Hello! 🖖

Here you may find the code written for the paper Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation submitted to MICCAI-2021 conference. Below are the instructions, allowing for reproduction of the experiments.

ln -sfn ~/workspace/domain_adaptation_mri/damri ~/miniconda3/lib/python3.*/site-packages/

where * is the version of your python.

git clone https://github.com/neuro-ml/deep_pipe.git
cd deep_pipe
git checkout develop
pip install -e .

git clone https://github.com/deepmind/surface-distance.git
pip install surface-distance/

Original repository: https://github.com/deepmind/surface-distance

There is a minor error in surface_distance/metrics.py: the line 102 should be commented, please do it (might be already fixed by the time you are reading this)

1. Python: 3.7.6
2. Torch: 1.7.1

1. The path to your local copy of CC359 should be specified here: config/assets/dataset/cc359.config. You should place config/assets/dataset/meta.csv in the same folder with the data. From the available in CC359 ground truths we used the "Silver standard" binary mask (https://sites.google.com/view/calgary-campinas-dataset/download)

2. You should specify the 'device' on which you are going to run an experiment by setting the corresponding variable 'device', in /config/assets/core/ (line 78)

3. To run a single experiment, please follow the steps below:

First, the experiment structure should be created:

dpipe-build /path/to/the/config /path/to/the/experiment
# e.g.
dpipe-build ~/config/experiments/unet2d/unfreeze_first.config ~/dart_results/unfreeze_first

where the first argument is the path to the .config file and the second argument is the path to the folder where the experiment structure will be organized.

Then, to run an experiment please go to the experiment folder inside the created structure (i corresponds to the particular experiment, i.e. to the particular source-target pair):

cd ~/dart_results/unfreeze_first/experiment_{i} 

and call the following command to start the experiment:

dpipe-run ../resources.config

where resources.config is the general .config file of the experiment.

4. First, you should run one2all baseline. The models trained are utilized in the dowstream fine-tuning experiments. You should specify the folder, where these models are stored in the baseline_exp_path variable, which is present in each downstream experiment config.