Crafting Detailed Neuronal Meshes: Unleashing the Digital Sculptor for Membrane Structures!

Published on October 28, 2022

Imagine sculpting a piece of clay, gradually giving it shape and form. Now picture applying this technique to construct intricate 3D meshes that accurately represent the surface of neurons. Scientists have developed a groundbreaking method where an initial sphere is transformed into a watertight mesh, following the guidance of the neuronal skeleton. It’s like meticulously molding clay! Filigree detail is achieved by convolving a finite-support convolution kernel along the neuron’s line skeleton, resulting in a smooth surface at both unidirectional and bifurcating regions. Mesh quality is always maintained through quasi-uniform rules, ensuring regular triangles and adjusting vertices to create a cohesive structure. Even the density of vertices on the mesh is intelligently determined based on the radius and curvature of neurites. This fascinating research enables us to visualize and simulate neurons with exceptional precision, unlocking new possibilities in neuroscience. Explore the full research article to discover how cutting-edge technology merges with the intricacies of the brain!

Creating high-quality polygonal meshes which represent the membrane surface of neurons for both visualization and numerical simulation purposes is an important yet nontrivial task, due to their irregular and complicated structures. In this paper, we develop a novel approach of constructing a watertight 3D mesh from the abstract point-and-diameter representation of the given neuronal morphology. The membrane shape of the neuron is reconstructed by progressively deforming an initial sphere with the guidance of the neuronal skeleton, which can be regarded as a digital sculpting process. To efficiently deform the surface, a local mapping is adopted to simulate the animation skinning. As a result, only the vertices within the region of influence (ROI) of the current skeletal position need to be updated. The ROI is determined based on the finite-support convolution kernel, which is convolved along the line skeleton of the neuron to generate a potential field that further smooths the overall surface at both unidirectional and bifurcating regions. Meanwhile, the mesh quality during the entire evolution is always guaranteed by a set of quasi-uniform rules, which split excessively long edges, collapse undersized ones, and adjust vertices within the tangent plane to produce regular triangles. Additionally, the local vertices density on the result mesh is decided by the radius and curvature of neurites to achieve adaptiveness.

Read Full Article (External Site)

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes:

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>