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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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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.