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.
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.
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.
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
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.
To train the model, please first update the following fields in the aforementioned JSON configuration file:
gpu_ids: specific to your hardwarepath_output: where the model will be savedloader_parameters:path_data: path to training dataloader_parameters:bids_config: path to the custom bids config located inivadomed/config/config_bids.jsonsplit_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.
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