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

Area of Science:

• Computational Neuroscience
• Biomedical Engineering
• Neuroimaging

Background:

• Increasing availability of digital brain cell skeleton data.
• Existing gap between skeleton data and simulation-ready meshes.
• Need for efficient and accurate mesh generation tools for brain cell simulations.

Purpose of the Study:

• To develop and implement Alpha_Mesh_Swc (AMS), a tool for automatic generation of simulation-optimized surface meshes from brain cell skeletons.
• To improve the accuracy and efficiency of creating 3D meshes for finite element simulations of brain cells.
• To address limitations in current methods for converting skeleton descriptions to high-quality meshes.

Main Methods:

• Utilized an Alpha Wrapping method with an offset parameter for watertight mesh generation.
• Applied mesh simplification and re-meshing techniques to optimize surface meshes.
• Developed a robust methodology compatible with imperfect skeleton data and mixed cell descriptions.

Main Results:

• AMS automatically generates accurate triangular surface meshes for brain cells (neurons, glia) efficiently.
• Achieved significant improvements in mesh accuracy compared to existing tools.
• Generated simplified meshes with approximately 10k nodes in minutes on a laptop.
• Successfully used generated meshes for diffusion MRI simulations in neurons and microglia.

Conclusions:

• Alpha_Mesh_Swc (AMS) effectively bridges the gap between brain cell skeleton data and simulation-ready meshes.
• The tool offers a robust, efficient, and accurate solution for generating high-quality surface meshes for computational neuroscience.
• Publicly available code and sample meshes facilitate further research in brain cell simulations and neuroimaging.