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A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation

  • Tobias Heimann
  • Sascha Münzing
  • Hans-Peter Meinzer
  • Ivo Wolf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4584)

Abstract

We present a novel method for the segmentation of volumetric images, which is especially suitable for highly variable soft tissue structures. Core of the algorithm is a statistical shape model (SSM) of the structure of interest. A global search with an evolutionary algorithm is employed to detect suitable initial parameters for the model, which are subsequently optimized by a local search similar to the Active Shape mechanism. After that, a deformable mesh with the same topology as the SSM is used for the final segmentation: While external forces strive to maximize the posterior probability of the mesh given the local appearance around the boundary, internal forces governed by tension and rigidity terms keep the shape similar to the underlying SSM. To prevent outliers and increase robustness, we determine the applied external forces by an algorithm for optimal surface detection with smoothness constraints. The approach is evaluated on 54 CT images of the liver and reaches an average surface distance of 1.6 ±0.5 mm in comparison to manual reference segmentations.

Keywords

Training Image Shape Model Appearance Model Deformable Model Active Shape Model 
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. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models – their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  2. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  3. Shen, D., Davatzikos, C.: An adaptive-focus deformable model using statistical and geometric information. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 906–913 (2000)CrossRefGoogle Scholar
  4. Weese, J., Kaus, M., Lorenz, C., Lobregt, S., Truyen, R., Pekar, V.: Shape constrained deformable models for 3D medical image segmentation. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 380–387. Springer, Heidelberg (2001)Google Scholar
  5. Soler, L., Delingette, H., Malandain, G., Montagnat, J., Ayache, N., et al.: Fully automatic anatomical, pathological, and functional segmentation from ct scans for hepatic surgery. In: Proc. SPIE Medical Imaging, pp. 246–255 (2000)Google Scholar
  6. Hill, A., Taylor, C.J., Cootes, T.F.: Object recognition by flexible template matching using genetic algorithms. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 852–856. Springer, Heidelberg (1992)Google Scholar
  7. de Bruijne, M., Nielsen, M.: Shape particle filtering for image segmentation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 168–175. Springer, Heidelberg (2004)Google Scholar
  8. Subsol, G., Thirion, J.P., Ayache, N.: A scheme for automatically building three-dimensional morphometric anatomical atlases: application to a skull atlas. Medical Image Analysis 2(1), 37–60 (1998)CrossRefGoogle Scholar
  9. Frangi, A.F., Rueckert, D., Schnabel, J.A., Niessen, W.J.: Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans. Medical Imaging 21(9), 1151–1166 (2002)CrossRefGoogle Scholar
  10. Kaus, M.R., Pekar, V., Lorenz, C., Truyen, R., Lobregt, S., Weese, J.: Automated 3-D PDM construction from segmented images using deformable models. IEEE Trans. Medical Imaging 22(8), 1005–1013 (2003)CrossRefGoogle Scholar
  11. Davies, R.H., Twining, C.J., Cootes, T.F., Waterton, J.C., Taylor, C.J.: 3D statistical shape models using direct optimisation of description length. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 3–20. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. Heimann, T., Wolf, I., Meinzer, H.P.: Optimal landmark distributions for statistical shape model construction. In: Proc. SPIE Medical Imaging: Image Processing. vol. 6144, pp. 518–528 (2006)Google Scholar
  13. de Bruijne, M., van Ginneken, B., Viergever, M.A., Niessen, W.J.: Adapting active shape models for 3D segmentation of tubular structures in medical images. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 136–147. Springer, Heidelberg (2003)Google Scholar
  14. Kittler, J., Alkoot, F.M.: Moderating k-NN classifiers. Pattern Analysis & Applications 5(3), 326–332 (2002)CrossRefGoogle Scholar
  15. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. John Wiley, New York (1966)zbMATHGoogle Scholar
  16. Schwefel, H.P.: Evolution and Optimum Seeking. John Wiley & Sons, Inc, New York (1995)Google Scholar
  17. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  18. Behiels, G., Maes, F., Vandermeulen, D., Suetens, P.: Evaluation of image features and search strategies for segmentation of bone structures in radiographs using active shape models. Medical Image Analysis 6(1), 47–62 (2002)CrossRefGoogle Scholar
  19. Li, K., Millington, S., Wu, X., Chen, D.Z., Sonka, M.: Simultaneous segmentation of multiple closed surfaces using optimal graph searching. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 406–417. Springer, Heidelberg (2005)Google Scholar
  20. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  21. Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)CrossRefzbMATHGoogle Scholar
  22. Lamecker, H., Lange, T., Seebass, M.: Segmentation of the liver using a 3D statistical shape model. Technical report, Zuse Institute, Berlin, Germany (2004)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Tobias Heimann
    • 1
  • Sascha Münzing
    • 1
  • Hans-Peter Meinzer
    • 1
  • Ivo Wolf
    • 1
  1. 1.Div. Medical and Biological Informatics, German Cancer Research Center, 69120 HeidelbergGermany

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