Department of Visualization and Data Analysis, Zuse Institute Berlin, Takustraße 7, 14195, Berlin, Germany.
Neuroinformatics. 2014 Apr;12(2):325-39. doi: 10.1007/s12021-013-9213-2.
Neuroanatomical analysis, such as classification of cell types, depends on reliable reconstruction of large numbers of complete 3D dendrite and axon morphologies. At present, the majority of neuron reconstructions are obtained from preparations in a single tissue slice in vitro, thus suffering from cut off dendrites and, more dramatically, cut off axons. In general, axons can innervate volumes of several cubic millimeters and may reach path lengths of tens of centimeters. Thus, their complete reconstruction requires in vivo labeling, histological sectioning and imaging of large fields of view. Unfortunately, anisotropic background conditions across such large tissue volumes, as well as faintly labeled thin neurites, result in incomplete or erroneous automated tracings and even lead experts to make annotation errors during manual reconstructions. Consequently, tracing reliability renders the major bottleneck for reconstructing complete 3D neuron morphologies. Here, we present a novel set of tools, integrated into a software environment named 'Filament Editor', for creating reliable neuron tracings from sparsely labeled in vivo datasets. The Filament Editor allows for simultaneous visualization of complex neuronal tracings and image data in a 3D viewer, proof-editing of neuronal tracings, alignment and interconnection across sections, and morphometric analysis in relation to 3D anatomical reference structures. We illustrate the functionality of the Filament Editor on the example of in vivo labeled axons and demonstrate that for the exemplary dataset the final tracing results after proof-editing are independent of the expertise of the human operator.
神经解剖学分析,如细胞类型的分类,取决于对大量完整的 3D 树突和轴突形态的可靠重建。目前,大多数神经元重建都是从体外单个组织切片的制备中获得的,因此存在树突被切断的问题,更严重的是,轴突被切断。一般来说,轴突可以支配几个立方毫米的体积,并且可能达到几十厘米的路径长度。因此,它们的完整重建需要在体内标记、组织切片和大视场成像。不幸的是,如此大的组织体积上的各向异性背景条件,以及标记微弱的细神经突,导致自动追踪不完整或错误,甚至导致专家在手动重建过程中出现注释错误。因此,追踪的可靠性成为重建完整 3D 神经元形态的主要瓶颈。在这里,我们提出了一组新的工具,集成到一个名为“Filament Editor”的软件环境中,用于从稀疏标记的体内数据集创建可靠的神经元追踪。Filament Editor 允许在 3D 查看器中同时可视化复杂的神经元追踪和图像数据,对神经元追踪进行校对编辑,在切片之间进行对齐和连接,以及与 3D 解剖参考结构相关的形态计量分析。我们以体内标记的轴突为例展示了 Filament Editor 的功能,并证明对于示例数据集,经过校对编辑后的最终追踪结果与人类操作员的专业知识无关。