AxonDeepSeg default TEM model and testing image. This model is suited for a resolution of 0.01 micrometer per pixel and was trained on mouse brain data collected via a Transmission Electron Microscope (TEM).
To segment an image using this model, use the following command in an axondeepseg
virtual environment:
axondeepseg -t TEM -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_mouse_axon-myelin_tem.json configuration file located in this repo.
git clone https://github.com/axondeepseg/default-TEM-model
The TEM dataset used to train this model is hosted on git-annex at data.neuro.polymtl.ca:datasets/data_axondeepseg_tem
. The specific dataset revision hash used for training is documented in the version_info.log file.
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.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_mouse_axon-myelin_tem.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_mouse_axon-myelin_tem.json
The evaluation results will be saved in "path_output"/results_eval/evaluation_3Dmetrics.csv