Generating high fidelity surface meshes of neocortical neurons using skin modifiers

Summary

The dataset contained in this repository is the resulting meshes from applying the Skin Modifier in Blender on all neuronal morphological types (mtypes) identified for comastosensory cortical cells by the Blue Brain Project. 

The papser, Generating high fidelity surface meshes of neocortical neurons using skin modifiers, is published in the 2019 EG Computer Graphics & Visual Computing (CGVC) conference. 

https://doi.org/10.2312/cgvc.20191257https://diglib.eg.org:443/handle/10.2312/cgvc20191257

Paper abstract 

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.

BibTex citation

@inproceedings {10.2312:cgvc.20191257,       booktitle = {Computer Graphics and Visual Computing (CGVC)},       editor = {Vidal, Franck P. and Tam, Gary K. L. and Roberts, Jonathan C.},       title = {{Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers}},       author = {Abdellah, Marwan and Favreau, Cyrille and Hernando, Juan and Lapere, Samuel and Schürmann, Felix},       year = {2019},       publisher = {The Eurographics Association},       ISBN = {978-3-03868-096-3},       DOI = {10.2312/cgvc.20191257}    }