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[<img src="https://github.com/uncbiag/unigradicon/actions/workflows/test_readme_works.yml/badge.svg">](https://github.com/uncbiag/unigradicon/actions) [![arXiv](https://img.shields.io/badge/arXiv-2403.05780-b31b1b.svg)](https://arxiv.org/abs/2403.05780)
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This the official repository for `uniGradICON`: A Foundation Model for Medical Image Registration
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## News
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- 🔥 uniGradICON [Slicer Extension](#slicer-extension) is available in [3D Slicer](https://www.slicer.org/) 5.7.0 Extensions Manager (12/2024)
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## Introduction
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`uniGradICON` is based on [GradICON](https://github.com/uncbiag/ICON) but trained on several different datasets (see details below).
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The result is a deep-learning-based registration model that works well across datasets. More results can be found [here](/demos/Examples.md). To get you started quickly there is also an easy to use [Slicer extension](#-inferece-via-slicer-extension).
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<div align="center">
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<img src="IntroFigure.jpg" width=550 alt="teaser">
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</div>
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This is the repository for the following papers
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**uniGradICON: A Foundation Model for Medical Image Registration**
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Tian, Lin and Greer, Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Estepar, Raul San Jose and Bouix, Sylvain and Rushmore, Richard and Niethammer, Marc
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_MICCAI 2024_ https://arxiv.org/abs/2403.05780

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