model_seg_rat_axon-myelin_bf

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Model overview

Image courtesy of Gregory Borschel and Simeon Daeschler.

Default Bright-Field (BF) optical microscopy model that works at a resolution of 0.1 micrometer per pixel.

Segment (ADS)

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

axondeepseg -t BF -i <IMG_PATH> -s <PIXEL_SIZE>

The -m option can be omitted in this case because this is a default built-in model.

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_seg_rat_axon-myelin_bf.json configuration file located in this repo.

git clone https://github.com/axondeepseg/default-BF-model

Get the data

The dataset used to train this model is hosted on git-annex at data.neuro.polymtl.ca:datasets/data_axondeepseg_bf_training. The dataset revision hash used for training is f833b905c2cb221d45b2ef5ac2fad1100e70b410.

Train this model

To train the model, please first update the following fields in the aforementioned JSON configuration 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:bids_config: path to the custom bids config located in ivadomed/config/config_bids.json
• split_dataset:fname_split: path to the split_dataset.joblib file

Then, you can train the model with

ivadomed --train -c path/to/model_seg_rat_axon-myelin_bf.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/model_seg_rat_axon-myelin_bf.json

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