Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution...

Area of Science:

• Computational Neuroscience
• Computer Graphics
• Biomedical Imaging

Background:

• Generating accurate 3D polygonal meshes of neuronal membranes is challenging due to complex structures.
• Existing methods may struggle with watertightness and adaptive detail for visualization and simulation.

Purpose of the Study:

• To develop a novel, efficient method for constructing watertight 3D polygonal meshes representing neuronal membrane surfaces.
• To enable high-quality visualization and numerical simulations of neuronal morphology.

Main Methods:

• A novel approach reconstructs neuronal surfaces by deforming an initial sphere guided by the neuronal skeleton (digital sculpting).
• Local mapping, inspired by animation skinning, efficiently updates surface vertices within a defined region of influence (ROI).
• A finite-support convolution kernel generates a potential field for surface smoothing, while quasi-uniform rules maintain mesh quality and adaptive density is achieved based on neurite geometry.

Main Results:

• A watertight 3D mesh is successfully constructed from abstract point-and-diameter representations of neuronal morphology.
• The digital sculpting process, guided by the neuronal skeleton, effectively captures complex neuronal shapes.
• The method ensures mesh quality through edge manipulation and vertex adjustment, with adaptive density reflecting neurite characteristics.

Conclusions:

• The developed approach provides a robust and efficient method for generating high-quality 3D neuronal meshes.
• This technique facilitates improved visualization and numerical simulations of neuronal structures.
• The adaptive meshing strategy enhances the representation of neuronal complexity.