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首页> 外文期刊>Journal of Computational Neuroscience >Fast extraction of neuron morphologies from large-scale SBFSEM image stacks
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Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

机译：从大规模SBFSEM图像堆栈中快速提取神经元形态

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摘要

Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and the axonal projections, morphological classification is largely defined by the den-drites of a neuron and their subcellular compartments, referred to as dendritic spines. The dimensions of a neuron's dendritic compartment, including its spines, is also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns, changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures and a ID geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines.
机译：神经元形态经常用于对哺乳动物皮质中的细胞类型进行分类。除了躯体的形状和轴突突起以外，形态学分类主要由神经元的树突及其亚细胞区室（称为树突棘）定义。神经元的树突区室的尺寸，包括其棘突，也是树突的被动和主动电兴奋性的主要决定因素。此外，树突状分支和棘的尺寸在产后发育过程中发生变化，并且可能会遵循某些类型的神经元活动模式而根据神经元的活动而变化。由于它们的尺寸小，难以准确地定量出脊柱的数目和结构（Larkman，J Comp Neurol 306：332，1991）。在这里，我们遵循使用高分辨率EM技术的分析方法。串行块面扫描电子显微镜（SBFSEM）使高分辨率的大样品体积自动成像成为可能。通过这种技术生成的大量数据集使人工重建神经元结构变得费力。在这里，我们介绍了NeuroStruct，这是一种重建环境，可使用针对CPU和GPU硬件的优化算法对包含单个染色神经元的大型SBFSEM数据集进行快速自动分析。 NeuroStruct基于3D运算符，整合了充满生物胞素并被四氧化染色的单个神经元图像堆栈的图像信息。提出的工作的重点是树突分支的重建与棘刺的详细表示。 NeuroStruct既提供了重建结构的3D表面模型，又提供了与重建结构的骨架相对应的ID几何模型。两种表示形式都是分析捕获棘突影响的神经元内的形态特征和模拟信号的前提。

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  • 来源
    《Journal of Computational Neuroscience》 |2011年第3期|p.533-545|共13页
  • 作者

    Stefan Lang; Panos Drouvelis; Enkelejda Tafaj; Peter Bastian; Bert Sakmann;

  • 作者单位

    Interdisciplinary Center for Scientific Computing, Im Neuenheimer Feld 368, 69120, Heidelberg, Germany,Max Planck Florida Institute, 5353 Parkside Drive, MC19-RE, Jupiter, FL 33458-2906, USA;

    Interdisciplinary Center for Scientific Computing, Im Neuenheimer Feld 368, 69120, Heidelberg, Germany,Max Planck Florida Institute, 5353 Parkside Drive, MC19-RE, Jupiter, FL 33458-2906, USA;

    Interdisciplinary Center for Scientific Computing, Im Neuenheimer Feld 368, 69120, Heidelberg, Germany;

    Interdisciplinary Center for Scientific Computing, Im Neuenheimer Feld 368, 69120, Heidelberg, Germany;

    Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Martinsried, Germany,Max Planck Florida Institute, 5353 Parkside Drive, MC19-RE, Jupiter, FL 33458-2906, USA;

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  • 正文语种 eng
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  • 关键词

    SBFSEM; segmentation; reconstruction of neurons; image processing; GPGPU computing;

    机译：扫描电镜分割;神经元重建;图像处理;GPGPU计算;

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