Introduction

AxonDeepSeg is an open-source software using deep learning and aiming at automatically segmenting axons and myelin sheaths from microscopy images. It performs 3-class semantic segmentation using a convolutional neural network.

AxonDeepSeg was developed at NeuroPoly Lab, Polytechnique Montreal, University of Montreal, Canada.

Installation

The following sections will help you install all the tools you need to run AxonDeepSeg.

Note

Starting with Version 2.0, AxonDeepSeg supports the Windows operating system. However, please note that our continuous integration testing framework (TravisCI) only tests AxonDeepSeg for Unix-style systems, so releases may be more unstable for Windows than Linux/macOS.

Miniconda

Starting with version 4.0.0, AxonDeepSeg is only supported using Python 3.8.x. Although your system may already have a Python environment installed, we strongly recommend that AxonDeepSeg be used with Miniconda, which is a lightweight version of the Anaconda distribution. Miniconda is typically used to create virtual Python environments, which provides a separation of installation dependencies between different Python projects. Although it can be possible to install AxonDeepSeg without Miniconda or virtual environments, we will only provide instructions for this recommended installation setup.

First, verify if you already have an AxonDeepSeg-compatible version of Miniconda or Anaconda properly installed and is in your systems path.

In a new terminal window (macOS or Linux) or Anaconda Prompt (Windows – if it is installed), run the following command::

conda search python

If a list of available Python versions are displayed and versions >=3.8.0 are available, you may skip to the next section (git).

Linux

To install Miniconda, run the following commands in your terminal::

cd ~
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda
echo ". ~/miniconda/etc/profile.d/conda.sh" >> ~/.bashrc
source ~/.bashrc

Note

If conda isn’t available on new terminal sessions after running these steps, it’s possible that your system is configured to use a different startup script. Try adding the line source ~/.bashrc to your ~/.bash_profile file. See here for more details.

macOS

To install Miniconda, run the following commands in your terminal::

cd ~
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda
echo ". ~/miniconda/etc/profile.d/conda.sh" >> ~/.bash_profile
source ~/.bash_profile

Windows

Note

The AxonDeepSeg installation instruction using the Miniconda have only been tested for Windows 10. Older versions of Windows may not be compatible with the tools required to run AxonDeepSeg.

To install Miniconda, go to the Miniconda installation website and click on the Python 3.x version installer compatible with your Windows system (64 bit recommended). After the download is complete, execute the downloaded file, and follow the instructions. If you are unsure about any of the installation options, we recommend you use the default settings.

git

git is a software version control system. Because AxonDeepSeg is hosted on GitHub, a service that hosts git repositories, having git installed on your system allows you to download the most up-to-date development version of AxonDeepSeg from a terminal, and also allows you to contribute to the project if you wish to do so.

To install git, please follow instructions for your operating system on the git website

Install AxonDeepSeg

To install AxonDeepSeg, in a terminal window (macOS or Linux) or Anaconda Prompt (Windows), “clone” AxonDeepSeg’s repository (you will need to have git installed on your system) and then open the directory:

git clone https://github.com/neuropoly/axondeepseg.git
cd axondeepseg

Virtual environments are a tool to separate the Python environment and packages used between Python projects. They allow for different versions of Python packages to be installed and managed for the specific needs of your projects. There are several virtual environment managers available, but the one we recommend and will use in our installation guide is conda, which is installed by default with Miniconda. We strongly recommend you install into a virtual environment.

Note

Linux systems can accelerate some of AxonDeepSeg’s functions with an NVIDIA GPU, but these are expensive and rare, and if you do not own one you can save some time and space by not downloading the accelerated codes. You can do this by putting this in your pip.conf before continuing:

# ~/.config/pip/pip.conf
[install]
extra-index-url =
  https://download.pytorch.org/whl/cpu

To setup the Python virtual environment with all the required packages, run the following command:

conda env create

Warning

For some users, the installation may take up to 30 minutes as many dependencies have shared subdependencies, and resolving these potential conflicts takes time. If that’s the case, we encourage you to take a break from your screen and go for a walk while listening to the AxonDeepSeg Spotify playlist.

