformation fields using multiscale bayesian priors and 3-D ultrasound. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2082, pp. 155–161). Springer Verlag. https://doi.org/10.1007/3-540-45729-1_14
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