Advertisement

Robust 3D Reconstruction and Mean-Shift Clustering of Motoneurons from Serial Histological Images

  • Nicolas Guizard
  • Pierrick Coupe
  • Nicolas Stifani
  • Stefano Stifani
  • D. Louis Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)

Abstract

Motoneurons (MNs) are neuronal cells involved in several central nervous system (CNS) diseases. In order to develop new treatments and therapies, there is a need to understand MN organization and differentiation. Although recently developed embryo mouse models have enabled the investigation of the MN specialization process, more robust and reproducible methods are required to evaluate the topology and structure of the neuron bundles. In this article, we propose a new fully automatic approach to identify MN clusters from stained histological slices. We developed a specific workflow including inter-slice intensity normalization and slice registration for 3D volume reconstruction, which enables the segmentation, mapping and 3D visualization of MN bundles. Such tools will facilitate the understanding of MN organization, differentiation and function.

Keywords

Motor Neuron Pool Histological Slice Topology Preservation Nonlinear Registration Current Slice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Christensen, G.E., Johnson, H.J.: Consistent image registration. Medical Imaging, IEEE TMI 20(7), 568–582 (2001)CrossRefGoogle Scholar
  2. 2.
    Collins, D.L., Evans, A.C.: Animal: Validation and applications of non-linear registration-based segmentation. IJPRAI 11, 1271–1294 (1997)Google Scholar
  3. 3.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  4. 4.
    Arber, et al.: Ets gene er81 controls the formation of functional connections between group ia sensory afferents and motor neurons. Cell 101(5), 485–498 (2000)CrossRefGoogle Scholar
  5. 5.
    Chakravarty, et al.: The creation of a brain atlas for image guided neurosurgery using serial histological data. NeuroImage 30(2), 359–376 (2006)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Chakravarty, et al.: Three-dimensional reconstruction of serial histological mouse brain sections. In: ISBI, IEEE International Symposium, pp. 987–990 (2008)Google Scholar
  7. 7.
    Dasen, et al.: A hox regulatory network establishes motor neuron pool identity and target-muscle connectivity. Cell 123(3), 477–491 (2005)CrossRefGoogle Scholar
  8. 8.
    Livet, et al.: Ets gene pea3 controls the central position and terminal arborization of specific motor neuron pools. Neuron 35(5), 877–892 (2002)CrossRefGoogle Scholar
  9. 9.
    Manjón, et al.: Fetal mri slice reconstruction using 3d inpainting. NeuroImage 47, S72 (2009)Google Scholar
  10. 10.
    Ourselin, et al.: Reconstructing a 3d structure from serial histological sections. Image and Vision Computing 19(1-2), 25–31 (2001)CrossRefGoogle Scholar
  11. 11.
    Stifani, et al.: Suppression of interneuron programs and maintenance of selected spinal motor neuron fates by the transcription factor aml1/runx1. International Journal of Developmental Neuroscience 8, 877 (2008)CrossRefGoogle Scholar
  12. 12.
    Tao, et al.: Symmetric inverse consistent nonlinear registration driven by mutual information. Computer Methods and Programs in Biomedicine 95(2), 105–115 (2009)CrossRefGoogle Scholar
  13. 13.
    Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE TMI 21(1), 32–40 (2003)MathSciNetGoogle Scholar
  14. 14.
    Jessell, T.M.: Neuronal specification in the spinal cord: inductive signals and transcriptional codes. Nature Reviews. Genetics 1(1), 20–29 (2000)CrossRefGoogle Scholar
  15. 15.
    Lee, H., Hong, H.: Robust surface registration using a gaussian-weighted distance map in pet-ct brain images. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 794–803. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Nyúl, L.G., Udupa, J.K.: On standardizing the mr image intensity scale. Magnetic Resonance in Medicine 42(6), 1072–1081 (1999)CrossRefGoogle Scholar
  17. 17.
    Sullivan, G.E.: Anatomy and embryology of the wing musculature of the domestic fowl (gallus). Australian Journal of Zoology 10(3), 458 (1962)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nicolas Guizard
    • 1
  • Pierrick Coupe
    • 1
  • Nicolas Stifani
    • 2
  • Stefano Stifani
    • 2
  • D. Louis Collins
    • 1
  1. 1.McConnell Brain Imaging Centre, Montréal Neurological InstituteMcGill UniversityMontréalCanada
  2. 2.Center for Neuronal Survival, Montréal Neurological InstituteMcGill UniversityMontréalCanada

Personalised recommendations