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Reconstructing the Surface Mesh Representation for Single Neuron

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Book cover Advances in Computer Graphics (CGI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13443))

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Abstract

In this paper, we present a pipeline to reconstruct the membrane surface of single neuron. Based on the abstract skeleton described by points with diameter information, a surface mesh representation is generated to approximate the neuronal membrane. The neuron has multi-branches (called neurites) connected together. Using a pushing-forward way, the algorithm computes a series of non-parallel contour lines along the extension direction of each neurite. These contours are self-adaptive to the neurite’s cross-sectional shape size and then be connected sequentially to form the surface. The soma is a unique part for the nerve cell but is usually detached to the neurites when reconstructed previously. The algorithm creates a suitable point set and obtains its surface mesh by triangulation, which can be combined with the surface of different neurite branches exactly to get the whole mesh model. Compared with the measurements, experiments show that our method is conducive to reconstruct high quality and density surface for single neuron.

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Authors and Affiliations

  1. Princeton University, Princeton, NJ, 08544, USA

    Ivar Ekeland

  2. Université de Paris-Sud, Laboratoire d’Analyse Numérique, Bâtiment 425, Orsay, France

    Roger Temam

Authors
  1. Ivar Ekeland
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  2. Roger Temam
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Correspondence to Ivar Ekeland .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. Bournemouth University, Poole, UK

    Jian Zhang

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. University of Crete, Heraklion, Greece

    George Papagiannakis

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  7. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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Ekeland, I., Temam, R. (2022). Reconstructing the Surface Mesh Representation for Single Neuron. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_14

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  • DOI: https://doi.org/10.1007/978-3-031-23473-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23472-9

  • Online ISBN: 978-3-031-23473-6

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