Three-Dimensional Multimodality Modelling by Integration of High-Resolution Interindividual Atlases and Functional MALDI-IMS Data

  • Felix Bollenbeck
  • Stephanie Kaspar
  • Hans-Peter Mock
  • Diana Weier
  • Udo Seiffert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5462)


We present an approach for the analysis of phenotypic diversity in morphology and internal composition of biological specimen by means of high resolution 3-D models of developing barley grains. Three-dimensional histological structures are resolved by reconstructing specimen from large stacks of serially sectioned material, which is a preliminary for the spatial assignment of key tissues in differentiation. By sampling and constructing models at different developmental time steps from multiple individuals, we address two aims in a computational phenomics context: i) Generation of averaging atlases as structural references for integration of functional data, and ii) building the basis for a mathematical model of grain morphogenesis. We have established an algorithmic pipeline for automated processing of large image stacks towards phenotypic 3-D models and data-integration, comprising registration, multi-label segmentation, and alignment of functional measurements. The described algorithms allow high-throughput reconstruction and tissue recognition of datasets comprising thousands of images. The usefulness of the approach is demonstrated by automated model generation, allowing volumetric measurements of tissue composition, three-dimensional analysis of diversity, and the integration of MALDI-IMS data by mutual information based registration, which is a significant contribution to a systematic analysis of differentiation and development.


Plant Phenotyping 3-D Modelling Computational Phenomics Multimodality Registration 


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  1. 1.
    Alpert, N.M., Bradshaw, J.F., Kennedy, D., Correia, J.A.: The principal axes transformation - a method for image registration. The Journal of Nuclear Medicine 31, 1717–1722 (1990)PubMedGoogle Scholar
  2. 2.
    Bard, J.B.L., Baldock, R.A., Davidson, D.R.: Elucidating the genetic networks of development: A bioinformatics approach. Genome Research 8(9), 859–863 (1998)PubMedGoogle Scholar
  3. 3.
    Bollenbeck, F., Seiffert, U.: Fast registration-based automatic segmentation of serial section images for high-resolution 3-D plant seed modeling. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008, pp. 352–355 (2008)Google Scholar
  4. 4.
    Bougourd, S., Marrison, J., Haseloff, J.: An aniline blue staining procedure for confocal microscopy and 3-D imaging of normal and perturbed cellular phenotypes in mature Arabidopsis embryos. The Plant Journal 24, 443–550 (2000)CrossRefGoogle Scholar
  5. 5.
    Brüß, C., Strickert, M., Seiffert, U.: Towards automatic segmentation of serial high-resolution images. In: Proceedings Workshop Bildverarbeitung für die Medizin, pp. 126–130 (2006)Google Scholar
  6. 6.
    Chaurand, P., Norris, J.L., Cornett, D.S., Mobley, J.A., Caprioli, R.M.: New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry. J. Proteome Res. 5, 2889–2900 (2006)CrossRefPubMedGoogle Scholar
  7. 7.
    Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multi-modality image registration based on information theory. In: Information Processing in Medical Imaging, pp. 263–274 (1995)Google Scholar
  8. 8.
    Cornett, D.S., Reyzer, M.L., Chaurand, P., Caprioli, R.M.: MALDI imaging mass spectrometry: molecular snapshots of biochemical systems. Nature Methods 4, 828–833 (2007)CrossRefPubMedGoogle Scholar
  9. 9.
    Dercksen, V.J., Brüß, C., Stalling, D., Gubatz, S., Seiffert, U., Hege, H.-C.: Towards automatic generation of 3D models of biological objects based on serial sections. In: Linsen, L., Hagen, H., Hamann, B. (eds.) Visualization in Medicine and Life Sciences, pp. 3–25. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Glidewell, S.M.: NMR imaging of developing barley grains. Journal of Cereal Science 43, 70–78 (2006)CrossRefGoogle Scholar
  11. 11.
    Gubatz, S., Dercksen, V.J., Brüß, C., Weschke, W., Wobus, U.: Analysis of barley (Hordeum vulgare) grain development using three-dimensional digital models. The Plant Journal 52(4), 779–790 (2007)CrossRefPubMedGoogle Scholar
  12. 12.
    Guest, E., Baldock, R.: Automatic reconstruction of serial sections using the finite element method. Bioimaging 3(4), 154–167 (1995)CrossRefGoogle Scholar
  13. 13.
    Heeren, R.M.A.: Proteome imaging: a closer look at lifes organization. Proteomics 5(17), 4316–4326 (2005)CrossRefPubMedGoogle Scholar
  14. 14.
    Ibanez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide, 2nd edn. Kitware, Inc. (2005) ISBN 1-930934-15-7,
  15. 15.
    Lontoc-Roy, M., Dutilleul, P., Prasher, S.O., Han, L., Smith, D.L.: Computed tomography scanning for three-dimensional imaging and complexity analysis of developing root systems. Can. Jour. of Botany 83(11), 1434 (2005)CrossRefGoogle Scholar
  16. 16.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Kerkyra, Greece, vol. 2, pp. 1150–1157 (1999)Google Scholar
  17. 17.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. Trans. on Med. Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  18. 18.
    Mattes, D., Haynor, D.R., Vesselle, H., Lewellyn, T.K., Eubank, W.: Nonrigid multimodality image registration, vol. 4322, pp. 1609–1620 (July 2001)Google Scholar
  19. 19.
    McDonnell, L.A., Piersma, S.R., Altelaar, A.F.M., Mize, T.H., Luxembourg, S.L., Verhaert, P.D.E.M., van Minnen, J., Heeren, R.M.A.: Subcellular imaging mass spectrometry of brain tissue. Journal of Mass Spectrometry 40(2), 160–168 (2005)CrossRefPubMedGoogle Scholar
  20. 20.
    Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press, Oxford (2004)Google Scholar
  21. 21.
    Ringwald, M., Baldock, R.A., Bard, J., Kaufman, M.H., Eppig, J.T., Richardson, J.E., Nadeau, J.H., Davidson, D.: A database for mouse development. Science 265, 2033–2034 (1994)CrossRefPubMedGoogle Scholar
  22. 22.
    Schmitt, O., Modersitzki, J., Heldmann, S., Wirtz, S., Fischer, B.: Image registration of sectioned brains. Int. J. Comput. Vision 73(1), 5–39 (2006)CrossRefGoogle Scholar
  23. 23.
    Sinha, T.K., Khatib-Shahidi, S., Yankeelov, T.E., Mapara, K., Ehtesham, M., Cornett, D.S., Dawant, B.M., Caprioli, R.M., Gore, J.C.: Integrating spatially resolved three-dimensional MALDI IMS with in vivo magnetic resonance imaging. Nature Methods 5, 57–59 (2008)CrossRefPubMedGoogle Scholar
  24. 24.
    Styner, M., Gerig, G.: Evaluation of 2D/3D bias correction with 1+1 ES-optimization – Technical Report 179. Technical report, Image Science Lab, ETH Zürich (1997)Google Scholar
  25. 25.
    Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Felix Bollenbeck
    • 1
  • Stephanie Kaspar
    • 2
  • Hans-Peter Mock
    • 2
  • Diana Weier
    • 2
  • Udo Seiffert
    • 1
  1. 1.Fraunhofer Institute for Factory Operation and Automation IFFMagdeburgGermany
  2. 2.Leibniz Institute of Plant Genetics and Crop Plant ResearchGaterslebenGermany

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