| <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> |
| |
