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Constrained Surface Evolutions for Prostate and Bladder Segmentation in CT Images

  • Mikael Rousson
  • Ali Khamene
  • Mamadou Diallo
  • Juan Carlos Celi
  • Frank Sauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

Abstract

We propose a Bayesian formulation for coupled surface evolutions and apply it to the segmentation of the prostate and the bladder in CT images. This is of great interest to the radiotherapy treatment process, where an accurate contouring of the prostate and its neighboring organs is needed. A purely data based approach fails, because the prostate boundary is only partially visible. To resolve this issue, we define a Bayesian framework to impose a shape constraint on the prostate, while coupling its extraction with that of the bladder. Constraining the segmentation process makes the extraction of both organs’ shapes more stable and more accurate. We present some qualitative and quantitative results on a few data sets, validating the performance of the approach.

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References

  1. 1.
    Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  3. 3.
    Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: Teaching level sets to walk. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 36–44. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Cremers, D., Rousson, M.: Efficient kernel density estimation of shape and intensity priors for level set segmentation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 757–764. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Dam, E.B., Fletcher, P.T., Pizer, S., Tracton, G., Rosenman, J.: Prostate shape modeling based on principal geodesic analysis bootstrapping. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 1008–1016. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Freedman, D., Radke, R.J., Zhang, T., Jeong, Y., Lovelock, D.M., Chen, G.T.: Model-based segmentation of medical imagery by matching distributions. IEEE Trans Med Imaging 24(3), 281–292 (2005)CrossRefGoogle Scholar
  7. 7.
    Leventon, M., Grimson, E., Faugeras, O.: Statistical Shape Influence in Geodesic Active Contours. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, June 2000, pp. 316–323. Hilton Head Island, South Carolina (2000)Google Scholar
  8. 8.
    Osher, S., Sethian, J.: Fronts propagating with curvature dependent speed: algorithms based on the Hamilton–Jacobi formulation. J. of Comp. Phys. 79, 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Paragios, N., Deriche, R.: Geodesic active regions: a new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, Special Issue on Partial Differential Equations in Image Processing, Computer Vision and Computer Graphics 13(1/2), 249–268 (2002)Google Scholar
  10. 10.
    Rousson, M., Paragios, N., Deriche, R.: Implicit active shape models for 3d segmentation in mr imaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 209–216. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Tsai, A., Wells, W., Tempany, C., Grimson, E., Willsky, A.: Mutual information in coupled multi-shape model for medical image segmentation. Medical Image Analysis 8(4), 429–445 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mikael Rousson
    • 1
  • Ali Khamene
    • 1
  • Mamadou Diallo
    • 1
  • Juan Carlos Celi
    • 2
  • Frank Sauer
    • 1
  1. 1.Imaging and Visulization Dept.Siemens Corporate ResearchPrincetonUSA
  2. 2.Oncology Care SystemsSiemens Medical SolutionsHeidelbergGermany

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