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Meshing of Spiny Neuronal Morphologies using Union Operators

Marwan Muhammad Ahmed Abdellah, Alessandro Enrico Foni, Juan José García Cantero, Nadir Roman Guerrero, Felix Schürmann

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). Computer Graphics Marwan Abdellah, Juan José García Cantero, Alessandro Foni, Nadir Román Guerrero, Elvis Boci, and Felix Schürmann

The Eurographics Association2022

A Unified Framework for Neuroscience Morphological Data Visualization

Juan José García Cantero

The complexity of the human brain makes its understanding one of the biggest challenges that science is currently confronting. Due to its complexity, the brain has been studied at many different levels and from many disciplines and points of view, using a diversity of techniques for getting meaningful data at each specific level and perspective, producing sometimes data that are difficult to integrate. In order to advance understanding of the brain, scientists need new tools that can speed up this analysis process and that can facilitate integrating research results from different disciplines and techniques. Visualization has proved to be useful in the analysis of complex data, and this paper focuses on the design of visualization solutions adapted to the specific problems posed by brain research. In this paper, we propose a unified framework that allows the integration of specific tools to work together in a coordinated manner in a multiview environment, displaying information at different levels of abstraction and combining schematic and realistic representations. The two use cases presented here illustrate the capability of this approach for providing a visual environment that supports the exploration of the brain at all its organizational levels.

2021