Aivia Software

Cell Count

The Cell Count recipe in Aivia detects cells in fluorescence microscopy images. This recipe is designed for 2D data.

The recipe measures the morphology and intensity of the detected cells.

This recipe can be used for detection of any bright foreground objects against a dark background.

Parameters and Presets

Parameters

Recipe parameters for Cell Count and their descriptions are summarized in the table below.

Preset Group

Parameter Name

Min Value

Max Value

Description

Preset Group

Parameter Name

Min Value

Max Value

Description

Detection

Background Removal Factor

(Remove Background only)

0

100

Adjusts the sensitivity of the background removal operation; a lower value will preserve larger objects and more background variations

Contrast Threshold (Remove Background)

Intensity Threshold (Skip Remove Background)

0

255 (8-bit)

65,535 (16-bit)

Adjusts the detection sensitivity on the segmentation image; when Remove Background is selected, the threshold will be applied to the background-removed image; whereas when Skip Remove Background is selected, the threshold will be applied to the input image; a lower value will detect bigger and more objects

Fill Holes Size

0

1,000,000 px2 or µm2

Adjusts the maximum size threshold for filling in gaps inside a detected object; a lower value will preserve more holes in the detection

Smoothing Factor

0

100

Adjusts the amount of smoothing applied to the outline of the detected objects; a lower value will preserve more of the object's morphological features

Subset Filtering

Object Size

0

1,000,000,000 px2 or µm2

Specifies the range of objects to be included in the analysis results based on the areas of the detected objects

Separation Factor (Cell Partition only)

0

100

Adjusts the sensitivity of the object separation operation; a lower value will preserve larger objects with multiple intensity peaks


Presets

There are two preset groups in the recipe: Detection and Subset Filtering. Each group has three pre-configured parameter groupings to help you get started on the analysis. The default preset values are in the sections to follow.

 

Detection

Parameter Name

Low

Medium

High

Background Removal Factor

25

55

75

Contrast Threshold

Intensity Threshold

23 (8-bit)

8 (8-bit)

1 (8-bit)

5,898 (16-bit)

1,966 (16-bit)

262 (16-bit)

Fill Holes Size

10 px2 or µm2

25 px2 or µm2

50 px2 or µm2

Smoothing Factor

4

4

4



 

Subset Filtering

Parameter Name

Small

Medium

Large

Object Size

1 - 250 px2 or µm2

50 - 1,000 px2 or µm2

150 - 6,000 px2 or µm2

Separation Factor

50

70

85

 

 

Tutorial (written steps)

Before beginning the tutorial, please download the Cell Count Demo image. For information on how to select presets or modify parameter values, please refer to the tutorial on how to use the Recipe Console.

  1. Unzip the demo file and load the demo image, “CellCountDemo.tif,” into Aivia.

  2. In the Recipe Console, click on the Recipe selection dropdown menu and select the Cell Count recipe.

  3. Select the High preset for the Detection group and the Small preset for the Subset Filtering group.

  4. Click on the Show Advanced Interface icon to expand the Recipe Console and show parameter options for the recipe.

  5. Modify the parameter values in the recipe as follows while leaving the other values intact:

    • Contrast Threshold: 90

    • Separation Factor: 81

  6. Click the Start button or press the F4 key on your keyboard to begin applying the recipe to the image.

The detected object outlines will be overlaid on the image.

 

Cell Count tutorial results

 

Tutorial (videos)

 

Video: Cell Count Tutorial Worked Example (59s)

 

 

Measurements

The Cell Count recipe generates morphological and intensity measurements for each detected cell as well as a count of the total number of cells on the image. You can add additional measurements to the analysis results by using the Measurement Tool in Aivia and explore measurement definitions on the Measurement Definitions page. The measurements generated by the recipe are in the table below.

Morphological

Intensity

Summary

Morphological

Intensity

Summary

  • Area

  • Circularity

  • Component Count

  • Pixel-based Mean Intensity

  • Pixel-based Total Intensity

  • Pixel-based Standard Deviation Intensity

  • Total Component Count

  • Average Mean Intensity