Count-on-it Plugin
Count-on-it Plugin - Related Videos
11:01
Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
0 Views •
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.
07:19
Clock Scan Protocol for Image Analysis: ImageJ Plugins
0 Views •
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.
10:19
Using a Hemacytometer to Count Cells
0 Views •
2023
Many biomedical experiments require manipulation of a known quantity of cells, in order to achieve accurate, reproducible, and statistically-relevant data. Therefore, learning how to count cells is a particularly essential technique for any successful biomedical scientist. The most common way to count cells is by using a hemacytometer - an instrument that bears two laser-etched grids, which aid in the enumeration of an aliquot cells under a simple light microscope. This data can then be used to...
08:14
LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement
0 Views •
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.
05:47
Counting and Determining the Viability of Cultured Cells
0 Views •
Cited by 19 •
2008
Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. In this video, we demonstrate how cells are counted using a hemacytometer.
FAQs
Count-on-it Plugin updates a tally by treating each user-defined selection or detection as a counted object. The researcher examines the displayed digital microscopy image, identifies a biological object according to the selected counting approach, and records it through the plugin. This connects the visual examination directly to an accumulating count instead of requiring separate manual bookkeeping.
User-defined selections or detections determine which image features enter the tally, making the counting approach an important condition of the measurement. The researcher can focus the count on cells, colonies, or particles, depending on the biological question. Applying the same selection approach across images supports more consistent comparisons between samples or experimental groups.
The plugin combines image examination with count recording in the same workflow. Instead of repeatedly looking between an image and a separate tallying system, the researcher registers each selected or detected object as it is examined. This reduces repetitive bookkeeping and creates a more reproducible path from visual observation to quantitative microscopy data.
A typical workflow begins by displaying the digital microscopy image, followed by identifying the object type relevant to the experiment. The researcher then makes user-defined selections or detections as objects are examined, allowing the tally to update during the review. The resulting count can be used as quantitative data for comparing biological samples or conditions.
In biology, the approach can support measurements of cell abundance, colony formation, and particle counts in digital microscopy images. These targets represent different kinds of biological objects, but each can be converted from visual observations into a numerical tally. Such measurements help researchers organize microscopy results for analysis rather than relying only on descriptive inspection.
Counts generated from microscopy images can provide a quantitative basis for examining treatment effects. For example, researchers may compare the abundance of cells or the formation of colonies between samples exposed to different experimental conditions. Because the workflow records selections or detections systematically, it can make visual differences easier to summarize and analyze across observations.