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DeepReg is a freely available, community-supported open-source toolkit for researchand 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 theircombinations and variants;
• Focusing on growing and diverse clinical applications, with all DeepReg Demos usingopen-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 theopen-source process is writing or proof-reading documentation, suggesting orimplementing tests, or giving feedback about the project. You might see the errors andassumptions that have been glossed over. If you can write any code at all, you cancontribute code to open-source. We are constantly trying out new skills, makingmistakes, and learning from those mistakes. That's how we all improve, and we are happyto help others learn with us.

This project is released with aCode of Conduct.By participating in this project, you agree to abide by its terms.

For guidance on making a contribution to DeepReg, see ourContribution Guidelines.

Have a registration application with openly accessible data? Considercontributing a DeepReg Demo.

OurMICCAI Educational Challengesubmission on DeepReg is an Award Winner!

Check it outhere -you can also

Members of the DeepReg dev team presented "The Road to DeepReg" at the Centre forMedical Imaging Computing (CMIC) seminar series at University College London on the 4thof November 2020. You can access the talkhere.

DeepReg is research software, made by ateam of academic researchers.Citations and use of our software help us justify the effort which has gone into, andwill keep going into, maintaining and growing this project.

If you have used DeepReg in your research, please consider citing us:

Fuet 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}}