Then, activate your virtual environment:

conda activate ads_venv

Note

To switch back to your default environment, run:

conda deactivate

Once your virtual environment is installed and activated, install the AxonDeepSeg software with the following commands:

pip install -e . plugins/

Note

To update an already cloned AxonDeepSeg package, pull the latest version of the project from GitHub and re-install the application:

cd axondeepseg
git pull
pip install -e . plugins/

Warning

When re-installing the application, the model folders in AxonDeepSeg/models will be deleted and re-downloaded. Please do not store valuable data in these folders.

Testing the installation

Warning

Ensure that the virtual environment is activated.

Quick test

To test if the software was installed correctly, you can launch a quick integrity test by running the following command on the terminal:

axondeepseg_test

This integrity test automatically performs the axon and myelin segmentation of a test sample. If the test succeeds, the following message will appear in the terminal:

* * * Integrity test passed. AxonDeepSeg is correctly installed. * * *

Comprehensive test

To run the entire testing suite (more code coverage), go to your AxonDeepSeg project directory on the terminal and run py.test:

cd axondeepseg
py.test --cov AxonDeepSeg/ --cov-report term-missing

If all tests pass, AxonDeepSeg was installed succesfully.

Graphical User Interface (GUI)

AxonDeepSeg can be run via a Graphical User Interface (GUI) instead of the Terminal command line. This GUI is a plugin for the software Napari. Beyond the convenience of running AxonDeepSeg with the click of a button, this GUI is also an excellent way to manually correct output segmentations (if needed).

https://raw.githubusercontent.com/axondeepseg/doc-figures/main/introduction/napari_image.png

Launch Napari

napari

In Napari, do the following: - Click on Plugins -> ADS plugin (napari-ads)

Below is a short tutorial describing how to use the AxonDeepSeg plugin for Napari.

Existing models

Three models are available and shipped together with the installation package, so you don’t need to install them separately. The three models are described below:

SEM model (model_seg_rat_axon-myelin_sem), that works at a resolution of 0.1 micrometer per pixel. For more information, please visit the SEM model repository.
TEM model (model_seg_mouse_axon-myelin_tem), that works at a resolution of 0.01 micrometer per pixel. For more information, please visit the TEM model repository.
BF (bright-field) model (model_seg_rat_axon-myelin_bf, formerly called model_seg_pns_bf), that works at a resolution of 0.1 micrometer per pixel. For more information, please visit the BF model repository.

Using AxonDeepSeg

Activate the virtual environment

To use AxonDeepSeg, you must first activate the virtual environment if it isn’t currently activated. To do so, run:

conda activate ads_venv

Note

To switch back to your default environment, run:

conda deactivate

Example dataset

You can demo the AxonDeepSeg by downloading the test data available here. It contains two SEM test samples and one TEM test sample.

Segmentation

Syntax

The script to launch is called axondeepseg. It takes several arguments:

Required arguments:

-t MODALITY: Type of acquisition to segment.

SEM: scanning electron microscopy samples.

TEM: transmission electron microscopy samples.

BF: bright field optical microscopy samples.
-i IMGPATH: Path to the image to segment or path to the folder where the image(s) to segment is/are located.

Optional arguments:

-m MODEL: Folder where the model is located, if different from the default model.
-s SIZEPIXEL: Pixel size of the image(s) to segment, in micrometers. If no pixel size is specified, a pixel_size_in_micrometer.txt file needs to be added to the image folder path ( that file should contain a single float number corresponding to the resolution of the image, i.e. the pixel size). The pixel size in that file will be used for the segmentation.
-v VERBOSITY: Verbosity level.

0 (default): Quiet mode. Shows minimal information on the terminal.

