Villa M F, Amthor F R
Department of Computer and Information Sciences, University of Alabama at Birmingham 35294-1170, USA.
J Neurosci Methods. 1995 Jan;56(1):77-88. doi: 10.1016/0165-0270(94)00109-t.
Neurons in the central and peripheral nervous system vary widely in their dendritic branching patterns. Quantification of the morphological characteristics used to identify different classes of neurons and relate neural structure to function requires that accurate metric and non-metric data be obtained from neural images obtained by camera-lucida drawing or from digitized video images made with transmitted, fluorescence or confocal microscopy. This paper describes a largely automated procedure for determining the dendritic tree structure of largely planar cells (such as retinal ganglion cells or cells in tissue culture monolayers) from an initial pictorial representation or digitized image. From this structure, non-metric data (such as the ordered 'tree' of branches) and metric information (such as total dendritic length and dendritic field area) can be automatically computed. The use of this method is specifically illustrated in the capture of the dendritic tree structure of retinal ganglion cells from the rabbit retina.
中枢神经系统和周围神经系统中的神经元，其树突分支模式差异很大。用于识别不同类型神经元并将神经结构与功能相关联的形态学特征的量化，要求从通过明场绘图获得的神经图像或通过透射、荧光或共聚焦显微镜制作的数字化视频图像中获取准确的度量和非度量数据。本文描述了一种主要自动化的程序，用于从初始图像表示或数字化图像中确定主要为平面细胞（如视网膜神经节细胞或组织培养单层中的细胞）的树突树结构。从这种结构中，可以自动计算非度量数据（如分支的有序“树”）和度量信息（如总树突长度和树突野面积）。该方法的应用在从兔视网膜捕获视网膜神经节细胞的树突树结构中得到了具体说明。
