Automatic Nuclei Counter plug-in for ImageJ

ITCN (Image-based Tool for Counting Nuclei) is an ImageJ plugin for counting the number cells within an image. The inputs are: (1) an estimation of the diameter of a cell, (2) an estimation of the minimum distance between cells, and (3) either a region of interest (ROI) selected with ImageJ's selection tools or a black and white mask image that is white in regions that are to be counted. Already more than 1,000 downloads!

Features

Robust automatic nuclei detector
Integrates with popular ImageJ software
Tested on large variety of biological date, such as: confocal, light, etc.

Required Downloads

ITCN Plugin ver. 1.6 (19K, jar include source code)
ImageJ: ImageJ download page

Documentation

Read more for Installation and configuration guide

Introduction

ITCN is an ImageJ plugin for counting the number cells within an image. The inputs are: (1) an estimation of the diameter of a cell, (2) an estimation of the minimum distance between cells, and (3) either a region of interest (ROI) selected with ImageJ's selection tools or a black and white mask image that is white in regions that are to be counted.

Example Images and Parameters

Crop of cat retina INL [parameters: width=20 and min_dist=10]:
NZ403_0001_ONL.tif (15K)
Cat retina INL and ONL stained with TOPRO [parameters: width=10 and min_dist=5]:
3d.cat.topro.z1_0001.tif (265K)
Binary mask selecting INL from previous image:
3d.cat.topro.z1_0001.mask.png (3K)

Installation

Instructions: Download itcn_.jar to the plugins folder of ImageJ and restart ImageJ.

$For windows: Save in C:\Program Files\ImageJ\plugins$ For Macintosh Save in /Applications/ImageJ/plugins

How to Use

Open ImageJ.

Open the image for nuclei counting.

Convert image to 8-bit.

igure 4: Menu commands to convert the image to 8-bit.

Open ITCN plugin.

Check “Image Name” to make sure the proper image is selected.

Figure 7: Check the Image Name in the plugin window.

Enter width into the textfield. The width is an approximation of the diameter of the cells that are to be counted. It's value can be determined using the line selection tool to draw a line that is the diameter of a cell, and pressing the adjacent "Measure Line Length."

Figure 8: Example of width approximation.

Enter minimum distance into the textfield. The minimum distance is an approximation of the distance between nuclei centers. It's value can be determined using the line selection tool to draw a line that is the diameter of a cell, and pressing the adjacent "Measure Line Length." (For tightly spaced cells, use half the distance of the diameter, which is set automatically.)

Figure 9: Example of minimum distance approximation for tightly spaced cells.

Figure 10: Example of minimum distance approximation for loosely spaced cells.

Enter threshold into textfield or use adjacent slider bar. The threshold determines the quality of detected cells, i.e. a higher threshold will detect more obvious cells.

Figure 11: ITCN window and result of threshold at 0.0.

Figure 12: ITCN window and result of threshold at 1.0.

Mark checkbox if you're looking for dark peaks. (Warning: some images in ImageJ have inverted values compared to their appearance, thus this checkbox works incorrectly)

Figure 13: The dark peaks checkbox in the ITCN window.

Select a region of interest by using ImageJ selection tools or open a masked image. The 4 methods of doing so are listed below.

PROCESS THE ENTIRE IMAGE

Figure 14: Setup perform cell counting on the entire image.

Go to Step 11

SELECT A REGION OF INTEREST

Figure 16: Image with selected region of interest.

Go to Step 11

SELECT A MASK FROM THE MENU

Figure 18: Setup to perform cell counting on an image with an opened mask.

Go to Step 11

OPEN A NEW MASK IMAGE

Figure 20: Setup to perform cell counting on an image with a mask opened by the prompt.

Go to Step 11

Click OK if all values are set correctly or click cancel to quit.

View Result image for location of cell centers or the "Results" window for total count and density.

PROCESS THE ENTIRE IMAGE

Figure 21: The result windows for an entire image.

SELECT A REGION OF INTEREST

Figure 22: The result windows for a region of interest selection.

SELECT A MASK FROM THE MENU

Figure 23: The result windows for an pre-opened mask image.

OPEN A NEW MASK IMAGE

Figure 24: The result windows for an opened mask image.

Plans for future releases

Use of magnification information to initially estimate width and minimum distance.
Options for generating a mask.
Automatic determination of cell width.

Known Bugs

Email bugs not listed to thekuo[at]ece.ucsb.edu.

Contact Information

To get more information, give suggestions or comments, or to help develop this program please visit the Center for Bio-Image Informatics website or email thekuo[at]ece.ucsb.edu.

< Prev