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Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation
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 waterti...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648403/ https://www.ncbi.nlm.nih.gov/pubmed/36387589 http://dx.doi.org/10.3389/fninf.2022.953930 |
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author | Zhu, Xiaoqiang Liu, Xiaomei Liu, Sihu Shen, Yalan You, Lihua Wang, Yimin |
author_facet | Zhu, Xiaoqiang Liu, Xiaomei Liu, Sihu Shen, Yalan You, Lihua Wang, Yimin |
author_sort | Zhu, Xiaoqiang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9648403 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96484032022-11-15 Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation Zhu, Xiaoqiang Liu, Xiaomei Liu, Sihu Shen, Yalan You, Lihua Wang, Yimin Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2022-10-27 /pmc/articles/PMC9648403/ /pubmed/36387589 http://dx.doi.org/10.3389/fninf.2022.953930 Text en Copyright © 2022 Zhu, Liu, Liu, Shen, You and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Zhu, Xiaoqiang Liu, Xiaomei Liu, Sihu Shen, Yalan You, Lihua Wang, Yimin Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation |
title | Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation |
title_full | Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation |
title_fullStr | Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation |
title_full_unstemmed | Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation |
title_short | Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation |
title_sort | robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648403/ https://www.ncbi.nlm.nih.gov/pubmed/36387589 http://dx.doi.org/10.3389/fninf.2022.953930 |
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