README.md

DDMR: Deep Deformation Map Registration

Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation

⚠️WARNING: Under construction

DDMR was developed by SINTEF Health Research. The corresponding manuscript describing the framework has been published in PLOS ONE and the manuscript is openly available here.

💻 Getting started

1. Setup virtual environment:

virtualenv -ppython3 venv --clear
source venv/bin/activate

2. Install requirements:

pip install -r requirements.txt

🏋️‍♂️ Training

Use the "MultiTrain" scripts to launch the trainings, providing the neccesary parameters. Those in the COMET folder accepts a .ini configuration file (see COMET/train_config_files for example configurations).

For instance:

python TrainingScripts/Train_3d.py

🔍 Evaluate

Use Evaluate_network to test the trained models. On the Brain folder, use "Evaluate_network__test_fixed.py" instead.

For instance:

python EvaluationScripts/evaluation.py

✨ How to cite

Please, consider citing our paper, if you find the work useful:

@article{perezdefrutos2022ddmr,
    title = {Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation},
    author = {Pérez de Frutos, Javier AND Pedersen, André AND Pelanis, Egidijus AND Bouget, David AND Survarachakan, Shanmugapriya AND Langø, Thomas AND Elle, Ole-Jakob AND Lindseth, Frank},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    year = {2023},
    month = {02},
    volume = {18},
    doi = {10.1371/journal.pone.0282110},
    url = {https://doi.org/10.1371/journal.pone.0282110},
    pages = {1-14},
    number = {2}
}

⭐ Acknowledgements

This project is based on VoxelMorph library, and its related publication:

@article{balakrishnan2019voxelmorph,
    title={VoxelMorph: A Learning Framework for Deformable Medical Image Registration}, 
    author={Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R. and Guttag, John and Dalca, Adrian V.},
    journal={IEEE Transactions on Medical Imaging}, 
    year={2019},
    volume={38},
    number={8},
    pages={1788-1800},
    doi={10.1109/TMI.2019.2897538}
}