1.0.0 Spheroid Sprouting Assay Application Documentation

 

Application Name

Spheroid Sprouting Assay

Version

1.0.0

Documentation Version

22.05.2020 - 1

Input Image(s)

2D (standard); RGB and grayscale (RGB images are automatically converted to grayscale images)

Input Parameter(s)

None

Keywords

Spheroid, sprouting, sprout, formation, in-vitro, angiogenesis, vessel, growth, microscopy

Short Description

Detection and quantification of sprouts in a spheroid capillary sprouting assay used for in-vitro angiogenesis research.

References / Literature

For more information regarding the assay check e.g. https://www.ncbi.nlm.nih.gov/pubmed/23911327;

Reference laboratory: Department of Obstetrics and Gynecology: Dr. Ursula Hiden; Jasmin Strutz, MSc;

Table of contents

IKOSA Prisma Spheroid Sprouting 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@kmlvision.com.

Application description

This application automatically segments the body and sprouts created by endothelial cell spheroids in a 3D collagen matrix, and extracts relevant measures (number of sprouts, sprouting lengths, body measures).

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)

Image type

Color channels

Color depth (per channel)

Size (px)

Resolution (μm/px)

2D (standard)

 

Check image format https://kmlvision.atlassian.net/wiki/spaces/KB/pages/3406725124

1 (Grayscale)

3 (RGB)

RGB images are automatically converted to grayscale images

8 Bit

16 Bit

Min: 1280 x 1280

Max: 4096 x 4096

typically: 0.5 - 1.0

Image content

Transmitted light microscopy image of spheroid capillary sprouting assay, typically taken with 10x magnification. In case of multiple bodies in the image, the body closest to the image center is detected and used for sprout analysis.

Additional requirements: None

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

results.csv:

A csv file containing the analysis results for the input image.

Please note: This file is only [created]/[included] if …

2

jpg

results_visualization.jpg:

A visualization of the detection:

  • The segmented body is shown in yellow, while different sprouts are shown in different other colors.

  • In case of multiple bodies in the image, the body closest to the image center is detected and used for sprout analysis..

  • The skeleton, which is used to calculate the sprouting length, is shown in red.

Content

results.csv

Single csv-file

Column NO.

Column name

Examples

Value range

Description

Column NO.

Column name

Examples

Value range

Description

1

total length sprouts [Px]

104

0 -

Total length of all sprouts in pixels.

2

number of sprouts

26

0 - 

Number of detected sprouts.

3

body area [Px^2]

58775

0 -

Area of body in pixels^2.

4

body circularity

0.7644

0 - 1

Circularity of the body calculated as

where A denotes the area of the body and P the perimeter of the body.

Note: Due to the discrete nature of images, the calculation of the perimeter can only be approximated. Therefore, the circularity of a body being a circle, results in a value of approximately 0.9. Theoretically, a circle has a value of 1.0. [1]

[1] Bottema, Murk J. "Circularity of objects in images." 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 00CH37100). Vol. 4. IEEE, 2000.

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@kmlvision.com.

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

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

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