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[R] [P] UNC BIAG Releases Mermaid, Pytorch based image registration toolkit

We are thrilled to release our image registration toolkit after a long time! 🔥🔥

You can quickly prototype and test your image registration pipelines with Mermaid, based on PyTorch. 🌟

By using Mermaid, it is convenient to utilize GPU acceleration for registration models. PRs, questions are welcome! 🙌

We also have another package called easyreg, it wraps Mermaid and uses deep networks.

Github repository: https://github.com/uncbiag/mermaid

Documentation: https://mermaid.readthedocs.io/en/latest/index.html

Related papers:

Region-specific Diffeomorphic Metric Mapping https://arxiv.org/pdf/1906.00139.pdf https://github.com/uncbiag/easyreg

Zhengyang Shen, François-Xavier Vialard, Marc Niethammer. NeurIPS 2019.

Networks for Joint Affine and Non-parametric Image Registration https://arxiv.org/pdf/1903.08811.pdf https://github.com/uncbiag/easyreg

Zhengyang Shen, Xu Han, Zhenlin Xu, Marc Niethammer. CVPR 2019.

Metric Learning for Image Registration https://arxiv.org/pdf/1904.09524.pdf

Marc Niethammer, Roland Kwitt, Francois-Xavier Vialard. CVPR 2019.

Quicksilver: Fast predictive image registration–a deep learning approach https://arxiv.org/pdf/1703.10908.pdf https://github.com/rkwitt/quicksilver

Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer, NeuroImage 2017.

Fast Predictive Image Registration https://github.com/rkwitt/FastPredictiveImageRegistration

Xiao Yang, Roland Kwitt, Marc Niethammer. DLMIA 2016.

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