model_seg_rabbit_axon-myelin_bf

Model overview

Standard 2-class segmentation model for ADS trained on rabbit samples (Virginia Commonwealth University)

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_rabbit_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 model_seg_rabbit_axon-myelin_bf.json located in this repo.

git clone https://github.com/axondeepseg/model_seg_rabbit_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_vcu.

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: None (NOTE this training configuration did not use a joblib split file. The specific training image hashes can be found at the bottom of the config 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