1.1.0 CAM Grid Assay Application Documentation

 

Application Name

CAM Grid Assay

Version

1.1.0

Documentation Version

25.03.2020 - 1

Input Image(s)

2D (standard); RGB

Input Parameter(s)

None

Keywords

CAM, grid, onplant, chorioallantoic membrane, in-vivo, angiogenesis, chick, vessel, growth, microscopy, assay

Short Description

Segmentation of new blood vessels on polymerized grids (“onplants”) placed on chick chorioallantoic membrane assay used in ex-vivo angiogenesis research.

References / Literature

For more information regarding the assay check e.g. https://www.ncbi.nlm.nih.gov/pubmed/19007659 

Table of contents

IKOSA Prisma CAM Grid Assay

You can use this image analysis application or any of our other applications in your account on the IKOSA platform. If it is not on the list of available applications, please contact your organization's administrator or our team at support@ikosa.ai.

Application description

This application automatically measures the number and sizes of blood vessels in microscopy images that are taken from CAM Assay samples. The application is optimized for detecting new blood vessels that grow through polymerized grids (onplants) placed on the CAM small vessels. Since the focus is on neoangiogenesis, larger vessels in the background are not detected.

In the following sections, we provide the necessary input data requirements that are necessary to obtain accurate image analysis results and a description of the output files.

Sample data

To try out this application, sample images can be downloaded here: ikosa-prisma-testdata.zip.

Input data requirements

Input image(s)

Input for this application is the following image data:

Image type

Color channels

Color depth (per channel)

Size (px)

Resolution (μm/px)

Image type

Color channels

Color depth (per channel)

Size (px)

Resolution (μm/px)

2D (standard)

 

Check image format File formats

3 (RGB)

8 Bit

Min: 128 x 128

Max: 6680 x 5239

Min: 0.8

Max: 1.2

Image content

Microscopy image of CAM Assay region with typically 40x magnification and usage of polarization filter.

Additional requirements

  • The background must be yellow to orange, avoid red background.

  • Use a polarization filter while imaging.

Important:

For all images, the following requirements apply:

  • The illumination must be constant throughout the image(s).

  • The sample must be in focus, i.e. no blurry regions in image(s).

Input parameter(s)

No additional input parameters are required for this application.

Description of output files and their content

Files

File format

Description

File format

Description

1

csv

segmentation_stats.csv:

Coverage of conidia/hyphae for each well. See also below (Description of files).

2

jpg

segmentation.jpg:

An image containing for each pixel a confidence value in the range 0-255 for being a vessel (“prediction”).

Results visualization - segmentation

Please note: the image quality might be reduced for the presentation in the technical docs.

3

jpg

results_visualization.jpg:

A combined visualization of the original image, the prediction, and an overlay of original image and prediction.

Results visualization

Reference: Dr. Nassim Ghaffari Tabrizi-Wizsy, Otto Loewi Research Center, Medical University of Graz

Please note: the image quality might be reduced for the presentation in the technical docs.

Content

segmentation_stats.csv

File no. 1: Single csv-file with the following content:

Column NO.

Column name

Examples

Value range

Description

Column NO.

Column name

Examples

Value range

Description

1

total vessel area [pixel]

3668

0 - #of pixels in image

Total area of detected vessels in pixel.

2

number of vessels

7

>=0

Number of vessels detected.

3

mean vessel area [pixel]

524.0

0 - #of pixels in image

Mean size of vessels in pixels.

4

median vessel area [pixel]

336.0

0 - #of pixels in image

Median size of vessels in pixels.

5

mean CNN prediction intensity

0.00708

0 - 1

Mean intensity of prediction in image. A value 0 means no vessels, while 1 would mean that everything is covered by vessels.

6

number of vessels (threshold 0.1)

11

>=0

Number of vessels detected for alternative threshold of prediction (0.1).

7

mean vessel area (threshold 0.1) [pixel]

572.6

0 - #of pixels in image

Mean size of vessels in pixels for alternative threshold of prediction (0.1).

8

median vessel area (threshold 0.1) [pixel]

490.0

0 - #of pixels in image

Median size of vessels in pixels for alternative threshold of prediction (0.1).

Error information

More information about errors can be found in the Application Error Documentation.

Contact

If you have any questions about this app, as well as suggestions or ideas for new ones, email us at support@ikosa.ai.

Feel free to book a 30-minute meeting to speak with us about IKOSA and the apps!

https://calendly.com/kolaido/book-the-ikosa-platform-demo

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