Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 50–57Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Incorporating Parameter Uncertainty in Bayesian Segmentation Models: Application to Hippocampal Subfield Volumetry

Incorporating Parameter Uncertainty in Bayesian Segmentation Models: Application to Hippocampal Subfield Volumetry

  • Juan Eugenio Iglesias19,
  • Mert Rory Sabuncu19 &
  • Koen Van Leemput19,20,21 
  • Conference paper
  • 4191 Accesses

  • 5 Citations

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

Abstract

Many successful segmentation algorithms are based on Bayesian models in which prior anatomical knowledge is combined with the available image information. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free parameters in Bayesian segmentation models more accurately by using Monte Carlo sampling. We demonstrate our technique by sampling atlas warps in a recent method for hippocampal subfield segmentation, and show a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the method also yields informative “error bars” on the segmentation results for each of the individual sub-structures.

Keywords

  • Markov Chain Monte Carlo
  • Quadratic Discriminant Analysis
  • Hippocampal Subfield
  • Probabilistic Atlas
  • Hamiltonian Monte Carlo

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.

Download conference paper PDF

References

  1. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the Expectation-Maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)

    CrossRef  Google Scholar 

  2. Fischl, B., Salat, D., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.: Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002)

    CrossRef  Google Scholar 

  3. Wells, W., Grimson, W., Kikinis, R., Jolesz, F.: Adaptive segmentation of MRI data. IEEE Transactions on Medical Imaging 15(4), 429–442 (1996)

    CrossRef  Google Scholar 

  4. Fischl, B., Salat, D., van der Kouwe, A., Makris, N., Segonne, F., Quinn, B., Dale, A.: Sequence-independent segmentation of magnetic resonance images. NeuroImage 23, S69–S84 (2004)

    CrossRef  Google Scholar 

  5. Ashburner, J., Friston, K.: Unified segmentation. NeuroImage 26, 839–851 (2005)

    CrossRef  Google Scholar 

  6. Pohl, K., Fisher, J., Grimson, W., Kikinis, R., Wells, W.: A Bayesian model for joint segmentation and registration. NeuroImage 31(1), 228–239 (2006)

    CrossRef  Google Scholar 

  7. Van Leemput, K., Bakkour, A., Benner, T., Wiggins, G., Wald, L., Augustinack, J., Dickerson, B., Golland, P., Fischl, B.: Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19, 549–557 (2009)

    CrossRef  Google Scholar 

  8. Simpson, I.J.A., Woolrich, M., Groves, A.R., Schnabel, J.A.: Longitudinal Brain MRI Analysis with Uncertain Registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 647–654. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  9. Risholm, P., Pieper, S., Samset, E., Wells III, W.M.: Summarizing and Visualizing Uncertainty in Non-rigid Registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 554–561. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  10. Allassonniére, S., Amit, Y., Trouvé, A.: Toward a coherent statistical framework for dense deformable template estimation. Journal of the Royal Statistical Society, Series B 69, 3–29 (2007)

    Google Scholar 

  11. Ashburner, J., Andersson, J., Friston, K.: Image registration using a symmetric prior – in three dimensions. Human Brain Mapping 9(4), 212–225 (2000)

    CrossRef  Google Scholar 

  12. Duane, S., Kennedy, A., Pendleton, B., Roweth, D.: Hybrid Monte Carlo. Physics Letters B 195(2), 216–222 (1987)

    CrossRef  Google Scholar 

  13. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Chupin, M., Benali, H., Colliot, O.: Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage 56(2), 766–781 (2011)

    CrossRef  Google Scholar 

  14. DeLong, E., DeLong, D., Clarke-Pearson, D.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 837–845 (1988)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Martinos Center for Biomedical Imaging, MGH, Harvard Medical School, USA

    Juan Eugenio Iglesias, Mert Rory Sabuncu & Koen Van Leemput

  2. Department of Informatics and Mathematical Modeling, DTU, Denmark

    Koen Van Leemput

  3. Departments of Information and Computer Science and of Biomedical Engineering and Computational Science, Aalto University, Finland

    Koen Van Leemput

Authors
  1. Juan Eugenio Iglesias
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mert Rory Sabuncu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koen Van Leemput
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Iglesias, J.E., Sabuncu, M.R., Van Leemput, K. (2012). Incorporating Parameter Uncertainty in Bayesian Segmentation Models: Application to Hippocampal Subfield Volumetry. 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_7

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33454-2_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33453-5

  • Online ISBN: 978-3-642-33454-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature