Deformable Atlas for Multi-structure Segmentation

  • Xiaofeng Liu
  • Albert Montillo
  • Ek. T. Tan
  • John F. Schenck
  • Paulo Mendonca
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)


We develop a novel deformable atlas method for multi-structure segmentation that seamlessly combines the advantages of image-based and atlas-based methods. The method formulates a probabilistic framework that combines prior anatomical knowledge with image-based cues that are specific to the subject’s anatomy, and solves it using expectation-maximization method. It improves the segmentation over conventional label fusion methods especially around the structure boundaries, and is robust to large anatomical variation. The proposed method was applied to segment multiple structures in both normal and diseased brains and was shown to significantly improve results especially in diseased brains.


Segmentation deformable atlas label fusion MLE GVF 


  1. 1.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comp. Vis. 1(4), 321–331 (1988)CrossRefGoogle Scholar
  2. 2.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Patt. Anal. Mach. Intell. 17(2), 158–175 (1995)CrossRefGoogle Scholar
  3. 3.
    Heckemann, R., Hajnal, J., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1), 115–126 (2006)CrossRefGoogle Scholar
  4. 4.
    Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med. Imag. 23(7), 903–930 (2004)CrossRefGoogle Scholar
  5. 5.
    Artaechevarria, X., Munoz-Barrutia, A., de Solorzano, C.O.: Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Trans. Med. Imag. 28(8), 1266–1277 (2009)CrossRefGoogle Scholar
  6. 6.
    Sabuncu, M.R., Yeo, B.T.T., Leemput, K.V., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Trans. Med. Imag. 29(10), 1714–1729 (2010)CrossRefGoogle Scholar
  7. 7.
    Shiee, N., Bazin, P.L., Cuzzocreo, J.L., Blitz, A., Pham, D.L.: Segmentation of brain images using adaptive atlases with application to ventriculomegaly. Inf. Process. Med. Imag., 1–22 (2011)Google Scholar
  8. 8.
    Wachinger, C., Golland, P.: Spectral label fusion. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 410–417. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Xu, C., Yezzi, A., Prince, J.: A summary of geometric level-set analogues for a general class of parametric active contour and surface models. In: Workshop on Variational and Level Set Methods in Computer Vision, pp. 104–111 (2001)Google Scholar
  10. 10.
    Xu, C., Pham, D.L., Prince, J.L.: Medical Image Segmentation Using Deformable Models. In: SPIE Handbook on Medical Imaging. Medical Image Analysis, vol. III, SPIE (2000)Google Scholar
  11. 11.
    Rosen, J.B.: The gradient projection method for nonlinear programming: Part II, nonlinear constraints. J. Soc. Indust. Appl. Math. 9(4), 514–532 (1961)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  13. 13.
    Roussau, F., Habas, P., Studholme, C.: A supervised patched-based approach for human brain labeling. IEEE Trans. Med. Imag. 30(10), 1852–1862 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaofeng Liu
    • 1
  • Albert Montillo
    • 1
  • Ek. T. Tan
    • 1
  • John F. Schenck
    • 1
  • Paulo Mendonca
    • 1
  1. 1.General Electric Global Research CenterNiskayunaUSA

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