基于可控卷积曲面的三维神经元建模

doi:10.19678/j.issn.1000-3428.0064207

摘要/Abstract

摘要： 在神经科学领域，大脑复杂神经行为的分析需要构造表面光滑且高质量的神经元模型。针对三维神经元形态数据的复杂性，现有三维神经元模型的研究在构造模型的过程中，骨架之间的折痕较大且表面光滑度较低。为解决支撑半径过大导致的卷积过渡混合问题，采用基于采样点密度和半径的算法进行数据预处理并结合骨架的抽象性和卷积曲面的光滑性，提出一种利用局部可变支撑半径控制的卷积曲面混合方法。采用基于VDB的稀疏体素自适应调整空间分辨率提高生成效率，用于解决提取不同半径神经元等值面的速度问题。为验证生成模型数据的有效性，利用MeshLab工具验证网格的水密性并基于Isotropic Remeshing算法重构网格，利用Loop算法细分神经元网格，使其表面更加光滑且包含更多细节信息。为构造在脑神经组织中进行光传播模拟实验的神经元模型，利用TetGen软件生成高质量的神经元四面体模型。实验结果表明，与现有神经元建模方法相比，该方法不仅能有效提高生成速率，而且能生成高阶光滑的网格模型。

关键词: 神经元可视化, 骨架, 隐式曲面, 卷积曲面, 曲面混合, 网格优化

Abstract: In neuroscience, the analysis of complex neural behavior of the brain requires the construction of a smooth and high-quality neuron model.Given the complexity of three-dimensional neuron morphology data, the existing research on the three-dimensional neuron model has the challenges of large creases between skeletons and low surface smoothness when constructing the model.To solve the challenge that the support radius is too large and the convolution transition is mixed, a new method of convolution surface blending using local variable support radius control is proposed.It involves using the algorithm based on the density and radius of sampling points to preprocess the data and combine the abstraction of the skeleton and the smoothness of the convolution surface.The sparse voxels based on VDB usage adaptively adjust the spatial resolution to improve the generation efficiency, thus solving the speed problem of extracting the isosurface of neurons with different radii.The MeshLab tool verifies the water tightness of the mesh and reconstructs the mesh based on the isotropic remeshing algorithm to validate the generated model data.The Loop algorithm subdivides the neuron mesh, making its surface smoother and containing more details.TetGen software generates a high-quality neuron tetrahedron model to construct a neuron model that can execute light propagation simulation experiments in brain nerve tissue.The experimental results show that, compared with the existing neural modeling methods, the proposed method can effectively improve the generation rate and generate high-order smooth mesh models.

Key words: neuron visualization, skeleton, implicit surface, convolution surface, surface blending, grid optimization

中图分类号:

TP391.9

朱晓强, 陈琦. 基于可控卷积曲面的三维神经元建模[J]. 计算机工程, 2023, 49(3): 231-237.

ZHU Xiaoqiang, CHEN Qi. 3D Neuron Modeling Based on Controllable Convolution Surface[J]. Computer Engineering, 2023, 49(3): 231-237.

