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

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!

napari plugin	MITK integration	3D Slicer extension	ITK-SNAP extension	OHIF integration

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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:

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.

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:

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.

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:

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. Note that the Python backend code is released separately under the Apache-2.0 license.

Acknowledgments

    

This repository is developed and maintained by the Applied Computer Vision Lab (ACVL) of Helmholtz Imaging and the Division of Medical Image Computing at DKFZ.