A novel method for analyzing images of live nerve cells

Kim, Kwangmin; Kim, Sung-Yeol; Minxha, Juri; Palmore, G. Tayhas R.

doi:10.1016/j.jneumeth.2011.07.017

Cited by 9 publications

(17 citation statements)

References 29 publications

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“…Detecting the exact shape of a soma is a pre-requisite to quantifying neurite length and direction. Soma detection based on DAPI staining 12 or edge detection 14 generates ambiguous shapes of somata, which results in false identification of a soma and hinders precise measurement of neurite lengths. To address this issue, somata are detected by the Laplacian of Gaussian (LoG) filter as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Neurite detection is an essential part of the process of evaluating neuronal responses to external stimuli. Current neurite detection methods (e.g., edge detection, skeletonization) are based on raster representation, and consequently are prone to generate non-continuous neurites or deform the original structures (e.g., dilation, inconsistent thickness) 12 14 . To overcome these inaccuracies, we have modeled neurite detection as a tracing problem, where inferring one-dimensional discrete latent variables are combined with graphical models to give a vector representation of the neurite ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The axons connected to adjacent neurons were not measured. For analysis using edge detection and skeletonization methods, morphological functions in MATLAB were applied to neuron images based on previous studies 12 14 . For analysis using NIA, captured images were processed with our codes, which implemented with the proposed graphical algorithms (see Figs 2 , 3 , 4 and HMM and FCM in Methods ).…”

Section: Methodsmentioning

confidence: 99%

“…Popular algorithms used to analyze neuronal morphology include skeletonization and edge detection 11 . However, methods that use these algorithms 12 13 14 15 still require the merging of two separate sets of images to reveal both immnunostained nuclei and neurites in a neural image, and multi-step adjustments to different images (e.g., threshold levels) prior to analysis. More importantly, we will show that using these open-source methods can result in the (1) loss of signal associated with neurites, (2) generation of artifact signals, and (3) false identification of neuronal structures, all of which prevents precise quantification of changes in neuron morphology post-stimulation.…”

mentioning

confidence: 99%

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Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images

Kim

Son

Palmore

2015

Sci Rep

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Image analysis software is an essential tool used in neuroscience and neural engineering to evaluate changes in neuronal structure following extracellular stimuli. Both manual and automated methods in current use are severely inadequate at detecting and quantifying changes in neuronal morphology when the images analyzed have a low signal-to-noise ratio (SNR). This inadequacy derives from the fact that these methods often include data from non-neuronal structures or artifacts by simply tracing pixels with high intensity. In this paper, we describe Neuron Image Analyzer (NIA), a novel algorithm that overcomes these inadequacies by employing Laplacian of Gaussian filter and graphical models (i.e., Hidden Markov Model, Fully Connected Chain Model) to specifically extract relational pixel information corresponding to neuronal structures (i.e., soma, neurite). As such, NIA that is based on vector representation is less likely to detect false signals (i.e., non-neuronal structures) or generate artifact signals (i.e., deformation of original structures) than current image analysis algorithms that are based on raster representation. We demonstrate that NIA enables precise quantification of neuronal processes (e.g., length and orientation of neurites) in low quality images with a significant increase in the accuracy of detecting neuronal changes post-stimulation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images

Kim

Son

Palmore

2015

Sci Rep

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“…Moreover, the PSD also contains information which can be related to the size of the structures. Spectral methods have been applied to the analysis of e.g., collagen and other fibers in different tissues and biomaterials [ 5 – 10 ], myocytes subjected to magnetic fields [ 11 ], the contractile cytoskeleton of stem cell derived cardiomyocytes [ 12 , 13 ], neurite development [ 14 ] and keratoconic corneas [ 15 ]. In addition to these main classes of methods, approaches combining spatial and frequency domain analysis steps have been reported [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

CytoSpectre: a tool for spectral analysis of oriented structures on cellular and subcellular levels

et al. 2015

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BackgroundOrientation and the degree of isotropy are important in many biological systems such as the sarcomeres of cardiomyocytes and other fibrillar structures of the cytoskeleton. Image based analysis of such structures is often limited to qualitative evaluation by human experts, hampering the throughput, repeatability and reliability of the analyses. Software tools are not readily available for this purpose and the existing methods typically rely at least partly on manual operation.ResultsWe developed CytoSpectre, an automated tool based on spectral analysis, allowing the quantification of orientation and also size distributions of structures in microscopy images. CytoSpectre utilizes the Fourier transform to estimate the power spectrum of an image and based on the spectrum, computes parameter values describing, among others, the mean orientation, isotropy and size of target structures. The analysis can be further tuned to focus on targets of particular size at cellular or subcellular scales. The software can be operated via a graphical user interface without any programming expertise. We analyzed the performance of CytoSpectre by extensive simulations using artificial images, by benchmarking against FibrilTool and by comparisons with manual measurements performed for real images by a panel of human experts. The software was found to be tolerant against noise and blurring and superior to FibrilTool when analyzing realistic targets with degraded image quality. The analysis of real images indicated general good agreement between computational and manual results while also revealing notable expert-to-expert variation. Moreover, the experiment showed that CytoSpectre can handle images obtained of different cell types using different microscopy techniques. Finally, we studied the effect of mechanical stretching on cardiomyocytes to demonstrate the software in an actual experiment and observed changes in cellular orientation in response to stretching.ConclusionsCytoSpectre, a versatile, easy-to-use software tool for spectral analysis of microscopy images was developed. The tool is compatible with most 2D images and can be used to analyze targets at different scales. We expect the tool to be useful in diverse applications dealing with structures whose orientation and size distributions are of interest. While designed for the biological field, the software could also be useful in non-biological applications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0782-y) contains supplementary material, which is available to authorized users.

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The potential of apolipoprotein E4 to act as a substrate for primary cultures of hippocampal neurons

2013

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A novel method for analyzing images of live nerve cells

Cited by 9 publications

References 29 publications

Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images

Neuron Image Analyzer: Automated and Accurate Extraction of Neuronal Data from Low Quality Images

CytoSpectre: a tool for spectral analysis of oriented structures on cellular and subcellular levels

The potential of apolipoprotein E4 to act as a substrate for primary cultures of hippocampal neurons

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