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DeepReg is a freely available, community-supported open-source toolkit for research and education in medical image registration using deep learning.
- TensorFlow 2-based for efficient training and rapid deployment;
- Implementing major unsupervised and weakly-supervised algorithms, with their combinations and variants;
- Focusing on growing and diverse clinical applications, with all DeepReg Demos using open-accessible data;
- Simple built-in command line tools requiring minimal programming and scripting;
- Open, permissible and research-and-education-driven, under the Apache 2.0 license.
- DeepReg.Net
- Documentation, Tutorials, and Quick Start
- Medical Image Registration Demos using DeepReg
- Issue Tracker
Get involved, and help make DeepReg better! We want your help - Really.
Being a contributor doesn't just mean writing code. Equally important to the open-source process is writing or proof-reading documentation, suggesting or implementing tests, or giving feedback about the project. You might see the errors and assumptions that have been glossed over. If you can write any code at all, you can contribute code to open-source. We are constantly trying out new skills, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn with us.
This project is released with a Code of Conduct. By participating in this project, you agree to abide by its terms.
For guidance on making a contribution to DeepReg, see our Contribution Guidelines.
Have a registration application with openly accessible data? Consider contributing a DeepReg Demo.
Our MICCAI Educational Challenge submission on DeepReg is an Award Winner!
Check it out
here -
you can also
Members of the DeepReg dev team presented "The Road to DeepReg" at the Centre for Medical Imaging Computing (CMIC) seminar series at University College London on the 4th of November 2020. You can access the talk here.
DeepReg is research software, made by a team of academic researchers. Citations and use of our software help us justify the effort which has gone into, and will keep going into, maintaining and growing this project.
If you have used DeepReg in your research, please consider citing us:
Fu et al., (2020). DeepReg: a deep learning toolkit for medical image registration. Journal of Open Source Software, 5(55), 2705, https://doi.org/10.21105/joss.02705
Or with BibTex:
@article{Fu2020,
doi = {10.21105/joss.02705},
url = {https://doi.org/10.21105/joss.02705},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {55},
pages = {2705},
author = {Yunguan Fu and Nina Montaña Brown and Shaheer U. Saeed and Adrià Casamitjana and Zachary M. C. Baum and Rémi Delaunay and Qianye Yang and Alexander Grimwood and Zhe Min and Stefano B. Blumberg and Juan Eugenio Iglesias and Dean C. Barratt and Ester Bonmati and Daniel C. Alexander and Matthew J. Clarkson and Tom Vercauteren and Yipeng Hu},
title = {DeepReg: a deep learning toolkit for medical image registration},
journal = {Journal of Open Source Software}
}