Tip

For an updated version of this model trained with nnunetv2, see axondeepseg/default-wakehealth-model

model_seg_human_axon-myelin_bf

---

Model overview

This Bright-Field (BF) optical microscopy model works at a resolution of 0.226 micrometer per pixel and was trained on human tibial nerve data stained with Toluidine Blue.

Segment (ADS)

To segment an image using this model, use the following command in an axondeepseg virtual environment:

axondeepseg -t BF -i <IMG_PATH> -m path/to/model_seg_human_axon-myelin_bf -s <PIXEL_SIZE>

Train and test (ivadomed)

This model was trained and tested with ivadomed. We recommend you install ivadomed in a virtual environment to reproduce the original training steps. The specific revision hash of the version used for training is documented in the version_info.log file.

Clone this repository

You will need the model's JSON configuration file named <FILENAME.json> located in this repo.

git clone https://github.com/axondeepseg/model_seg_human_axon-myelin_bf

Get the data

The dataset used to train this model is hosted on git-annex at data.neuro.polymtl.ca:datasets/data_axondeepseg_wakehealth_training. The specific dataset revision hash used for training is documented in the version_info.log file.

Train this model

To train the model, please first update the following fields in the training config file:

• gpu_ids: specific to your hardware
• path_output: where the model will be saved
• loader_parameters:path_data: path to training data
• loader_parameters:fname_split: full path to the split_dataset.joblib file
• bids_config: path to the custom bids config usually located in ivadomed/config/config_bids.json

Then, you can train the model with

ivadomed --train -c path_to_config_file.json

The trained model file will be saved under the path_output directory. For more information about training models in ivadomed, please refer to the following tutorial.

Evaluate this model

To test the performance of this model, use

ivadomed --test -c path_to_config_file.json

The evaluation results will be saved in "path_output"/results_eval/evaluation_3Dmetrics.csv