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. Install this repository using one of the following options:

   • Git clone (for developpers)

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

   git clone [email protected]:neuropoly/AugLab.git
   cd AugLab
   python3 -m pip install -e .

4. 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

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