NeuronMetrics: Software for semi-automated processing of cultured neuron images

Abstract

Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of approximately 60 2D images is 1.0-2.5 h, from a folder of images to a table of numeric data. NeuronMetrics' output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery.

Neuron tracing in perspective

“…Most global processing algorithms implement the following sequence of operations: binarization, skeletonization, rectification, and graph representation. The binarization step, which aims to yield an initial segmentation of the target image structures, is usually implemented by some form of (adaptive) thresholding (25,26,35,(41)(42)(43)(44)(45)(46)(47)(48)(49). However, intensity thresholding, while commonly used for its simplicity and efficiency, is generally known to be one of the most error-prone segmentation methods, and it will be successful only if the staining is sufficiently homogeneous, such that the intensity levels in the structures of interest are significantly and consistently different from the background.…”

“…To obtain a more compact description of the neuronal tree, a common next step is to extract the centerlines of the segmented areas (25,35,36,38,41,42,(45)(46)(47)(48)51,(53)(54)(55)(56)(57), for which various skeletonization algorithms have been proposed. Neurite centerlines may alternatively be obtained directly from the grayscale images, by applying Hessian (58)(59)(60)(61) or Jacobian (62) based analysis of critical points, or by nonmaximum suppression (37,63).…”

“…The result of the skeletonization step often contains errors (such as spurious gaps or branches) and (especially in 2D) ambiguities (spurious loops or crossings). Various filling and pruning strategies have been developed to try to rectify these retrospectively (25,37,38,(46)(47)(48)53,57,59,62,63). Given the centerlines, it is easy to estimate the neurite diameter at each location, for example using the Rayburst algorithm (46,64).…”

Genetic Modifiers ofdFMR1Encode RNA Granule Components in Drosophila

“…Dendrites were quantified using Neuronmetric software (Narro et al 2007), an ImageJ Plug-in. Settings deviating from default settings are listed as follows: rolling ball radius ¼ 1 pixel, gap distance ¼ 20 pixels, extend distance ¼ 20 pixels, maximum deviation ¼ 10 pixels, length threshold ¼ 20 pixels.…”

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

“…Although this binary mask offers a basic measure of the network density, it is often skeletonized to retrieve more detailed parameters, including neurite length and diameter, the number of bifurcations and endpoints. As the resulting skeleton often contains errors (such as spurious gaps or branches), filling and pruning strategies are often used to rectify these retrospectively 63 . In many neuronal network analyses, a measure of cellular density is calculated as well.…”