1: Developer mode. Shows more information on the terminal, useful for debugging..
--overlap OVERLAP: Overlap value (in pixels) of the patches when doing the segmentation. Higher values of overlap can improve the segmentation at patch borders, but also increase the segmentation time. Default value: 48. Recommended range of values: [10-100].
-z ZOOM: Zoom factor. When applying the model, the size of the segmentation patches relative to the image size will change according to this factor.
--no-patch: Flag to segment the image without using patches. The “no-patch” flag supersedes the “overlap” flag. This option could potentially produce better results but may not be suitable with large images depending on computer RAM capacity.
--gpu-id GPU_ID: Number representing the GPU ID for segmentation if available. Default: 0.

Note

You can get the detailed description of all the arguments of the axondeepseg command at any time by using the -h argument:

axondeepseg -h

Segment a single image

To segment a single microscopy image, specify the path to the image to segment in the -i argument. For instance, to segment the SEM image ‘77.png’ of the test dataset that has a pixel size of 0.07 micrometers, use the following command:

axondeepseg -t SEM -i test_segmentation/test_sem_image/image1_sem/77.png -s 0.07

The script will use the explicitely specified size argument (here, 0.07) for the segmentation. If no pixel size is provided in the arguments, it will automatically read the image resolution encoded in the file ‘pixel_size_in_micrometer.txt’ if that file exists in the folder containing the image to segment. The segmented acquisition will be saved in the same folder as the acquisition image, with the suffix ‘_seg-axonmyelin.png’, in png format, along with the binary axon and myelin segmentation masks (with the suffixes ‘_seg-axon.png’ and ‘_seg-myelin.png’). In our example, the following output files will be generated: ‘77_seg-axonmyelin.png’, ‘77_seg-axon.png’ and ‘77_seg-myelin.png’.

To segment the same image by using the ‘pixel_size_in_micrometer.txt’ file in the folder (i.e. not specifying the pixel size as argument in the command), use the following command:

axondeepseg -t SEM -i test_segmentation/test_sem_image/image1_sem/77.png

Segment multiple images of the same resolution

To segment multiple microscopy images of the same resolution that are located in the same folder, specify the path to the folder in the -i argument. For instance, to segment the images in folder ‘test_sem_image/image1_sem/’ of the test dataset that have a pixel size of 0.07 micrometers, use the following command:

axondeepseg -t SEM -i test_segmentation/test_sem_image/image1_sem/ -s 0.07

To segment multiple images of the same folder and of the same resolution by using the ‘pixel_size_in_micrometer.txt’ file in the folder (i.e. not specifying the pixel size as argument in the command), use the following folder structure:

folder_with_samples/
├── image_1.png
├── image_2.png
├── image_3.png
├── ...
└── pixel_size_in_micrometer.txt

Then, use the following command:

axondeepseg -t SEM -i test_segmentation/test_sem_image/image1_sem/

Please note that when using axondeepseg, a file called axondeepseg.log will be saved in the current working directory. The console output will be saved in this file so you can review it later (useful to process large folders).

Segment images from multiple folders

To segment images that are located in different folders, specify the path to the folders in the -i argument, one after the other. For instance, to segment all the images of folders ‘test_sem_image/image1_sem/’ and ‘test_sem_image/image2_sem/’ of the test dataset, use the following command:

axondeepseg -t SEM -i test_segmentation/test_sem_image/image1_sem/ test_segmentation/test_sem_image/image2_sem/

Segment images using a zoom factor

Sometimes, the quality of the segmentation can be improved by changing the size of the segmentation patches so that, for example, the size of the axons within the segmentation patches are closer to the size that they were during the training of the model. This is why we provide the -z argument, which lets you specify a zoom factor to adjust the segmentation patch sizes relative to the image size. Note that this option also works for multiple images or multiple folders.

For example, using a zoom value of 2.0 will make the patches 2x smaller relative to the image

axondeepseg -t SEM -i test_segmentation/test_sem_image/image1_sem/77.png -s 0.07 -z 2.0

Using the zoom factor can also be useful when your image size is too small for a given resolution, as our segmentation models resample images to a standard pixel size. Using the zoom factor effectively enlarges your image so that the patches can then fit inside it. If you encounter this issue but have not set a zoom factor, an error message will appear informing you of the minimum zoom factor you should use.

Segment an image using a range of zoom factors

As mentioned above, choosing an appropriate zoom factor can enhance the quality of your segmentations. To facilitate the process of finding the best zoom value, we provide a feature that sweeps a range of zoom factors. To use the zoom factor sweep on a single image, you can adjust the range of values to sweep using the -r argument and the number of equidistant values to sample within this range using the -l argument. The lower bound of the range is inclusive whereas the upper bound is exclusive.

For example, using a range of 0.5 to 3 and a length of 5 on the the ‘77.png’ image image will create a folder called ‘77_sweep’ in that folder containing segmentations for zoom factors 0.5, 1.0, 1.5, 2.0, and 2.5:

axondeepseg -t SEM -i test_segmentation/test_sem_image/image1_sem/77.png -s 0.13 -r 0.5 3.0 -l 5

Morphometrics

You can generate morphometrics using AxonDeepSeg via the command line interface.

Syntax

The script to launch is called axondeepseg_morphometrics. It has several arguments.

Required arguments:

-i IMGPATH: Path to the image file whose morphometrics needs to be calculated.

Optional arguments:

-s SIZEPIXEL: Pixel size of the image(s) to segment, in micrometers. If no pixel size is specified, a pixel_size_in_micrometer.txt file needs to be added to the image folder path (that file should contain a single float number corresponding to the resolution of the image, i.e. the pixel size). The pixel size in that file will be used for the morphometrics computation.
-a AXONSHAPE: Axon shape circle: Axon shape is considered as circle. In this case, diameter is computed using equivalent diameter. ellipse: Axon shape is considered as an ellipse. In this case, diameter is computed using ellipse minor axis. The default axon shape is set to circle.
-f FILENAME: Name of the excel file in which the morphometrics will be stored. The excel file extension can either be .xlsx or .csv. If name of the excel file is not provided, the morphometrics will be saved as axon_morphometrics.xlsx.
-b: Flag to extract additionnal bounding box information on axonmyelin objects. Specifying this option -b flag will add a boolean value indicating if the axon touches one of the image border. It will also output every axon’s bounding box (including its myelin). For more information, see the morphometrics file description in the subsection below.
-c: Flag to save the colorized instance segmentation. For more information about this feature, see the Colorization subsection below.

Morphometrics of a single image

Before computing the morphometrics of an image, make sure it has been segmented using AxonDeepSeg

axondeepseg_morphometrics -i test_segmentation/test_sem_image/image1_sem/77.png -a circle -f axon_morphometrics

This generates a ‘77_axon_morphometrics.xlsx’ file in the image directory:

image1_sem/
├── 77.png
├── 77_seg-axon.png
├── 77_seg-axonmyelin.png
├── 77_seg-myelin.png
├── 77_axon_morphometrics.xlsx
└── pixel_size_in_micrometer.txt

Morphometrics of a specific image from multiple folders

To generate morphometrics of images which are located in different folders, specify the path of the image folders using the -i argument of the CLI separated by space. For instance, to compute morphometrics of the image ‘77.png’ and ‘image.png’ present in the folders ‘test_sem_image/image1_sem/’ and ‘test_sem_image/image2_sem/’ respectively of the test dataset, use the following command:

axondeepseg_morphometrics -i test_segmentation/test_sem_image/image1_sem/77.png test_segmentation/test_sem_image/image2_sem/image.png

This will generate ‘77_axon_morphometrics.xlsx’ and ‘image_axon_morphometrics.xlsx’ files in the ‘image1_sem’ and ‘image2_sem’ folders:

--image1_sem/
---- 77.png
---- 77_seg-axon.png
---- 77_seg-axonmyelin.png
---- 77_seg-myelin.png
---- 77_axon_morphometrics.xlsx
---- pixel_size_in_micrometer.txt

...

--image2_sem/
---- image.png
---- image_seg-axon.png
---- image_seg-axonmyelin.png
---- image_seg-myelin.png
---- image_axon_morphometrics.xlsx
---- pixel_size_in_micrometer.txt

Morphometrics of all the images present in folder(s)

To compute the morphometrics of batches of images present in folder(s), input the path of the directories using the -i argument separated by space. For example, the morphometrics files of the images present in the directories ‘test_sem_image/image1_sem/’ and ‘test_sem_image/image2_sem/’ are computed using the following CLI command:

axondeepseg_morphometrics -i test_segmentation/test_sem_image/image1_sem test_segmentation/test_sem_image/image2_sem

This will generate ‘77_axon_morphometrics.xlsx’ and ‘78_axon_morphometrics.xlsx’ morphometrics files in the ‘image1_sem’ directory. And, the ‘image_axon_morphometrics.xlsx’ and ‘image2_axon_morphometrics.xlsx’ morphometrics files are generated in the ‘image2_sem’ directory:

--image1_sem/
---- 77.png
---- 77_seg-axon.png
---- 77_seg-axonmyelin.png
---- 77_seg-myelin.png
---- 77_axon_morphometrics.xlsx

---- 78.png
---- 78_seg-axon.png
---- 78_seg-axonmyelin.png
---- 78_seg-myelin.png
---- 78_axon_morphometrics.xlsx

---- pixel_size_in_micrometer.txt

...

--image2_sem/
---- image.png
---- image_seg-axon.png
---- image_seg-axonmyelin.png
---- image_seg-myelin.png
---- image_axon_morphometrics.xlsx

---- image2.png
---- image2_seg-axon.png
---- image2_seg-axonmyelin.png
---- image2_seg-myelin.png
---- image2_axon_morphometrics.xlsx

---- pixel_size_in_micrometer.txt

Please note that when using the axondeepseg_morphometrics command, the console output will be logged in a file called axondeepseg.log in the current working directory.

Axon Shape: Circle vs Ellipse

Circle

Usage

axondeepseg_morphometrics -i test_segmentation/test_sem_image/image1_sem/77.png -a circle

Studies using Circle as axon shape:

Duval et al: https://pubmed.ncbi.nlm.nih.gov/30326296/
Salini et al: https://www.frontiersin.org/articles/10.3389/fnana.2017.00129/full

Ellipse

Usage

axondeepseg_morphometrics -i test_segmentation/test_sem_image/image1_sem/77.png -a ellipse

Studies using Ellipse as axon shape:

Payne et al: https://pubmed.ncbi.nlm.nih.gov/21381867/
Payne et al: https://pubmed.ncbi.nlm.nih.gov/22879411/
Fehily et al: https://pubmed.ncbi.nlm.nih.gov/30702755/

Note

In the literature, both equivalent diameter and ellipse minor axis are used to compute the morphometrics. Thus, depending on the usecase, the user is advised to choose axon shape accordingly.

Morphometrics file

The resulting ‘axon_morphometrics.csv/xlsx’ file will contain the following columns headings. Most of the metrics are computed using skimage.measure.regionprops.

By default for axon shape, that is, circle, the equivalent diameter is used. For ellipse axon shape, minor axis is used as the diameter. The equivalent diameter is defined as the diameter of a circle with the same area as the region.

