Skip to main content
SpringerLink
Log in

Geometric Analysis of 3D Electron Microscopy Data

  • Conference paper
Applications of Discrete Geometry and Mathematical Morphology (WADGMM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7346))

Abstract

We present a complete pipeline for the segmentation of 3-dimensional electron microscopy data. Efficient algorithms and parallelization have been developed to make the system applicable to data as large as eight gigavoxels. Discrete geometry plays a prominent role at several processing stages (initial watershed segmentation, cell complex representation, reduction of oversegmentation by a graphical model, topological and geometric feature computation). Many modules described here are available via our open-source software repository.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 72.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. HDF5 data storage technologies (2010), http://www.hdfgroup.org/HDF5/

  2. Andres, B., Koethe, U., Kroeger, T., Helmstaedter, M., Briggman, K.L., Denk, W., Hamprecht, F.A.: 3D segmentation of sbfsem images of neuropil by a graphical model over supervoxel boundaries. Medical Image Analysis (2011)

    Google Scholar 

  3. Andres, B., Köthe, U., Kröger, T., Hamprecht, F.A.: How to extract the geometry and topology from very large 3D segmentations. ArXiv e-prints (2010) (submitted)

    Google Scholar 

  4. Baldacci, F., Braquelaire, A., Desbarats, P., Domenger, J.-P.: 3D Image Topological Structuring with an Oriented Boundary Graph for Split and Merge Segmentation. In: Coeurjolly, D., Sivignon, I., Tougne, L., Dupont, F. (eds.) DGCI 2008. LNCS, vol. 4992, pp. 541–552. Springer, Heidelberg (2008), doi:10.1007/978-3-540-79126-3_48

    Chapter  Google Scholar 

  5. Bishop, C.: Pattern Recognition and Machine Learning. Springer (2006)

    Google Scholar 

  6. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  7. Cazals, F., Pouget, M.: Estimating differential quantities using polynomial fitting of osculating jets. Computer Aided Geometric Design 22(2), 121–146 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  8. Damiand, G.: Topological model for 3D image representation: Definition and incremental extraction algorithm. Comput. Vis. Image Underst. 109(3), 260–289 (2008)

    Article  Google Scholar 

  9. Denk, W., Horstmann, H.: Serial Block-Face scanning electron microscopy to reconstruct Three-Dimensional tissue nanostructure. PLoS Biology 2(11) (November 2004); PMID: 15514700 PMCID: 524270

    Google Scholar 

  10. Fourey, S., Malgouyres, R.: Normals and Curvature Estimation for Digital Surfaces Based on Convolutions. In: Coeurjolly, D., Sivignon, I., Tougne, L., Dupont, F. (eds.) DGCI 2008. LNCS, vol. 4992, pp. 287–298. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Jain, V., Murray, J., Roth, F., Turaga, S., Zhigulin, V., Briggman, K., Helmstaedter, M., Denk, W., Seung, H.: Supervised learning of image restoration with convolutional networks. In: ICCV 2007, pp. 1–8 (2007)

    Google Scholar 

  12. Jurrus, E., Hardy, M., Tasdizen, T., Fletcher, P.T., Koshevoy, P., Chien, C., Denk, W., Whitaker, R.: Axon tracking in serial block-face scanning electron microscopy. Medical Image Analysis 13(1), 180–188 (2009); PMID: 18617436

    Google Scholar 

  13. Jurrus, E., Whitaker, R., Jones, B., Marc, R., Tasdizen, T.: An optimal-path approach for neural circuit reconstruction. In: IEEE Int. Sym. Biomedical Imaging, pp. 1609–1612 (2008)

    Google Scholar 

  14. 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. LNCS, vol. 6362, pp. 209–216. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  15. Khalimsky, E., Kopperman, R., Meyer, P.: Computer graphics and connected topologies on finite ordered sets. J. Topology and its Appl. 36, 1–27 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  16. Köthe, U.: Deriving Topological Representations from Edge Images. In: Asano, T., Klette, R., Ronse, C. (eds.) Geometry, Morphology, and Computational Imaging. LNCS, vol. 2616, pp. 320–334. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  17. Kovalevsky, V.: Algorithms in Digital Geometry Based on Cellular Topology. In: Klette, R., Žunić, J. (eds.) IWCIA 2004. LNCS, vol. 3322, pp. 366–393. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  18. Kovalevsky, V.A.: Finite topology as applied to image analysis. Comput. Vision Graph. Image Process. 46(2), 141–161 (1989)

    Article  Google Scholar 

  19. Kreshuk, A., Straehle, C.N., Sommer, C., Koethe, U., Cantoni, M., Knott, G., Hamprecht, F.A.: Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS ONE 6(10), e24899 (2011)

    Google Scholar 

  20. Lienhardt, P.: Topological models for boundary representation: a comparison with n-dimensional generalized maps. Computer-Aided Design 23(1), 59–82 (1991)

    Article  MATH  Google Scholar 

  21. Macke, J.H., Maack, N., Gupta, R., Denk, W., Schölkopf, B., Borst, A.: Contour-propagation algorithms for semi-automated reconstruction of neural processes. Journal of Neuroscience Methods 167(2), 349–357 (2008); PMID: 17870180

    Google Scholar 

  22. Mishchenko, Y.: Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs. Journal of Neuroscience Methods 176(2), 276–289 (2009)

    Article  MathSciNet  Google Scholar 

  23. Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proc. ICCV 2003, pp. 10–17 (2003)

    Google Scholar 

  24. Sommer, C., Straehle, C., Köthe, U., Hamprecht, F.: Ilastik: Interactive learning and segmentation toolkit. In: IEEE International Symposium on Biomedical Imaging, March 30-April 2, pp. 230–233 (2011)

    Google Scholar 

  25. Sporns, O., Tononi, G., Kötter, R.: The human connectome: A structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)

    Google Scholar 

  26. 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(2), 511–538 (2010)

    Article  MATH  Google Scholar 

  27. Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Patt. Anal. Mach. Intell. 13(6), 583–598 (1991)

    Article  Google Scholar 

  28. Vitaladevuni, S.N., Basri, R.: Co-clustering of image segments using convex optimization applied to EM neuronal reconstruction. In: CVPR 2010 (2010)

    Google Scholar 

  29. Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millennium. Morgan Kaufmann (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Multidimensional Image Processing Group, University of Heidelberg, Germany

    Ullrich Köthe, Björn Andres, Thorben Kröger & Fred Hamprecht

Authors
  1. Ullrich Köthe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Björn Andres
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thorben Kröger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fred Hamprecht
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. HCI, University of Heidelberg, Germany, Speyerer Strasse 6, D-69115, Heidelberg, Germany

    Ullrich Köthe

  2. GIPSA-lab, 961 rue de la Houille Blanche, 38402, Saint Martin d’Hères cedex, France

    Annick Montanvert

  3. EC JRC Space Application Institute, T.P. 262, 21020, Ispra, Italy

    Pierre Soille

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Köthe, U., Andres, B., Kröger, T., Hamprecht, F. (2012). Geometric Analysis of 3D Electron Microscopy Data. In: Köthe, U., Montanvert, A., Soille, P. (eds) Applications of Discrete Geometry and Mathematical Morphology. WADGMM 2010. Lecture Notes in Computer Science, vol 7346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32313-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32313-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32312-6

  • Online ISBN: 978-3-642-32313-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation