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In recent years, there has been a significant increase in publicly available skeleton descriptions of real brain cells from laboratories all over the world. In theory, this should make it is possible to perform large-scale realistic simulations on brain cells. However, currently there is still a gap between the skeleton descriptions and high-quality simulation-ready surface and volume meshes of brain cells. We propose and implement a tool called  Alpha_Mesh_Swc  ( AMS ) to generate automatically and efficiently triangular surface meshes that are optimized for finite element simulations. We use an Alpha Wrapping method with an offset parameter on component surface meshes to efficiently generate a global watertight mesh. Then mesh simplification and re-meshing are used to produce an optimal surface mesh. Our methodology limits the number of surface triangles, while preserving geometrical accuracy, permit cutting, and gluing of cell components, is robust to imperfect skeleton descriptions and allows mixed cell descriptions (surface meshes combined with skeletons). We compared the robustness, performance and accuracy of  AMS  against existing tools and found significant improvement in terms of mesh accuracy. We show, on average, we can generate fully automatically a brain cell (neurons or glia) surface mesh in a couple of minutes on a laptop computer resulting in a simplified surface mesh with only around 10k nodes. The resulting meshes were used to perform diffusion MRI simulations in neurons and microglia. The code and a number of sample brain cell surface meshes have been made publicly available.

Alpha_Mesh_Swc : automatic and robust surface mesh generation from the skeleton description of brain cells