brahimmade / ddmr

Deep learning powered image to image registration, based on VoxelMorph

DDMR: Deep Deformation Map Registration

Train smarter, not harder: learning deep abdominal CT registration on scarce data

⚠️WARNING: Under construction

DDMR was developed by SINTEF Health Research. The corresponding manuscript describing the framework has been submitted to PLOS ONE and the preprint is openly available on arXiv.

💻 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:

@misc{perezdefrutos2022ddmr,
    title = {Train smarter, not harder: learning deep abdominal CT registration on scarce data},
    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},
    year = {2022},
    doi = {10.48550/ARXIV.2211.15717},
    publisher = {arXiv},
    copyright = {Creative Commons Attribution 4.0 International},
    note = {preprint on arXiv at https://arxiv.org/abs/2211.15717}
}

⭐ Acknowledgements

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

@article{VoxelMorph2019,
    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}
}