1.0.0 CAM Grid Assay Application Documentation
- fanny.dobrenova (Unlicensed)
- Philipp Kainz
- Michael Mayrhofer-Reinhartshuber (Unlicensed)
- polina.vinogradova (Unlicensed)
Application Name | CAM Grid Assay |
Version | 1.0.0 |
Documentation Version | 10.10.2019 - 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 in-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.
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) |
|---|---|---|---|---|
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
| ||||
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 | |
|---|---|---|
| 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. |
Content
segmentation_stats.csv
Single csv-file
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). |
Please note: The parameters marked with an asterisk (*) are calculated using https://scikit-image.org/.
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.
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