Estimating Sparse Deformation Fields Using Multiscale Bayesian Priors and 3-D Ultrasound

  • Authors:
  • Andrew P. King;Philipp G. Batchelor;Graeme P. Penney;Jane M. Blackall;Derek L. G. Hill;David J. Hawkes

  • Affiliations:
  • -;-;-;-;-;-

  • Venue:
  • IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
  • Year:
  • 2001

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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.