DeepReg is a freely available, community-supported open-source toolkit for research and education in medical image registration using deep learning.

The current version is implemented as a TensorFlow2-based framework, and contains implementations for unsupervised- and weakly-supervised algorithms with their combinations and variants. DeepReg has a practical focus on growing and diverse clinical applications, as seen in the provided examples - DeepReg Demos.

Get involved and help make DeepReg better!


DeepReg extends and simplifies workflows for medical imaging researchers working in TensorFlow 2, and can be easily installed and used for efficient training and rapid deployment of deep-learning registration algorithms.

DeepReg is designed to be used with minimal programming or scripting, owing to its built-in command line tools.

Our development and all related work involved in the project is public, and released under the Apache 2.0 license.


DeepReg is maintained and led by a team of developers and researchers. People with significant contributions to DeepReg are listed below (in alphabetical order).


Affiliation (at time of contribution)

Adrià Casamitjana

University College London

Alexander Grimwood

University College London

Daniel C. Alexander

University College London

Dean C. Barratt

University College London

Ester Bonmati

University College London

Juan Eugenio Iglesias

University College London / Massachusetts Institute of Technology

Matt J. Clarkson

University College London

Nina Montaña Brown

University College London

Qianye Yang

University College London

Rémi Delaunay

University College London / King’s College London

Shaheer U. Saeed

University College London

Stefano B. Blumberg

University College London

Tom Vercauteren

King’s College London

Yipeng Hu

University College London

Yunguan Fu

University College London / InstaDeep

Zachary M. C. Baum

University College London

Zhe Min

University College London

This open-source initiative started within University College London, with support from the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), and partial support from the Wellcome/EPSRC Centre for Medical Engineering (CME).


For development matters, please raise an issue.

For matters regarding the Code of Conduct, such as a complaint, please email the DeepReg Development Team:

Alternatively, please contact one or more members of the CoC Committee as appropriate: Nina Montana Brown (, Ester Bonmati (, Matt Clarkson (