Field	Description
x0	Axon X centroid position in pixels.
y0	Axon Y centroid position in pixels.
gratio	Ratio between the axon diameter and the axon+myelin (fiber) diameter (gratio = axon_diameter / axonmyelin_diameter).
axon_area	Area of the axon region in \({\mu}\)m².
axon_perimeter	Perimeter of the axon object in \({\mu}\)m.
myelin_area	Difference between axon+myelin (fiber) area and axon area in \({\mu}\)m².
axon_diameter	Diameter of the axon in \({\mu}\)m.
myelin_thickness	Half of the difference between the axon+myelin (fiber) diameter and the axon diameter in \({\mu}\)m.
axonmyelin_area	Area of the axon+myelin (fiber) region in \({\mu}\)m².
axonmyelin_perimeter	Perimeter of the axon+myelin (fiber) object in \({\mu}\)m.
solidity	Ratio of pixels in the axon region to pixels of the convex hull image.
eccentricity	Eccentricity of the ellipse that has the same second-moments as the axon region.
orientation	Angle between the 0th axis (rows) and the major axis of the ellipse that has the same second moments as the axon region.
image_border_touching	Flag indicating if the axonmyelin objects touches the image border
bbox_min_y	Minimum y value of the bounding box (in pixels). This bound is inclusive.
bbox_min_x	Minimum x value of the bounding box (in pixels). This bound is inclusive.
bbox_max_y	Maximum y value of the bounding box (in pixels). This bound is exclusive.
bbox_max_x	Maximum x value of the bounding box (in pixels). This bound is exclusive.

Colorization

During the morphometrics computation, axondeepseg internally converts the semantic segmentation (output of the deep learning model) into an instance segmentation. This step is essential to take measurements on individual axons when the axon density is high, because if two or more elements have their myelin touching, the software needs to know which axon it is attached to. Using the -c flag, you can obtain the colorized instance segmentation to take a look at this internal representation. The image below illustrates what a typical instance segmentation looks like.

https://raw.githubusercontent.com/axondeepseg/doc-figures/main/introduction/instance_seg_example.png

Jupyter notebooks

Here is a list of useful Jupyter notebooks available with AxonDeepSeg:

00-getting_started.ipynb:
Notebook that shows how to perform axon and myelin segmentation of a given sample using a Jupyter notebook (i.e. not using the command line tool of AxonDeepSeg). You can also launch this specific notebook without installing and/or cloning the repository by using the Binder link.
01-performance_metrics.ipynb:
Notebook that computes a large set of segmentation metrics to assess the axon and myelin segmentation quality of a given sample (compared against a ground truth mask). Metrics include sensitivity, specificity, precision, accuracy, Dice, Jaccard, F1 score, Hausdorff distance.
02-morphometrics_extraction.ipynb:
Notebook that shows how to extract morphometrics from a sample segmented with AxonDeepSeg. The user can extract and save morphometrics for each axon (diameter, solidity, ellipticity, centroid, …), estimate aggregate morphometrics of the sample from the axon/myelin segmentation (g-ratio, AVF, MVF, myelin thickness, axon density, …), and generate overlays of axon/myelin segmentation masks, colocoded for axon diameter.

Note

To open a notebook, go to the notebooks/ subfolder of AxonDeepSeg and launch a particular notebook as follows:

cd notebooks
jupyter notebook name_of_the_notebook.ipynb

Warning

The current models available for segmentation are trained for patches of 256x256 pixels for SEM and 512x512 pixels for TEM and BF. This means that your input image(s) should be at least 256x256 or 512x512 pixels in size after the resampling to the target pixel size of the model you are using to segment.

For instance, the TEM model currently available has a target resolution of 0.01 micrometers per pixel, which means that the minimum size of the input image (in micrometers) is 5.12x5.12.

Option: If your image to segment is too small, you can use padding to artificially increase its size (i.e. add empty pixels around the borders).

Manual correction of segmentation masks

If the segmentation with AxonDeepSeg does not give optimal results, you can try one of the following options:

Napari plugin

Open image and mask

Open Napari by entering napari in the terminal (virtual environment must be activated).
Load the AxonDeepSeg plugin using the Napari toolbar: Plugins -> ADS plugin (napari-ads)
Load the microscopy image using the Napari toolbar: File -> Open file(s)
If no segmentation masks already exists:
- Choose one of AxonDeepSeg’s default models in the dropdown menu “Select the model”
- Then click on the Apply ADS model button
If a segmentation masks already exists:
- Click on the “Load mask” button and select the image with the suffix “_seg-axonmyelin”
After a mask is loaded or generated, the axon (blue) and myelin (red) layers will be overlayed on top of the histology image.
In the “layer list” panel on the left, you will find 3 layers (image, axon mask, and myelin mask).
- To show or hide layers, click on the eye icon.
- To edit a layer, make sure that it is highlighted by clicking on it. In the following example, the myelin layer is selected.

