Neuronj Plugin
Neuronj Plugin - Related Videos
07:19
Clock Scan Protocol for Image Analysis: ImageJ Plugins
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Cited by 16 •
2017
This paper describes two novel ImageJ plugins for 'Clock Scan' image analysis. These plugins expand the functionality of the original visual basic 6 program and, most importantly, make the program available to a large research community by bundling it with the ImageJ free image analysis software package.
11:01
Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
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Cited by 90 •
2016
This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.
08:14
LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
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Cited by 93 •
2013
Demonstration of key methods for high throughput leaf measurements. These methods can be used to accelerate leaf phenotyping when studying many plant mutants or otherwise screening plants by leaf phenotype.
09:39
TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis
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Cited by 3 •
2022
TrackMate Analysis of Calcium Imaging (TACI) is an open-source ImageJ plugin for 3D calcium imaging analysis that examines motion on the z-axis and identifies the maximum value of each z-stack to represent a cell's intensity at the corresponding time point. It can separate neurons overlapping in the lateral (x/y) direction but on different z-planes.
09:07
Genetic Manipulation of Cerebellar Granule Neurons In Vitro and In Vivo to Study Neuronal Morphology and Migration
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Cited by 8 •
2014
Neuronal morphogenesis and migration are crucial events underlying proper brain development. Here, we describe methods to genetically manipulate cultured cerebellar granule neurons and the developing cerebellum for the assessment of morphology and migratory characteristics of neurons.
FAQs
NeuronJ Plugin analyzes intensity information in a microscopy image to follow a neurite along its visible path. This signal helps the software propose a contour rather than requiring the researcher to mark every point manually. Because the user can guide, adjust, and verify the contour, the resulting trace combines computational assistance with visual scientific judgment.
User guidance helps resolve cases in which a neurite path is not represented perfectly by image intensity alone. Researchers can steer the tracing, correct the proposed contour, and verify that it follows the intended process. This interaction reduces reliance on fully manual tracing while preserving researcher oversight of the measurement outcome.
The traced contours support measurements of neurite length, branching, and spatial arrangement. Together, these features describe both the extent of neuronal outgrowth and the organization of processes within a sample. Quantifying several morphological properties allows researchers to compare neuronal structure rather than relying only on qualitative visual inspection.
A typical analysis begins with a microscopy image containing cultured neurons or tissue processes. The researcher uses the plugin to trace neurite paths with image-intensity assistance, guides or adjusts contours when needed, and verifies the final traces. Measurements such as length, branching, and spatial arrangement can then be used for morphological analysis.
Researchers can apply the plugin when they need quantitative information about neuronal morphology in cultured neurons or tissue samples. Its measurements support investigations of neuronal development, regeneration, connectivity, and responses to experimental treatments. The approach is especially relevant when experiments require more structured comparison of neurite organization than visual assessment alone can provide.
Fully manual analysis requires the researcher to identify and trace neuronal processes throughout the image without computational path-following assistance. NeuronJ Plugin instead uses image-intensity information to help follow neurites, while retaining manual guidance and verification. This semi-automated balance can reduce analysis time and subjectivity while keeping the researcher involved in confirming the contours.