Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images

  • C. N. Straehle
  • U. Köthe
  • G. Knott
  • F. A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)

Abstract

Interactive segmentation algorithms should respond within seconds and require minimal user guidance. This is a challenge on 3D neural electron microscopy images. We propose a supervoxel-based energy function with a novel background prior that achieves these goals. This is verified by extensive experiments with a robot mimicking human interactions. A graphical user interface offering access to an open source implementation of these algorithms is made available.

Keywords

electron microscopy seeded segmentation interactive segmentation graph cut watershed supervoxel 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Andres, B., Köthe, U., Helmstaedter, M., Denk, W., Hamprecht, F.A.: Segmentation of SBFSEM volume data of neural tissue by hierarchical classification. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 142–152. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE TPAMI 26, 1124–1137 (2004)CrossRefMATHGoogle Scholar
  3. 3.
    Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watershed: A unifying graph-based optimization framework. IEEE TPAMI 33, 1384–1399 (2011)CrossRefGoogle Scholar
  4. 4.
    Grady, L.: Random walks for image segmentation. IEEE TPAMI 28, 1768–1783 (2006)CrossRefGoogle Scholar
  5. 5.
    Gulshan, V., Rother, C., Criminisi, A., Blake, A., Zisserman, A.: Geodesic star convexity for interactive image segmentation. In: IEEE CVPR 2010, pp. 3129–3136 (2010)Google Scholar
  6. 6.
    Jain, V., Bollmann, B., Richardson, M., Berger, D., Helmstaedter, M., Briggman, K., Denk, W., Bowden, J., Mendenhall, J., Abraham, W., Harris, K., Kasthuri, N., Hayworth, K., Schalek, R., Tapia, J., Lichtman, J., Seung, H.: Boundary learning by optimization with topological constraints. In: IEEE CVPR 2010, pp. 2488–2495 (2010)Google Scholar
  7. 7.
    Jeong, W., Beyer, J., Hadwiger, M., Blue, R., Law, C., Vazquez, A., Reid, C., Lichtman, J., Pfister, H.: Ssecrett and neurotrace: Interactive visualization and analysis tools for large-scale neuroscience datasets. IEEE Comput Graph 30, 58–70 (2010)CrossRefGoogle Scholar
  8. 8.
    Jurrus, E., Hardy, M., Tasdizen, T., Fletcher, P., Koshevoy, P., Chien, C., Denk, W., Whitaker, R.: Axon tracking in serial block-face scanning electron microscopy. Med. Image Anal. 13(1), 180–188 (2009)CrossRefGoogle Scholar
  9. 9.
    Jurrus, E., Paiva, A., Watanabe, S., Anderson, J., Jones, B., Whitaker, R., Jorgensen, E., Marc, R., Tasdizen, T.: Detection of neuron membranes in electron microscopy images using a serial neural network architecture. Med. Image Anal. 14(6), 770–783 (2010)CrossRefGoogle Scholar
  10. 10.
    Kaynig, V., Fuchs, T., Buhmann, J.: Neuron geometry extraction by perceptual grouping in sstem images. In: IEEE CVPR 2010, pp. 2902–2909 (2010)Google Scholar
  11. 11.
    Knott, G., Marchman, H., Wall, D., Lich, B.: Serial section scanning electron microscopy of adult brain tissue using focused ion beam milling. J. Neurosci. 28(12), 2959–2964 (2008)CrossRefGoogle Scholar
  12. 12.
    Kolmogorov, V., Boykov, Y.: What metrics can be approximated by geo-cuts, or global optimization of length/area and flux. In: IEEE ICCV 2005, vol. 1, pp. 564–571 (2005)Google Scholar
  13. 13.
    Peng, H., Ruan, Z., Long, F., Simpson, J., Myers, E.: V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology 28(4), 348–353 (2010)CrossRefGoogle Scholar
  14. 14.
    Ren, X., Malik, J.: Learning a classification model for segmentation. In: IEEE ICCV 2003, vol. 2, pp. 10–17 (2003)Google Scholar
  15. 15.
    Turaga, S.C., Murray, J.F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., Seung, H.S.: Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Comput. 22, 511–538 (2010)CrossRefMATHGoogle Scholar
  16. 16.
    Turaga, S.C., Briggman, K., Helmstaedter, M., Denk, W., Seung, H.: Maximin affinity learning of image segmentation. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) NIPS 2009, pp. 1865–1873 (2009)Google Scholar
  17. 17.
    Vazquez-Reina, A., Miller, E., Pfister, H.: Multiphase geometric couplings for the segmentation of neural processes. In: IEEE CVPR 2009, pp. 2020–2027 (2009)Google Scholar
  18. 18.
    Vu, N., Manjunath, B.: Graph cut segmentation of neuronal structures from transmission electron micrographs. In: IEEE ICIP 2008, pp. 725–728 (2008)Google Scholar
  19. 19.
    Yang, H.-F., Choe, Y.: Electron microscopy image segmentation with graph cuts utilizing estimated symmetric three-dimensional shape prior. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R. (eds.) ISVC 2010. LNCS, vol. 6454, pp. 322–331. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • C. N. Straehle
    • 1
  • U. Köthe
    • 1
  • G. Knott
    • 2
  • F. A. Hamprecht
    • 1
  1. 1.University of HeidelbergHeidelbergGermany
  2. 2.Ecole Polytechnique FédéraleLausanneSwitzerland

Personalised recommendations