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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 107–114Cite as

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Selection of Optimal Hyper-Parameters for Estimation of Uncertainty in MRI-TRUS Registration of the Prostate

Selection of Optimal Hyper-Parameters for Estimation of Uncertainty in MRI-TRUS Registration of the Prostate

  • Petter Risholm19,
  • Firdaus Janoos19,
  • Jennifer Pursley19,
  • Andriy Fedorov19,
  • Clare Tempany19,
  • Robert A. Cormack19 &
  • …
  • William M. Wells III19 
  • Conference paper
  • 4183 Accesses

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

Transrectal ultrasound (TRUS) facilitates intra-treatment delineation of the prostate gland (PG) to guide insertion of brachytherapy seeds, but the prostate substructure and apex are not always visible which may make the seed placement sub-optimal. Based on an elastic model of the prostate created from MRI, where the prostate substructure and apex are clearly visible, we use a Bayesian approach to estimate the posterior distribution on deformations that aligns the pre-treatment MRI with intra-treatment TRUS. Without apex information in TRUS, the posterior prediction of the location of the prostate boundary, and the prostate apex boundary in particular, is mainly determined by the pseudo stiffness hyper-parameter of the prior distribution. We estimate the optimal value of the stiffness through likelihood maximization that is sensitive to the accuracy as well as the precision of the posterior prediction at the apex boundary. From a data-set of 10 pre- and intra-treatment prostate images with ground truth delineation of the total PG, 4 cases were used to establish an optimal stiffness hyper-parameter when 15% of the prostate delineation was removed to simulate lack of apex information in TRUS, while the remaining 6 cases were used to cross-validate the registration accuracy and uncertainty over the PG and in the apex.

Keywords

  • Posterior Distribution
  • Markov Chain Monte Carlo
  • Prostate Gland
  • Posterior Predictive Distribution
  • Markov Chain Monte Carlo Chain

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Author information

Authors and Affiliations

  1. Brigham and Women’s Hospital, Harvard Medical School, USA

    Petter Risholm, Firdaus Janoos, Jennifer Pursley, Andriy Fedorov, Clare Tempany, Robert A. Cormack & William M. Wells III

Authors
  1. Petter Risholm
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  2. Firdaus Janoos
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  3. Jennifer Pursley
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  4. Andriy Fedorov
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  5. Clare Tempany
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  6. Robert A. Cormack
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  7. William M. Wells III
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Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Risholm, P. et al. (2012). Selection of Optimal Hyper-Parameters for Estimation of Uncertainty in MRI-TRUS Registration of the Prostate. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_14

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  • DOI: https://doi.org/10.1007/978-3-642-33454-2_14

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  • Print ISBN: 978-3-642-33453-5

  • Online ISBN: 978-3-642-33454-2

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