UCL  IRIS
Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at http://www.ucl.ac.uk/finance/research/post_award/post_award_contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Bayesian estimation of intra-operative deformation for image-guided surgery using 3-D ultrasound
  • Publication Type:
    Conference
  • Authors:
    King AP, Blackall JM, Penney GP, Edwards PJ, Hill DLG, Hawkes DJ
  • Publication date:
    01/01/2000
  • Pagination:
    588, 597
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    1935
  • ISBN-10:
    3540411895
  • ISBN-13:
    9783540411895
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
© Springer-Verlag Berlin Heidelberg 2000. This paper describes the application of Bayesian theory to the problem of compensating for soft tissue deformation to improve the accuracy of image-guided surgery. A triangular surface mesh segmented from a pre-operative image is used as the input to the algorithm, and intra-operatively acquired ultrasound data compounded into a 3-D volume is used to guide the deformation process. Prior probabilities are defined for the boundary points of the segmented structure based on knowledge of the direction of gravity, the position of the surface of the surgical scene, and knowledge of the tissue properties. The posterior probabilities of the locations of each of the boundary points are then maximised according to Bayes’ theorem. A regularisation term is included to constrain deformation to the global structure of the object. The technique is demonstrated using a deformable phantom designed to have similar properties to human tissue. Results presented demonstrate that the algorithm was able to recover much of the deformation for a number of objects at varying depths from the source of deformation. This technique offers a convenient means of introducing prior knowledge of the operative situation into the problem of soft tissue deformation and has the potential for greatly improving the utility of image-guided surgery.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Author
Dept of Computer Science
Author
Dept of Med Phys & Biomedical Eng
Author
Dept of Med Phys & Biomedical Eng
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by