Application Name | Axon Quantification |
|---|---|
Version | 3.0.0 |
Documentation Version | 30.01.2024 - 1 |
Input Image(s) | 2D (standard and/or 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@kmlvision.com.
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 interponate 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) |
|---|---|---|---|---|
2D (standard and/or WSI) Check image format File formats | 3 (RGB) | 8 Bit | WSI formats: arbitrary Standard images: max. 25,000 x 25,000 | 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 | |
|---|---|---|
| 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:
   Reference: Dr. David Hercher, Ludwig Boltzmann Institute for Experimental and Clinical Traumatology Please note: These files are only created if qualitative result visualization was requested when submitting the analysis job. Please note: the image quality might be reduced for the presentation in the technical docs. |
| 4 | json | annotation_results.json: JSON file containing detected axons. The position is measured from the left upper corner (1,1) of the image. |
| 5 | json | roiMeta.json: A json file containing all information regarding the ROIs defined for the analysis job to ensure reproducibility. Also, information regarding image and analysis dimensions is provided. |
| 6 | 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. |
| 7 | 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. |
Please note:
In the case of inclusion ROIs that are partially outside of the image, the ROIs are cropped to the areas that lie inside the image.
In the case of inclusion ROIs that are completely outside of the image, no analysis is performed. However, they are still listed in corresponding results files.
A <roi-id> is generated automatically by the application corresponding to the creation date of a ROI. The location of a ROI within an image with its specific <roi-id> can be seen in the file “rois_visualization.jpg.” ROIs that are completely outside of the image are not shown in this file.
All visualizations are downscaled to 25 megapixels (MP), if the original image or inclusion ROI is larger than 25 MP.
Content
results.csv
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 |
|---|---|---|---|---|
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 | roi_size [Px^2] | 1212212 | 1 - | Size of the ROI that was analyzed in pixels^2. The size of the whole image is given if no inclusion ROI is specified and the whole image was analyzed. |
4 | axons_total_area [Px^2] | 122438 | 0 - no. of image px | Total area covered by detected axons in Pixels^2. |
5 | 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. |
6 | axons_total_nr_of_objects | 3796 | 0 - | Total number of detected axons in ROI or image. |
results_01_axons.csv
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 |
|---|---|---|---|---|
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 | roi_size [Px^2] | 1212212 | 1 - | Size of the ROI that was analyzed in pixels^2. The size of the whole image is given if no inclusion ROI is specified and the whole image was analyzed. |
4 | object_id | 5 | 1 - | ID of axon corresponding to id in visualization of ROI or image |
5 | area [Px^2] | 132 | 0 - | Area of detected axon in Pixels^2. |
6 | perimeter [Px] | 12.3 | 0 - | Perimeter of detected axon in Pixels. |
7 | elongation | 4.21 | 0 - | Elongation factor according to the method by https://doi.org/10.1007/s10851-007-0039-0. |
8 | circularity | 0.91 | Circularity factor of detected axon; circularity = 4*pi*area/(perimeter^2). The circularity of a circle is 1. | |
9 | 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. |
10 | 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. |
11 | mean_diameter_ circle [Px] | 10.3 | 0 - | Diameter of a circle having the same area as the detected object. |
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!