Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections

  • Dmitry Laptev
  • Alexander Vezhnevets
  • Sarvesh Dwivedi
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7510)


Connectomics based on high resolution ssTEM imagery requires reconstruction of the neuron geometry from histological slides. We present an approach for the automatic membrane segmentation in anisotropic stacks of electron microscopy brain tissue sections. The ambiguities in neuronal segmentation of a section are resolved by using the context from the neighboring sections. We find the global dense correspondence between the sections by SIFT Flow algorithm, evaluate the features of the corresponding pixels and use them to perform the segmentation. Our method is 3.6 and 6.4% more accurate in two different accuracy metrics than the algorithm with no context from other sections.


Membrane Segmentation Anisotropic Data Dense Correspondence SIFT Flow 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Advanced Weka Segmentation (Fiji),
  2. 2.
    ISBI 2012 challenge,
  3. 3.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. Trans. Pattern Anal. Mach. Intell. 26, 1124–1137 (2004)CrossRefGoogle Scholar
  4. 4.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)MATHCrossRefGoogle Scholar
  5. 5.
    Cardona, A., Saalfeld, S., Preibisch, S., Schmid, B., Pulokas, A.C.J., Tomancak, P., Hartenstein, V.: An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol. 10 (2010)Google Scholar
  6. 6.
    Jain, V., Bollmann, B., Richardson, M., Berger, D.R., Helmstaedter, M., Briggman, K.L., Denk, W., Bowden, J.B., Mendenhall, J.M., Abraham, W.C., Harris, K.M., Kasthuri, N., Hayworth, K.J., Schalek, R., Tapia, J.C., Lichtman, J.W., Seung, H.S.: Boundary learning by optimization with topological constraints. In: CVPR, pp. 2488–2495 (2010)Google Scholar
  7. 7.
    Kaynig, V., Fuchs, T.J., Buhmann, J.M.: Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 209–216. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Kaynig, V., Fuchs, T.J., Buhmann, J.M.: Neuron geometry extraction by perceptual grouping in sstem images. In: CVPR, pp. 2902–2909. IEEE (2010)Google Scholar
  9. 9.
    Kumar, R., Reina, A.V., Pfister, H.: Radon-like features and their application to connectomics. In: MMBIA. IEEE (2010)Google Scholar
  10. 10.
    Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. Trans. Pattern Anal. Mach. Intell. 33(5), 978–994 (2011)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, p. 1150. IEEE (1999)Google Scholar
  12. 12.
    Lucchi, A., Smith, K., Achanta, R., Lepetit, V., Fua, P.: A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 463–471. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Sandberg, K., Brega, M.: Segmentation of thin structures in electron micrographs using orientation fields. Journal of Structural Biology 157(2), 403–415 (2007)CrossRefGoogle Scholar
  14. 14.
    Vasilevskiy, A., Siddiqi, K.: Flux maximizing geometric flows. Trans. Pattern Anal. Mach. Intell. 24, 1565–1578 (2001)CrossRefGoogle Scholar
  15. 15.
    Vazquez-Reina, A., Huang, D., Gelbart, M., Lichtman, J., Miller, E., Pfister, H.: Segmentation fusion for connectomics. In: ICCV. IEEE (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dmitry Laptev
    • 1
  • Alexander Vezhnevets
    • 1
  • Sarvesh Dwivedi
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.Department of Computer ScienceETH ZurichSwitzerland

Personalised recommendations