Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation
Authors: Zhu, X., Liu, X., Liu, S., Shen, Y., You, L. and Wang, Y.
Journal: Frontiers in Neuroinformatics
Volume: 16
eISSN: 1662-5196
DOI: 10.3389/fninf.2022.953930
Abstract: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.
https://eprints.bournemouth.ac.uk/37836/
Source: Scopus
Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation.
Authors: Zhu, X., Liu, X., Liu, S., Shen, Y., You, L. and Wang, Y.
Journal: Front Neuroinform
Volume: 16
Pages: 953930
ISSN: 1662-5196
DOI: 10.3389/fninf.2022.953930
Abstract: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.
https://eprints.bournemouth.ac.uk/37836/
Source: PubMed
Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation
Authors: Zhu, X., Liu, X., Liu, S., Shen, Y., You, L. and Wang, Y.
Journal: FRONTIERS IN NEUROINFORMATICS
Volume: 16
eISSN: 1662-5196
DOI: 10.3389/fninf.2022.953930
https://eprints.bournemouth.ac.uk/37836/
Source: Web of Science (Lite)
Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation.
Authors: Zhu, X., Liu, X., Liu, S., Shen, Y., You, L. and Wang, Y.
Journal: Frontiers in neuroinformatics
Volume: 16
Pages: 953930
eISSN: 1662-5196
ISSN: 1662-5196
DOI: 10.3389/fninf.2022.953930
Abstract: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.
https://eprints.bournemouth.ac.uk/37836/
Source: Europe PubMed Central
Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation
Authors: Zhu, X., Liu, X., Liu, S., Shen, Y., You, L. and Wang, Y.
Journal: Frontiers in Neuroinformatics
Volume: 16
ISSN: 1662-5196
Abstract: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.
https://eprints.bournemouth.ac.uk/37836/
Source: BURO EPrints