README.md

---
license: cc-by-nc-sa-4.0
pipeline_tag: image-segmentation
---

<img src="imgs/nnInteractive_header_white.png">

# nnInteractive: Redefining 3D Promptable Segmentation

This repository hosts the official model checkpoints for `nnInteractive`, a state-of-the-art framework for 3D promptable segmentation.

> 📖 **Full documentation, installation, and usage live in the GitHub repository:**
> 👉 **[github.com/MIC-DKFZ/nnInteractive](https://github.com/MIC-DKFZ/nnInteractive)**
>
> Please refer to GitHub for the Python backend, installation instructions, code examples, the SuperVoxel module, and the changelog. This page only covers downloading the checkpoint.

`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 further integrations!

<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>** | **<div align="center">[ITK-SNAP extension](https://itksnap-dls.readthedocs.io/en/latest/quick_start.html)</div>** | **<div align="center">[OHIF integration](https://github.com/CCI-Bonn/OHIF-AI)</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) | [<img src="imgs/Logos/snaplogo_sq.png" width="200">](https://itksnap-dls.readthedocs.io/en/latest/quick_start.html) | [<img src="imgs/Logos/ohif.png" width="200">](https://github.com/CCI-Bonn/OHIF-AI) |

</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">

## Downloading the checkpoint

You normally don't need to download the weights manually — the napari, MITK, and other integrations, as well as the Python backend, fetch them for you. If you want the raw checkpoint, the snippet below pulls it from this repository:

```python
from huggingface_hub import snapshot_download  # pip install huggingface_hub

REPO_ID = "nnInteractive/nnInteractive"
MODEL_NAME = "nnInteractive_v1.0"  # Updated models may be available in the future

download_path = snapshot_download(
    repo_id=REPO_ID,
    allow_patterns=[f"{MODEL_NAME}/*"],
)
# The checkpoint is now in download_path/MODEL_NAME.
```

For how to actually run inference with these weights, see the [GitHub README](https://github.com/MIC-DKFZ/nnInteractive).

## 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
The model checkpoint hosted in this repository is licensed under `Creative Commons Attribution Non Commercial Share Alike 4.0` (CC-BY-NC-SA-4.0); see [`nnInteractive_v1.0/LICENSE`](nnInteractive_v1.0/LICENSE). Note that the [Python backend code](https://github.com/MIC-DKFZ/nnInteractive) is released separately under the Apache-2.0 license.

## 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.