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首页> 外文期刊>Journal of Neuroscience Methods >Neurient: An algorithm for automatic tracing of confluent neuronal images to determine alignment
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Neurient: An algorithm for automatic tracing of confluent neuronal images to determine alignment

机译：神经剂：汇合神经元图像自动跟踪算法以确定对齐

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A goal of neural tissue engineering is the development and evaluation of materials that guide neuronal growth and alignment. However, the methods available to quantitatively evaluate the response of neurons to guidance materials are limited and/or expensive, and may require manual tracing to be performed by the researcher. We have developed an open source, automated Matlab-based algorithm, building on previously published methods, to trace and quantify alignment of fluorescent images of neurons in culture. The algorithm is divided into three phases, including computation of a lookup table which contains directional information for each image, location of a set of seed points which may lie along neurite centerlines, and tracing neurites starting with each seed point and indexing into the lookup table. This method was used to obtain quantitative alignment data for complex images of densely cultured neurons. Complete automation of tracing allows for unsupervised processing of large numbers of images. Following image processing with our algorithm, available metrics to quantify neurite alignment include angular histograms, percent of neurite segments in a given direction, and mean neurite angle. The alignment information obtained from traced images can be used to compare the response of neurons to a range of conditions. This tracing algorithm is freely available to the scientific community under the name Neurient, and its implementation in Matlab allows a wide range of researchers to use a standardized, open source method to quantitatively evaluate the alignment of dense neuronal cultures. ? 2013 Elsevier B.V..
机译：神经组织工程的目标是引导神经元生长和对准的材料的开发和评估。然而，可用于定量评估神经元对引导材料的响应的方法是有限的和/或昂贵的，并且可能需要由研究人员执行手动跟踪。我们开发了一种开源，自动化的基于MATLAB的算法，建立先前发表的方法，以追踪和量化培养中神经元荧光图像的对准。该算法被分成三个阶段，包括计算表的查找表，其中包含每个图像的方向信息，一组种子点的位置可以沿着神经突中心线躺着，以及从每个种子点开始跟踪神经癖者并索引到查找表中。该方法用于获得密集培养神经元复杂图像的定量对准数据。完整的跟踪自动化允许无监督的大量图像处理。随着我们的算法进行图像处理，可用度量来量化神经突对准包括角度直方图，给定方向上的神经突段的百分比，以及意味着神经突角。从跟踪图像获得的对准信息可用于将神经元的响应与一系列条件进行比较。这种跟踪算法在名称神经外的科学界自由地提供，其在MATLAB中的实施允许各种研究人员使用标准化的开源方法来定量评估致密神经元培养物的对准。还2013年elestvier b.v ..

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