| <div align="center"> | |
| <img src="https://user-images.githubusercontent.com/30429725/204778476-4d24c659-9287-48b8-b616-92016ffcf4f6.svg" alt="drawing" width="600"> | |
| </div> | |
| <div align="center"> | |
| <h1 align="center">DDMR: Deep Deformation Map Registration</h1> | |
| <h3 align="center">Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation</h3> | |
| # ⚠️***WARNING: Under construction*** | |
| **DDMR** was developed by SINTEF Health Research. The corresponding manuscript describing the framework has been published in [PLOS ONE](https://journals.plos.org/plosone/) and the manuscript is openly available [here](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282110). | |
| </div> | |
| ## 💻 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: | |
| <pre> | |
| @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} | |
| } | |
| </pre> | |
| ## ⭐ Acknowledgements | |
| This project is based on [VoxelMorph](https://github.com/voxelmorph/voxelmorph) library, and its related publication: | |
| <pre> | |
| @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} | |
| } | |
| </pre> | |