Snakemake workflow for fetal bold brain segmentation

If you don't have AFNI and FSL installed, you need to use the --use-singularity option when running snakemake .

Training is best with a GPU, but inference can be done reasonably fast with CPU only.

Step 1: Obtain a copy of this workflow

1. Create a new github repository using this workflow as a template .

2. Clone the newly created repository to your local system, into the place where you want to perform the data analysis.

Step 2: Configure workflow

Configure the workflow according to your needs via editing config.yml file, specifically the paths to your nifti images.

Step 3: Install python dependencies

You should install your dependencies in a virtual environment. Once you have activated your virtual environment, you can install the dependencies with pip install .

A recommended alternative that also takes care of creating a virtual environment is to use Poetry. On OSX or Linux can be installed with:

curl -sSL https://install.python-poetry.org | python3 -

Once you have poetry installed you can simply use the following to install dependencies into a virtual environment, then activate it:

cd nnunet-fetalbrain
poetry install
poetry shell

Step 4: Execute workflow

To run inference on your test datasets, use:

snakemake all_test --cores all

By default, the trained model in the config will be downloaded and applied.

If you want to train a new model, set the use_downloaded config variable to one that is not in the download_model , then use:

snakemake all_train --cores all