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)


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.


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.


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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

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