https://www.ecice06.com/CN/Y2023/V49/I3/231

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参考文献

[1] GLASER JACOB R, GLASER EDMUND M.Neuron imaging with neurolucida-a PC-based system for image combining microscopy[J].Computerized Medical Imaging and Graphics, 1990, 14(5):307-317.
[2] EBERHARD J P, WANNER A, WITTUM G.NeuGen:a tool for the generation of realistic morphology of cortical neurons and neural networks in 3D[J].Neurocomputing, 2006, 70(1/2/3):327-342.
[3] WILSON M A, BHALLA U S, UHLEY J D, et al.GENESIS:a system for simulating neural networks[M]//TOURETZKY D S.Advances in neural information processing systems.New York, USA:ACM Press, 1989:485-492.
[4] AGUIAR P, SOUSA M, SZUCS P.Versatile morphometric analysis and visualization of the three-dimensional structure of neurons[J].Neuroinformatics, 2013, 11(4):393-403.
[5] LASSERRE S, HERNANDO J, HILL S, et al.A neuron membrane mesh representation for visualization of electrophysiological simulations[J].IEEE Transactions on Visualization and Computer Graphics, 2012, 18(2):214-227.
[6] BRITO J P, MATA S, BAYONA S, et al.Neuronize:a tool for building realistic neuronal cell morphologies[J].Frontiers in Neuroanatomy, 2013, 7:15.
[7] GARCIA-CANTERO J J, BRITO J P, MATA S, et al.NeuroTessMesh:a tool for the generation and visualization of neuron meshes and adaptive on-the-fly refinement[J].Frontiers in Neuroinformatics, 2017, 11:38.
[8] ABDELLAH M, HERNANDO J, ANTILLE N, et al.Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies[J].BMC Bioinformatics, 2017, 18(10):39-50.
[9] MCDOUGAL R A, HINES M L, LYTTON W W.Water-tight membranes from neuronal morphology files[J].Journal of Neuroscience Methods, 2013, 220(2):167-178.
[10] MÖRSCHEL K, BREIT M, QUEISSER G.Generating neuron geometries for detailed three-dimensional simulations using anamorph[J].Neuroinformatics, 2017, 15(3):247-269.
[11] KOENE R A, TIJMS B, HEES P, et al.NETMORPH:a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies[J].Neuroinformatics, 2009, 7(3):195-210.
[12] ABDELLAH M, FAVREAU C, HERNANDO J, et al.Generating high fidelity surface meshes of neocortical neurons using skin modifiers[C]//Proceedings of Computer Graphics & Visual Computing.Bangor, UK:[s.n.], 2019:1-10.
[13] KARLSSON J, ABDELLAH M, SPEIERER S, et al.High fidelity visualization of large scale digitally reconstructed brain circuitry with signed distance functions[C]//Proceedings of Visualization Conference.Washington D.C., USA:IEEE Press, 2019:176-180.
[14] HART J C, SANDIN D J, KAUFFMAN L H.Ray tracing deterministic 3-D fractals[J].ACM SIGGRAPH Computer Graphics, 1989, 23(3):289-296.
[15] BLOOMENTHAL J, SHOEMAKE K.Convolution surfaces[J].ACM SIGGRAPH Computer Graphics, 1991, 25(4):251-256.
[16] MCCORMACK J, SHERSTYUK A.Creating and rendering convolution surfaces[J].Computer Graphics Forum, 1998, 17(2):113-120.
[17] NISHIMURA H, HIRAI M, KAWAI T, et al.Object modeling by distribution function and a method of image generation[C]//Proceedings of Electronics Communications Conference.Washington D.C., USA:IEEE Press, 1985:718-725.
[18] JIN X G, TAI C L, ZHANG H L.Implicit modeling from polygon soup using convolution[J].The Visual Computer, 2009, 25(3):279-288.
[19] ZHU X Q, JIN X G, LIU S J, et al.Analytical solutions for sketch-based convolution surface modeling on the GPU[J].The Visual Computer, 2012, 28(11):1115-1125.
[20] LORENSEN W E, CLINE H E.Marching cubes:a high resolution 3D surface construction algorithm[J].ACM SIGGRAPH Computer Graphics, 1987, 21(4):163-169.
[21] MUSETH K.VDB:high-resolution sparse volumes with dynamic topology[J].ACM Transactions on Graphics, 2013, 32(3):1-22.
[22] BOTSCH M, KOBBELT L.A remeshing approach to multiresolution modeling[C]//Proceedings of the Eurographics Symposium on Geometry Processing.New York, USA:ACM Press, 2004:185-192.
[23] CIGNONI P, CORSINI M, RANZUGLIA G.MeshLab:an open-source 3D mesh processing system[J].Ercim News, 2008, 73(6):47-48.
[24] LOOP C T.Smooth subdivision surfaces based on triangles[D].Salt Lake City, USA:The University of Utah, 1987:33-50.
[25] SI H.TetGen, a delaunay-based quality tetrahedral mesh generator[J].ACM Transactions on Mathematical Software, 2015, 41(2):1-36.

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