Browsing by Author "Abdellah, Marwan"

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    Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers
    (The Eurographics Association, 2019) Abdellah, Marwan; Favreau, Cyrille; Hernando, Juan; Lapere, Samuel; Schürmann, Felix; Vidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.
    We present the results of exploring the capabilities of skinning modifiers to generate high fidelity polygonal surface meshes of neurons from their morphological skeletons that are segmented from optical microscopy slides. Our algorithm is implemented in Blender as an add-on relying on its standard Python API. The implementation is also integrated into an open source domain specific framework, NeuroMorphoVis, that is used to visualize and analyze neuronal morphologies available from the neuroscientific community. Our technique is applied to create meshes for a set of neurons with 55 different morphologies reconstructed from the neocortex of a 14-days-old rat. The generated meshes are used to visualize full compartmental simulations of neocortical activity for analysis purposes and also to create high quality scientific illustrations of in silico neuronal circuits for media production with physically-based path tracers.
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    Meshing of Spiny Neuronal Morphologies using Union Operators
    (The Eurographics Association, 2022) Abdellah, Marwan; Cantero, Juan José García; Foni, Alessandro; Guerrero, Nadir Román; Boci, Elvis; Schürmann, Felix; Peter Vangorp; Martin J. Turner
    Neurons are characterized by thin and long interleaving arborizations in which creating accurate mesh models of their cellular membranes is challenging. While union operators are central for CAD/CAM modeling and computer graphics applications, their applicability to neuronal mesh generation has not been explored. In this work, we present the results of exploring the effectiveness of using union operators to generate high fidelity surface meshes of spiny neurons from their morphological traces. To improve the visual realism of the resulting models, a plausible shape of the cell body is also realized with implicit surfaces (metaballs). The algorithm is implemented in Blender based on its Python API and is integrated into NeuroMorphoVis, a neuroscience-specific framework for visualization and analysis of neuronal morphologies. Our method is applied to a dataset consisting of more than 600 neurons representing 60 morphological types reconstructed from the neocortex of a juvenile rat. The performance of our implementation is quantitatively analyzed, and the results are qualitatively compared to previous implementation. The resulting meshes are applicable in multiple contexts including visualization and analysis of full compartmental simulations and generation of high quality multimedia content for scientific visualization and visual computing (Figure 1).