To zoom on the image, use two fingers on your trackpad and swipe up (zoom in) or down (zoom out), or use the zoom wheel on your mouse.
- If it’s not working, ensure that the “Pan/zoom mode” button (magnifying icon ) is selected on the left “layers control” panel.
To pan on the image, click and drag your trackpad or mouse.

Modify the mask

Click the mask (myelin or axon) that you want to modify in the “layer list” panel.
To edit the mask you chose, select one of the three editing modes in the “layer control” panel on the left.
- Paint brush : Add pixels to the mask.
  
  The size of the paint brush is determined by the “brush size” option in the “layer list” panel.
- Eraser : Remove pizels from the mask.
  
  The size of the eraser is also determined by the “brush size” option in the “layer list” panel.
- Bucket tool : Fills a closed area of the mask with the values of that same mask.

Note

Zooming and panning are disabled while editing the mask. To regain these functionalities, click on the magnifying icon to re-activate it.

The “Fill axons” button in the AxonDeepSeg plugin (right panel) can also be used to edit the masks, and overall can speed up your workflow.

Note

The “Fill axon” button will fill closed myelin mask areas by painting in the axon mask. A good workflow if starting from scratch would be to manually segment all the myelin in the image and then click the “Fill axons” button to fill in the axon areas.

Warning

The “Fill axons” functionality will not behave properly if there are myelin objects not closed, or if multiple myelin objects touch each other to form a big closed cluster.

Modify the mask

Click the “Save segmentation” button in the AxonDeepSeg plugin (right panel).
Note: In case of an overlap between the axons mask and the myelin mask, the myelin will have priority when saving the new segmentation.
The “_seg-axon.png” and “_seg-myelin.png” are the axons-only and myelin-only binary masks.
The “_seg-axonmyelin.png” file is the axon+myelin mask.
- Note that this mask is a PNG 8-bit file with 1 channel (256 grayscale), with color values of 0 for background, 127 for myelin and 255 for axons.

GIMP software

To create a new axon+myelin manual mask or to make manual correction on an existing segmentation mask, you can use the GIMP software (Link for download).
If you are making correction on an existing segmentation mask, note that when you launch a segmentation, in the folder output, you will also find the axon and myelin masks (with the suffixes ‘_seg-axon.png’ and ‘_seg-myelin.png’). You can then manually correct the myelin mask and create a corrected axon+myelin mask.
For a detailed procedure, please consult the following link: Manual labelling with GIMP.

Training Models

To train your own model for use in AxonDeepSeg, please refer to the IVADOMED documentation on model training for two-class microscopy images.

Help

Whether you are a newcomer or an experienced user, we will do our best to help and reply to you as soon as possible. Of course, please be considerate and respectful of all people participating in our community interactions.

If you encounter difficulties during installation and/or while using AxonDeepSeg, or have general questions about the project, you can start a new discussion on AxonDeepSeg GitHub Discussions forum. We also encourage you, once you’ve familiarized yourself with the software, to continue participating in the forum by helping answer future questions from fellow users!
If you encounter bugs during installation and/or use of AxonDeepSeg, you can open a new issue ticket on the AxonDeepSeg GitHub issues tracker.

Citation

If you use this work in your research, please cite:

Zaimi, A., Wabartha, M., Herman, V., Antonsanti, P.-L., Perone, C. S., & Cohen-Adad, J. (2018). AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks. Scientific Reports, 8(1), 3816. Link to the paper.