| Name | nnInteractive JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | Inference code for nnInteractive |
| upload_time | 2025-08-01 09:25:56 |
| maintainer | None |
| docs_url | None |
| author | Helmholtz Imaging Applied Computer Vision Lab |
| requires_python | >=3.10 |
| license | Apache License
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| keywords |
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<img src="imgs/nnInteractive_header_white.png">
# Python backend for `nnInteractive: Redefining 3D Promptable Segmentation`
This repository provides the official Python backend for `nnInteractive`, a state-of-the-art framework for 3D promptable segmentation. It 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>** | **<div align="center">[ITK-SNAP extension](https://itksnap-dls.readthedocs.io/en/latest/quick_start.html)</div>** |
|-------------------|----------------------|-------------------------|-------------------------|
| [<img src="imgs/Logos/napari.jpg" height="200">](https://github.com/MIC-DKFZ/napari-nninteractive) | [<img src="imgs/Logos/mitk.jpg" height="200">](https://www.mitk.org/wiki/MITK-nnInteractive) | [<img src="imgs/Logos/3DSlicer.png" height="200">](https://github.com/coendevente/SlicerNNInteractive) | [<img src="imgs/Logos/snaplogo_sq.png" height="200">](https://itksnap-dls.readthedocs.io/en/latest/quick_start.html) |
</div>
## 📰 News
- **07/2025**: 🧩 New ITK-SNAP extension released! Try nnInteractive directly in ITK-SNAP 👉 [Quick Start](https://itksnap-dls.readthedocs.io/en/latest/quick_start.html)
- **06/2025**: 🏆 We’re thrilled to announce that `nnInteractive` **won the 1st place** in the [CVPR 2025 Challenge on Interactive 3D Segmentation](https://www.codabench.org/competitions/5263/). Huge shoutout to the organizers and all contributors!
- **05/2025**: `nnInteractive` presents an **official baseline** at **CVPR 2025** in the _Foundation Models for Interactive 3D Biomedical Image Segmentation Challenge_ ([Codabench link](https://www.codabench.org/competitions/5263/)) → see [`nnInteractive/inference/cvpr2025_challenge_baseline`](nnInteractive/inference/cvpr2025_challenge_baseline)
- **04/2025**: 🎉 The **community contributed a 3D Slicer integration** – thank you! 👉 [SlicerNNInteractive](https://github.com/coendevente/SlicerNNInteractive)
- **03/2025**: 🚀 `nnInteractive` **launched** with native support for **napari** and **MITK**
---
## 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: [](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">
## Installation
### Prerequisites
You need a Linux or Windows computer with a Nvidia GPU. 10GB of VRAM is recommended. Small objects should work with \<6GB.
##### 1. Create a virtual environment:
nnInteractive supports Python 3.10+ and works with Conda, pip, or any other virtual environment. Here’s an example using Conda:
```
conda create -n nnInteractive python=3.12
conda activate nnInteractive
```
##### 2. Install the correct PyTorch for your system
Go to the [PyTorch homepage](https://pytorch.org/get-started/locally/) and pick the right configuration.
Note that since recently PyTorch needs to be installed via pip. This is fine to do within your conda environment.
For Ubuntu with a Nvidia GPU, pick 'stable', 'Linux', 'Pip', 'Python', 'CUDA12.6' (if all drivers are up to date, otherwise use and older version):
```
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126
```
##### 3. Install this repository
Either install via pip:
`pip install nninteractive`
Or clone and install this repository:
```bash
git clone https://github.com/MIC-DKFZ/nnInteractive
cd nnInteractive
pip install -e .
```
## Getting Started
Here is a minimalistic script that covers the core functionality of nnInteractive:
```python
import os
import torch
import SimpleITK as sitk
from huggingface_hub import snapshot_download # Install huggingface_hub if not already installed
# --- Download Trained Model Weights (~400MB) ---
REPO_ID = "nnInteractive/nnInteractive"
MODEL_NAME = "nnInteractive_v1.0" # Updated models may be available in the future
DOWNLOAD_DIR = "/home/isensee/temp" # Specify the download directory
download_path = snapshot_download(
repo_id=REPO_ID,
allow_patterns=[f"{MODEL_NAME}/*"],
local_dir=DOWNLOAD_DIR
)
# The model is now stored in DOWNLOAD_DIR/MODEL_NAME.
# --- Initialize Inference Session ---
from nnInteractive.inference.inference_session import nnInteractiveInferenceSession
session = nnInteractiveInferenceSession(
device=torch.device("cuda:0"), # Set inference device
use_torch_compile=False, # Experimental: Not tested yet
verbose=False,
torch_n_threads=os.cpu_count(), # Use available CPU cores
do_autozoom=True, # Enables AutoZoom for better patching
use_pinned_memory=True, # Optimizes GPU memory transfers
)
# Load the trained model
model_path = os.path.join(DOWNLOAD_DIR, MODEL_NAME)
session.initialize_from_trained_model_folder(model_path)
# --- Load Input Image (Example with SimpleITK) ---
input_image = sitk.ReadImage("FILENAME")
img = sitk.GetArrayFromImage(input_image)[None] # Ensure shape (1, x, y, z)
# Validate input dimensions
if img.ndim != 4:
raise ValueError("Input image must be 4D with shape (1, x, y, z)")
session.set_image(img)
# --- Define Output Buffer ---
target_tensor = torch.zeros(img.shape[1:], dtype=torch.uint8) # Must be 3D (x, y, z)
session.set_target_buffer(target_tensor)
