AugLab

This repository investigates the influence of different data augmentation strategies on MRI training performance.

What is available ?

This repository contains:

• A nnUNet trainer with extensive data augmentations
• A basic Monai segmentation script incorporating data augmentations
• A script generating augmentations from input images and segmentations

How to install ?

1. Open a bash terminal in the directory where you want to work.

2. Create and activate a virtual environment using python >=3.10 (highly recommended):

   • venv

   python3 -m venv venv
   source venv/bin/activate
   

   • conda env

   conda create -n myenv python=3.10
   conda activate myenv
   

3. Clone this repository:

   • Git clone

   git clone git@github.com:neuropoly/AugLab.git
   cd AugLab
   

4. Install AugLab using one of the following commands:

   Note: If you pull a new version from GitHub, make sure to rerun this command with the flag --upgrade

   • nnunetv2 only usage

   python3 -m pip install -e auglab
   

   • full usage (with Monai and other dependencies)

   python3 -m pip install -e auglab[all]
   

5. Install PyTorch following the instructions on their website. Be sure to add the --upgrade flag to your installation command to replace any existing PyTorch installation. Example:

python3 -m pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu118 --upgrade

Run nnunet training with AugLab trainer

To use the AugLab trainer with nnUNet, first add the trainer to your nnUNet installation by running:

auglab_add_nnunettrainer --trainer nnUNetTrainerDAExt

Then, when you run nnUNet training as usual, specifying the AugLab trainer, for example:

nnUNetv2_train 100 3d_fullres 0 -tr nnUNetTrainerDAExtGPU -p nnUNetPlans

You can also specify your data augmentation parameters by providing a JSON file using the environment variable AUGLAB_PARAMS_GPU_JSON:

Note: By default auglab/configs/transform_params_gpu.json is used if no file is specified.

AUGLAB_PARAMS_GPU_JSON=/path/to/your/params.json nnUNetv2_train 100 3d_fullres 0 -tr nnUNetTrainerDAExtGPU -p nnUNetPlans

⚠️ Warning : To avoid any paths issues, please specify an absolute path to your JSON file.

How to use my data ?

Scripts developped in this repository use JSON files to specify image and segmentation paths: see this example.

How do I specify my parameters ?

To track parameters used during data augmentation, JSON files are also used: see this example