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Automated three-dimensional tracing of neurons in confocal and brightfield images

机译:共焦和明场图像中神经元的自动三维跟踪

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摘要

Automated three-dimensional (3-D) image analysis methods are presented for tracing of dye-injected neurons imaged by fluorescence confocal microscopy and HRP-stained neurons imaged by transmitted-light brightfield microscopy. An improved algorithm for adaptive 3-D skeletonization of noisy images enables the tracing. This algorithm operates by performing connectivity testing over large N X N X N voxel neighborhoods exploiting the sparseness of the structures of interest, robust surface detection that improves upon classical vacant neighbor schemes, improved handling of process ends or tips based on shape collapse prevention, and thickness-adaptive thinning. The confocal image stacks were skeletonized directly. The brightfield stacks required 3-D deconvolution. The results of skeletonization were analyzed to extract a graph representation. Topological and metric analyses can be carried out using this representation. A semiautomatic method was developed for reconnection of dendritic fragments that are disconnected due to insufficient dye penetration, an imaging deficiency, or skeletonization errors. [References: 58]
机译:提出了自动三维(3-D)图像分析方法,用于跟踪通过荧光共聚焦显微镜成像的染料注射神经元和通过透射光明场显微镜成像的HRP染色神经元。用于噪声图像的自适应3-D骨架化的改进算法可实现跟踪。该算法通过对大型NXNXN体素邻域执行连通性测试,利用感兴趣的结构的稀疏性,鲁棒的表面检测(改进了经典的空缺邻居方案),基于形状塌陷预防的改进的工艺末端或尖端处理以及厚度自适应减薄来进行操作。共焦图像栈直接被骨架化。明场堆栈需要3-D反卷积。分析骨架化的结果以提取图形表示。可以使用此表示法进行拓扑和度量分析。开发了一种半自动方法,用于重新连接由于染料渗透不足,成像不足或骨架化错误而断开的树突片段。 [参考:58]

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