Deep Learning Models

Naming models

Each model is saved in its own repository under the ivadomed organization. The convention for naming repositories is the following:

model_task_animal_pathology_region_contrast_architecture

Should be small letters only.

Fields:
- task = {seg, label, find}, default=seg
- animal = {human, dog, cat, rat, mouse, ...}, default=human
- pathology = {ms, sci}
- region = {sc, gm, csf, brainstem, axon, myelin, ...}, default=sc
- contrast = {t1, t2, t2star, dwi, sem, tem, oi, ...}, default=None
- architecture = {unet2d, unet3d, filmCharley, hemisAndreanne}, default=unet2d

Examples: 

model_seg_monkey_sc_t1_unet3d

# multi-channel, multi-class
model_seg_sc-gm_t1-t2_unet3d

Packaging models

Models to be used by 3rd party software (e.g. SCT) should be uploaded as ‘assets’ to a release of the repository. The steps are:

1. Create a release of the repository. The tag and title of the release should be rYYYYMMDD, example: r20240915.

2. Prepare a folder with the model in the following format. Keep the nnUNet folder structure intact. To save space, include only checkpoint_final.pth. Example:

$ tree model-spinal-rootlets-mp2rage-r20240915.zip
└── nnUNetTrainer_2000epochs__nnUNetPlans__3d_fullres
    ├── dataset.json
    ├── dataset_fingerprint.json
    ├── fold_0
    │         ├── checkpoint_final.pth
    │         ├── debug.json
    │         ├── progress.png
    │         ├── training_log_2024_9_3_14_06_03.png
    │         └── training_log_2024_9_3_14_06_03.txt
    └── plans.json

Important

The dataset.json file should contain the image_orientation entry, for example: "image_orientation": "RPI". This entry is used by SCT to determine the image orientation. But, it is also a good idea to document which orientation was used during training (for the sake of reproducibility).

3. Zip the folder and upload it as an asset in the release

4. Publish the release.