Application Name | Axon Quantification |
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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
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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@kmlvisionsupport@ikosa.comai.
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) |
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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 | ||||
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Important: For all images, the following requirements apply:
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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 | |||||
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| 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
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| 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
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| 7 | json | jobResultBundleMeta.json: A json file containing all information regarding the analysis job (application name and version, project, etc.) to ensure reproducibility
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Please note:
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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 | on_border_num_objects | 12 | 0 - total_num_objects | Number of objects which are detected in the image/ROI and touch the border of the image/ROI. |
5 | holes_num_objects | 3 | 0 - total_num_objects | Number of objects which are detected in the image/ROI and have holes (>=1 pixel). |
6 | recombined_num_objects | 4 | 0 - total_num_objects | Number of objects which are detected at tile borders of image (tile size: 2048x2048 pixels) and are recombined. |
7 | axons_total_area [Px^2] | 122438 | 0 - no. of image px | Total area covered by detected axons in Pixels^2. |
8 | 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. |
9 | 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 | |||
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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 | is_on_border | “True” | “True” or “False” | Boolean indicator to show if object is touching the border of the image/ROI. | |||
6 | has_holes | “False” | “True” or “False” | Boolean indicator to show if object has holes (>=1 pixel). | |||
7 | is_recombined | “False” | “True” or “False” | Boolean indicator to show if object was recombined because it was detected at tile borders of image (tile size: 2048x2048 pixels). | |||
8 | area [Px^2] | 132 | 0 - | Area of detected axon in Pixels^2. | |||
9 | bbox_area [Px^2] | 304 | 8 - | Area of bounding box of detected object in pixels^2. | |||
10 | area_ratio [%] | 0.02 | 0 - 100 | Area of detected object as percentage of overall image area or ROI | |||
11 | perimeter [Px] | 12.3 | 0 - | Perimeter of detected axon in Pixels. | |||
12 | elongation | 4.21 | 0 - | Elongation factor according to the method by https://doi.org/10.1007/s10851-007-0039-0. | |||
13 | circularity | 0.91Circularity factor of detected axon; circularity = 4*pi*area/(perimeter^2). The circularity of a circle is 1. | 14 | circularity | 0.78 | 0 - ~1 | Circularity of detected object. Circularity = 4*pi*area/(perimeter^2). The circularity of a circle is 1. |
1514 | circularity_ISO | 0.65 | 0 - ~1 | Circularity of detected object | |||
1615 | solidity | 0.97 | 0 - 1 | Solidity of detected object. Solidity is calculated out of the ratio of an object’s area to the area of its convex hull. | |||
1716 | eccentricity | 0.92 | 0 - 1 | Eccentricity of detected object. Eccentricity of the ellipse that has the same second-moments as the object area. The eccentricity is the ratio of the focal distance (distance between focal points) over the major axis length. When it is 0, the ellipse becomes a circle. | |||
1817 | 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. | |||
1918 | 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. | |||
2019 | mean_diameter_ circle [Px] | 10.3 | 0 - | Diameter of a circle having the same area as the detected object. | |||
2120 | extent | 0.57 | 0 - 1 | Extent of detected object. The extent value is the ratio of the object area to the total bounding box area. | |||
2221 | minor_axis_length [Px] | 9 | 1 - | Minor axis length of detected object. The minor axis length is the length of the minor axis of the ellipse that has the same normalized second central moments as the object. | |||
2322 | major_axis_length [Px] | 23 | 1 - | Major axis length of detected object. The major axis length is the length of the major axis of the ellipse that has the same normalized second central moments as the object. |
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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@kmlvisionsupport@ikosa.comai.
Feel free to book a 30-minute meeting to speak with us about IKOSA and the apps!
https://calendly.com/kmlvisionkolaido/book-the-ikosa-platform-demo