DOI: 10.5281/zenodo.10817184
title: Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations

description: Summary 

The dataset contained in this repository is the resulting meshes from applying the Metaball skinner in Blender to the astrocytic morphologies created by Zisis et al., 2021.   

The paper, Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations, is published in Oxford Bioinformatics. 

Dataset

The dataset contains



30 randomly astroglial morphologies,  

30 corresponding surface and volume meshes (created with TetGen),

1000 astroglial morphologies, for large scale testing, 

meshes that are generated from the Metaball skinning algorithm,

meshes that are created from running the optimizer on the skinned meshes,

decimated meshes that can be used for visualization purpose, 

high quality renderings of all the astrocytic meshes, and 

comparative statistical analysis for the skinned meshes and the optimized watertight meshes.  


Paper record

Bioinformatics, Volume 37, Issue Supplement_1, July 2021, Pages i426–i433, https://doi.org/10.1093/bioinformatics/btab280

Paper abstract 



Motivation Astrocytes, the most abundant glial cells in the mammalian brain, have an instrumental role in developing neuronal circuits. They contribute to the physical structuring of the brain, modulating synaptic activity and maintaining the blood–brain barrier in addition to other significant aspects that impact brain function. Biophysically, detailed astrocytic models are key to unraveling their functional mechanisms via molecular simulations at microscopic scales. Detailed, and complete, biological reconstructions of astrocytic cells are sparse. Nonetheless, data-driven digital reconstruction of astroglial morphologies that are statistically identical to biological counterparts are becoming available. We use those synthetic morphologies to generate astrocytic meshes with realistic geometries, making it possible to perform these simulations.




 

Results We present an unconditionally robust method capable of reconstructing high fidelity polygonal meshes of astroglial cells from algorithmically-synthesized morphologies. Our method uses implicit surfaces, or metaballs, to skin the different structural components of astrocytes and then blend them in a seamless fashion. We also provide an end-to-end pipeline to produce optimized two- and three-dimensional meshes for visual analytics and simulations, respectively. The performance of our pipeline has been assessed with a group of 5000 astroglial morphologies and the geometric metrics of the resulting meshes are evaluated. The usability of the meshes is then demonstrated with different use cases.




 

Availability and implementation Our metaball skinning algorithm is implemented in Blender 2.82 relying on its Python API (Application Programming Interface). To make it accessible to computational biologists and neuroscientists, the implementation has been integrated into NeuroMorphoVis, an open source and domain specific package that is primarily designed for neuronal morphology visualization and meshing.


BibTex citation



@article{abdellah2021metaball,
  title={Metaball skinning of synthetic astroglial morphologies into realistic mesh models for in silico simulations and visual analytics},
  author={Abdellah, Marwan and Foni, Alessandro and Zisis, Eleftherios and Guerrero, Nadir Roman and Lapere, Samuel and Coggan, Jay S and Keller, Daniel and Markram, Henry and Sch{\"u}rmann, Felix},
  journal={Bioinformatics},
  volume={37},
  number={Supplement\_1},
  pages={i426--i433},
  year={2021},
  publisher={Oxford University Press}
}