NeuriteTracer: A novel ImageJ plugin for automated quantification of neurite outgrowth

Pool, Madeline; Thiemann, Joachim; Bar‐Or, Amit; Fournier, Alyson E.

doi:10.1016/j.jneumeth.2007.08.029

Cited by 229 publications

(201 citation statements)

References 24 publications

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“…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.…”

Section: Tree Segmentationmentioning

confidence: 99%

“…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).…”

Section: Tree Segmentationmentioning

confidence: 99%

“…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).…”

Section: Tree Segmentationmentioning

confidence: 99%

“…For measuring neurite length parameters in 2D assays, a popular semiautomatic tool is the NeuronJ plugin to ImageJ, whose corresponding article (58) was the highest cited article in the past decade of all those discussed in the present survey (according to the ISI Web of Science). NeuronJ has been used as a reference tool for testing later 2D neuron tracing algorithms (32,37,48) and has inspired others to develop interactive tools such as Neuromantic (75) for neuron reconstruction in 3D. Higher levels of automation for 2D applications are found in tools such as NeuronMetrics (47), NeuriteTracer (48), and NeuronCyto (32), and for 3D applications in NeuronStudio (46), NeuronIQ (109), ORION (76), and FARSIGHT (101).…”

Section: Segmentation Toolsmentioning

confidence: 99%

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Neuron tracing in perspective

2010

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The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. ' 2010 International Society for Advancement of Cytometry

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Section: Tree Segmentationmentioning

confidence: 99%

Section: Tree Segmentationmentioning

confidence: 99%

Section: Tree Segmentationmentioning

confidence: 99%

Section: Segmentation Toolsmentioning

confidence: 99%

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Neuron tracing in perspective

2010

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“…Although these methods might lack the precision of neuron tracing in case of signal inhomogeneities in the branches, they are well able to detect general changes in neuronal morphology (e.g. neurite length) in response to compounds that affect neurite outgrowth 61 . All global segmentation methods rely on binarization (i.e.…”

Section: Pan-labelled Neuronal Networkmentioning

confidence: 99%

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

Detrez

Verstraelen

Gebuis

et al. 2016

Focus on Bio-Image Informatics

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Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphological plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular communication and the associated calcium-bursting behaviour. In vitro cultured neuronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies.2

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Morphological Assessment of Neurite Outgrowth in Hippocampal Neuron‐Astrocyte Co‐Cultures

Giordano

Costa

2012

CP Toxicology

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Neurite outgrowth is a fundamental event in brain development, as well as in regeneration of damaged neurons. Astrocytes play a major role in neuritogenesis, by expressing and releasing factors that facilitate neurite outgrowth, such as extracellular matrix proteins, and factors that can inhibit neuritogenesis, such as the chondroitin sulfate proteoglycan neurocan. In this unit we describe a noncontact co-culture system of hippocampal neurons and cortical (or hippocampal) astrocytes for measurement of neurite outgrowth. Hippocampal pyramidal neurons are plated on glass coverslips, which are inverted onto an astrocyte feeder layer, allowing exposure of neurons to astrocyte-derived factors without direct contact between these two cell types. After co-culture, neurons are stained and photographed, and processes are assessed morphologically using Metamorph software. This method allows exposing astrocytes to various agents before co-culture in order to assess how these exposures may influence the ability of astrocytes to foster neurite outgrowth.

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NeuriteTracer: A novel ImageJ plugin for automated quantification of neurite outgrowth

Cited by 229 publications

References 24 publications

Neuron tracing in perspective

Neuron tracing in perspective

Image Informatics Strategies for Deciphering Neuronal Network Connectivity

Morphological Assessment of Neurite Outgrowth in Hippocampal Neuron‐Astrocyte Co‐Cultures

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