1.0.0 Axon Quantification Application Documentation

Owned by fanny.dobrenova (Unlicensed), created
Last updated: Nov 01, 2024 by Philipp Kainz
5 min read

 

Application Name

Axon Quantification

Version

1.0.0

Documentation Version

07.05.2021 - 1

Input Image(s)

2D (WSI); RGB

Input Parameter(s)

Regions of interest (optional)

Keywords

axon, nerve, neuro, peripheral nerve defect, neuroregeneration, regeneration, ex-vivo, trauma, microscopy

Short Description

Detection and quantification of axons in histological sections of peripheral nerves stained with NF200.

References / Literature

Reference laboratory: Ludwig Boltzmann Institute for Experimental and Clinical Traumatology: Mag.rer.nat. Dr. David Hercher; https://trauma.lbg.ac.at/

Table of contents

IKOSA Prisma Axon Quantification

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 segments the neurofilament-positive axons in histological sections of peripheral nerves stained with NF200. Number, area, circumference, roundness/elongation and diameter estimations of the axons are automatically calculated. The application was trained and tested with samples showing peripheral nerves in a rodent (rat) model of neurotmesis (front and hind limbs, proximal of defect, in nerve interpolate and distal of defect).

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 (WSI)

 

Check image format File formats

3 (RGB)

 

8 Bit

WSI formats: arbitrary

typically: 0.37 - 0.62

Image content

Scan of histological  section(s) showing neurofilament-positive axons of rodent (rat) peripheral nerves stained with NF200 (axons appear as brown/reddish objects), typically taken with 20x magnification.

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.

As an optional parameter, a single or multiple regions of interest (ROIs) can be defined in which the analysis should be performed (‘inclusion ROIs’).

Description of output files and their content

Files

File format

Description

File format

Description

1

csv

results.csv:

A csv file containing the overall analysis results for the input image or all inclusion ROIs.

2

csv

results_01_axons.csv:

A csv file containing the analysis results for all detected axons in the input image or inclusion ROIs.

3

jpg

results_vis/vis.jpg (no ROI) or results_vis/<roi-id>.jpg:

A visualization of the analysis result for either the whole image (if no inclusion ROIs selected for analysis) or each individual inclusion ROI. The visualization includes two parts:

  • segmentation

    • predicted axon areas are shown as an overlay in green color.

  • objects

    • axon instances are shown as an overlay in random color.

    • axon instance index number corresponds to the object_id as listed in the results_01_axons.csv file.

4

json

roiMeta.json:

A json file containing all information regarding the ROIs defined for the analysis job to ensure reproducibility.

5

jpg

rois_visualization.jpg:

An overview visualization to show locations of all analyzed ROIs.

Please note: This file is only created, if inclusion ROIs were defined for analysis.

6

json

jobResultBundleMeta.json:

A json file containing all information regarding the analysis job (application name and version, project, etc.) to ensure reproducibility.

Please note: This file is only included, if bundled or merged analysis jobs are downloaded.

Content

results.csv

File no. 1: Single csv-file

If one or more ROIs were specified, the results in a specific row refer to the ROI specified in the first columns, otherwise (empty ROI columns) the results refer to the whole image.

Column NO.

Column name

Examples

Value range

Description

Column NO.

Column name

Examples

Value range

Description

1

roi_id

ROI-03

ROI-01 - 

<roi-id> starting from “ROI1”. Empty, if no inclusion ROI is specified and the whole image was analyzed.

2

roi_name

“central”

text

Custom text to identify the ROI. Empty, if no inclusion ROI is specified and the whole image was analyzed.

3

axons_total_area [Px^2]

122438

0 - no. of image px

Total area covered by detected axons in Pixels^2.

4

axons_total_area [%]

3.66

0 - 100

Total covered by detected axons area as percentage of overall  image area or ROI area inside the image.

5

axons_total_nr_of_objects

3796

0 - 

Total number of detected axons in ROI or image.

results_01_axons.csv

File no. 2: Single csv-file

If one or more ROIs were specified, the results in a specific row refer to the ROI specified in the first columns, otherwise (empty ROI columns) the results refer to the whole image.

Column NO.

Column name

Examples

Value range

Description

Column NO.

Column name

Examples

Value range

Description

1

roi_id

ROI-03

ROI01 - 

<roi-id> starting from “ROI1”. Empty, if no inclusion ROI is specified and the whole image was analyzed.

2

roi_name

“central”

text

Custom text to identify the ROI. Empty if no inclusion ROI is specified and the whole image was analyzed.

3

object_id

5

1 - 

ID of axon corresponding to id in visualization of ROI or image

4

area [Px^2]

132

0 -

Area of detected axon in Pixels^2.

5

perimeter [Px]

12.3

0 -

Perimeter of detected axon in Pixels.

6

elongation

4.21

0 -

Elongation factor according to the method by https://doi.org/10.1007/s10851-007-0039-0.

7

circularity

0.91

 

Circularity factor of detected axon; circularity = 4*pi*area/(perimeter^2). The circularity of a circle is 1.

8

maximum_feret_diameter [Px]

13.6

0 - 

Maximum Feret’s diameter, computed as the longest distance between points around a region’s convex hull contour.

9

mean_diameter [Px]

8.2

0 -

Mean diameter of object which is computed by the object's area divided by the maximum Feret's diameter.

10

mean_diameter_ circle [Px]

10.3

0 -

Diameter of a circle having the same area as the detected object.

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

Copyright 2024 KOLAIDO GmbH. IKOSA® is a registered EU trademark.