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README.md

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 license: cc-by-nc-sa-4.0
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+pipeline_tag: image-segmentation
+---
+
+<img src="imgs/nnInteractive_header_white.png">
+
+# Model Checkpoint for `nnInteractive: Redefining 3D Promptable Segmentation`
+
+This repository provides the official checkpoints for `nnInteractive`, a state-of-the-art framework for 3D promptable segmentation. 
+For installation instructions and usage guidance, please refer to the official [python backend](https://github.com/MIC-DKFZ/nnInteractive).
+The backend is designed for seamless integration into Python-based workflows—ideal for researchers, developers, and power users working directly with code.
+`nnInteractive` is also available through graphical viewers (GUI) for those who prefer a visual workflow. The napari and MITK integrations are developed and maintained by our team. Thanks to the community for contributing the 3D Slicer extension!
+
+
+<div align="center">
+
+| **<div align="center">[napari plugin](https://github.com/MIC-DKFZ/napari-nninteractive)</div>** | **<div align="center">[MITK integration](https://www.mitk.org/wiki/MITK-nnInteractive)</div>** | **<div align="center">[3D Slicer extension](https://github.com/coendevente/SlicerNNInteractive)</div>** |
+|-------------------|----------------------|-------------------------|
+| [<img src="imgs/Logos/napari.jpg" width="200">](https://github.com/MIC-DKFZ/napari-nninteractive) | [<img src="imgs/Logos/mitk.jpg" width="200">](https://www.mitk.org/wiki/MITK-nnInteractive) | [<img src="imgs/Logos/3DSlicer.png" width="200">](https://github.com/coendevente/SlicerNNInteractive) |
+
+</div>
+
+
+## What is nnInteractive?
+
+> Isensee, F.\*, Rokuss, M.\*, Krämer, L.\*, Dinkelacker, S., Ravindran, A., Stritzke, F., Hamm, B., Wald, T., Langenberg, M., Ulrich, C., Deissler, J., Floca, R., & Maier-Hein, K. (2025). nnInteractive: Redefining 3D Promptable Segmentation. https://arxiv.org/abs/2503.08373 \
+> *: equal contribution
+
+Link: [![arXiv](https://img.shields.io/badge/arXiv-2503.08373-b31b1b.svg)](https://arxiv.org/abs/2503.08373)
+
+
+##### Abstract:
+
+Accurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation 
+models like SAM have revolutionized interactive segmentation, their 2D design and domain shift limitations make them 
+ill-suited for 3D medical images. Current adaptations address some of these challenges but remain limited, either 
+lacking volumetric awareness, offering restricted interactivity, or supporting only a small set of structures and 
+modalities. Usability also remains a challenge, as current tools are rarely integrated into established imaging 
+platforms and often rely on cumbersome web-based interfaces with restricted functionality. We introduce nnInteractive, 
+the first comprehensive 3D interactive open-set segmentation method. It supports diverse prompts—including points, 
+scribbles, boxes, and a novel lasso prompt—while leveraging intuitive 2D interactions to generate full 3D 
+segmentations. Trained on 120+ diverse volumetric 3D datasets (CT, MRI, PET, 3D Microscopy, etc.), nnInteractive 
+sets a new state-of-the-art in accuracy, adaptability, and usability. Crucially, it is the first method integrated 
+into widely used image viewers (e.g., Napari, MITK), ensuring broad accessibility for real-world clinical and research 
+applications. Extensive benchmarking demonstrates that nnInteractive far surpasses existing methods, setting a new 
+standard for AI-driven interactive 3D segmentation.
+
+<img src="imgs/figure1_method.png" width="1200">
+
+
+## Citation
+When using nnInteractive, please cite the following paper:
+
+> Isensee, F.\*, Rokuss, M.\*, Krämer, L.\*, Dinkelacker, S., Ravindran, A., Stritzke, F., Hamm, B., Wald, T., Langenberg, M., Ulrich, C., Deissler, J., Floca, R., & Maier-Hein, K. (2025). nnInteractive: Redefining 3D Promptable Segmentation. https://arxiv.org/abs/2503.08373 \
+> *: equal contribution
+
+Link: [![arXiv](https://img.shields.io/badge/arXiv-2503.08373-b31b1b.svg)](https://arxiv.org/abs/2503.08373)
+
+
+# License
+Note that the [model checkpoint](https://huggingface.co/nnInteractive/nnInteractive) is `Creative Commons Attribution Non Commercial Share Alike 4.0`! 
+
+## Acknowledgments
+
+<p align="left">
+  <img src="imgs/Logos/HI_Logo.png" width="150"> &nbsp;&nbsp;&nbsp;&nbsp;
+  <img src="imgs/Logos/DKFZ_Logo.png" width="500">
+</p>
+
+This repository is developed and maintained by the Applied Computer Vision Lab (ACVL)
+of [Helmholtz Imaging](https://www.helmholtz-imaging.de/) and the 
+[Division of Medical Image Computing](https://www.dkfz.de/en/medical-image-computing) at DKFZ.