Estimating sparse deformation fields using multiscale Bayesian priors and 3-D ultrasound

TY - CHAP

T1 - Estimating sparse deformation fields using multiscale Bayesian priors and 3-D ultrasound

AU - King, A P

AU - Batchelor, P G

AU - Penney, G P

AU - Blackall, J M

AU - Hill, D L G

AU - Hawkes, D J

PY - 2001

Y1 - 2001

N2 - 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 3D 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 MRI 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 intra-operative deformation in image-guided surgery. (6 References).

AB - 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 3D 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 MRI 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 intra-operative deformation in image-guided surgery. (6 References).

M3 - Conference paper

SP - 155

EP - 161

BT - Conference Proceedings - Lecture Notes in Computer Science (LNCS) Vol#2082

PB - Springer

CY - Berlin, Germany

T2 - IPMI 2001: Information Processing in Medical Imaging - 17th International Conference

Y2 - 18 June 2001 through 22 June 2001

ER -