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Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images

  • Kilian M. Pohl
  • John Fisher
  • Ron Kikinis
  • W. Eric L. Grimson
  • William M. Wells
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

Abstract

Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We present an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior information. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. Structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the maximum a posteriori probability estimation problem. We demonstrate the approach on 20 brain magnetic resonance images showing superior performance, particularly in cases where purely image based methods fail.

Keywords

Logistic Function Expectation Maximization Principle Component Analysis Shape Model Automatic Segmentation 
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

  1. 1.
    Shenton, M., Kikinis, R., Jolesz, F., Pollak, S., LeMay, M., Wible, C., Hokama, H., Martin, J., Metcalf, D., Coleman, M., McCarley, R.: Left temporal lobe abnormalities in schizophrenia and thought disorder: A quantitative MRI study. New England Journal of Medicine 327, 604–612 (1992)CrossRefGoogle Scholar
  2. 2.
    Kikinis, R., Shenton, M.E., Gering, G., Martin, J., Anderson, M., Metcalf, D., Guttmann, C., McCarley, R.W., Lorensen, W., Line, H., Jolesz, F.A.: Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. MRI 2(6), 619–629 (1992)Google Scholar
  3. 3.
    Pohl, K., Fisher, J., Levitt, J., Shenton, M., Kikinis, R., Grimson, W., Wells, W.: A unifying approach to registration, segmentation, and intensity correction. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 310–318. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Collins, D., Zijdenbos, A., Barre, W., Evans, A.: Animal+insect: Improved cortical structure segmentation. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, p. 210. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  5. 5.
    Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: CVPR, pp. 1316–1323 (2000)Google Scholar
  6. 6.
    Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Sègonne, F., Salat, D., Busa, E., Seidman, L., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.: Automatically parcellating the human cerebral cortex. Cerebral Cortex 14, 11–22 (2004)CrossRefGoogle Scholar
  7. 7.
    Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. TMI 22(2), 137–154 (2003)Google Scholar
  8. 8.
    Leventon, M.E.: Statistical Models in Medical Image Analysis. PhD thesis, Massachusetts Institute of Technology (2000)Google Scholar
  9. 9.
    Yang, J., Duncan, J.S.: Joint prior models of neighboring objects for 3D image segmentation. In: CVPR, pp. 314–319 (2004)Google Scholar
  10. 10.
    Mumford, D., Shah, J.: Boundary detection by minimizing functionals. In: CVPR, pp. 22–26 (1985)Google Scholar
  11. 11.
    Wells, W., Grimson, W., Kikinis, R., Jolesz, F.: Adaptive segmentation of MRI data. TMI 15, 429–442 (1996)Google Scholar
  12. 12.
    Wyatt, P.P., Noble, J.A.: MAP MRF joint segmentation and registration. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 580–587. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Cootes, T., Hill, A., Taylor, C., Haslam, J.: The use of active shape models for locating structures in medical imaging. Imaging and Vision Computing 12(6), 335–366 (1994)Google Scholar
  14. 14.
    Van Leemput, K., Maes, F., Vanermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. TMI 18(10), 885–895 (1999)Google Scholar
  15. 15.
    Pohl, K., Bouix, S., Kikinis, R., Grimson, W.: Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework. In: ISBI, pp. 81–84 (2004)Google Scholar
  16. 16.
    McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. John Wiley and Sons, Inc., Chichester (1997)zbMATHGoogle Scholar
  17. 17.
    Yang, J., Staib, L.H., Duncan, J.S.: Neighbor-constrained segmentation with level set based 3D deformable models. TMI 23(8), 940–948 (2004)Google Scholar
  18. 18.
    Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)Google Scholar
  19. 19.
    Dice, L.R.: Measure of the amount of ecological association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kilian M. Pohl
    • 1
  • John Fisher
    • 1
  • Ron Kikinis
    • 2
  • W. Eric L. Grimson
    • 1
  • William M. Wells
    • 2
  1. 1.Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Surgical Planning LaboratoryHarvard Medical School, and Brigham and Women’s HospitalBostonUSA

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