# --- Interacting with the Model ---
# Interactions can be freely chained and mixed in any order. Each interaction refines the segmentation.
# The model updates the segmentation mask in the target buffer after every interaction.
# Example: Add a **positive** point interaction
# POINT_COORDINATES should be a tuple (x, y, z) specifying the point location.
session.add_point_interaction(POINT_COORDINATES, include_interaction=True)
# Example: Add a **negative** point interaction
# To make any interaction negative set include_interaction=False
session.add_point_interaction(POINT_COORDINATES, include_interaction=False)
# Example: Add a bounding box interaction
# BBOX_COORDINATES must be specified as [[x1, x2], [y1, y2], [z1, z2]] (half-open intervals).
# Note: nnInteractive pre-trained models currently only support **2D bounding boxes**.
# This means that **one dimension must be [d, d+1]** to indicate a single slice.
# Example of a 2D bounding box in the axial plane (XY slice at depth Z)
# BBOX_COORDINATES = [[30, 80], [40, 100], [10, 11]] # X: 30-80, Y: 40-100, Z: slice 10
session.add_bbox_interaction(BBOX_COORDINATES, include_interaction=True)
# Example: Add a scribble interaction
# - A 3D image of the same shape as img where one slice (any axis-aligned orientation) contains a hand-drawn scribble.
# - Background must be 0, and scribble must be 1.
# - Use session.preferred_scribble_thickness for optimal results.
session.add_scribble_interaction(SCRIBBLE_IMAGE, include_interaction=True)
# Example: Add a lasso interaction
# - Similarly to scribble a 3D image with a single slice containing a **closed contour** representing the selection.
session.add_lasso_interaction(LASSO_IMAGE, include_interaction=True)
# You can combine any number of interactions as needed.
# The model refines the segmentation result incrementally with each new interaction.
# --- Retrieve Results ---
# The target buffer holds the segmentation result.
results = session.target_buffer.clone()
# OR (equivalent)
results = target_tensor.clone()
# Cloning is required because the buffer will be **reused** for the next object.
# Alternatively, set a new target buffer for each object:
session.set_target_buffer(torch.zeros(img.shape[1:], dtype=torch.uint8))
# --- Start a New Object Segmentation ---
session.reset_interactions() # Clears the target buffer and resets interactions
# Now you can start segmenting the next object in the image.
# --- Set a New Image ---
# Setting a new image also requires setting a new matching target buffer
session.set_image(NEW_IMAGE)
session.set_target_buffer(torch.zeros(NEW_IMAGE.shape[1:], dtype=torch.uint8))
# Enjoy!
```
## nnInteractive SuperVoxels
As part of the `nnInteractive` framework, we provide a dedicated module for **supervoxel generation** based on [SAM](https://github.com/facebookresearch/segment-anything) and [SAM2](https://github.com/facebookresearch/sam2). This replaces traditional superpixel methods (e.g., SLIC) with **foundation model–powered 3D pseudo-labels**.
🔗 **Module:** [`nnInteractive/supervoxel/`](nnInteractive/supervoxel)
The SuperVoxel module allows you to:
- Automatically generate high-quality 3D supervoxels via axial sampling + SAM segmentation and SAM2 mask propagation.
- Use the generated supervoxels as **pseudo-ground-truth labels** to train promptable 3D segmentation models like `nnInteractive`.
- Export `nnUNet`-compatible `.pkl` foreground prompts for downstream use.
For detailed installation, configuration, and usage instructions, check the [SuperVoxel README](nnInteractive/supervoxel/README.md).
## 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: [](https://arxiv.org/abs/2503.08373)
# License
Note that while this repository is available under Apache-2.0 license (see [LICENSE](./LICENSE)), the [model checkpoint](https://huggingface.co/nnInteractive/nnInteractive) is `Creative Commons Attribution Non Commercial Share Alike 4.0`!
# Changelog
### 1.1.1 - 2025-08-01
- We now detect whether linux kernel 6.11 is used and disable pin_memory in that case. See also [here](https://github.com/MIC-DKFZ/nnInteractive/issues/18)
- ✅ API compatible with 1.0.1 and 1.1.0
### 1.1.0 - 2025-08-01
- Reworked inference code. It's now well-structured and easier to follow.
- Fixed bugs that
- sometimes caused blocky predictions
- may cause failure to update segmentation map if changes were minor and AutoZoom was triggered
- ✅ API compatible with 1.0.1
## Acknowledgments
<p align="left">
<img src="imgs/Logos/HI_Logo.png" width="150">
<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.
Raw data
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"keywords": "deep learning, image segmentation, semantic segmentation, medical image analysis, medical image segmentation, nnU-Net, nnunet",
"author": "Helmholtz Imaging Applied Computer Vision Lab",
"author_email": "Fabian Isensee <f.isensee@dkfz-heidelberg.de>",
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"description": "<img src=\"imgs/nnInteractive_header_white.png\">\n\n# Python backend for `nnInteractive: Redefining 3D Promptable Segmentation`\n\nThis repository provides the official Python backend for `nnInteractive`, a state-of-the-art framework for 3D promptable segmentation. It is designed for seamless integration into Python-based workflows\u2014ideal for researchers, developers, and power users working directly with code.\n\n`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!\n\n\n<div align=\"center\">\n\n| **<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>** |\n|-------------------|----------------------|-------------------------|-------------------------|\n| [<img src=\"imgs/Logos/napari.jpg\" height=\"200\">](https://github.com/MIC-DKFZ/napari-nninteractive) | [<img src=\"imgs/Logos/mitk.jpg\" height=\"200\">](https://www.mitk.org/wiki/MITK-nnInteractive) | [<img src=\"imgs/Logos/3DSlicer.png\" height=\"200\">](https://github.com/coendevente/SlicerNNInteractive) | [<img src=\"imgs/Logos/snaplogo_sq.png\" height=\"200\">](https://itksnap-dls.readthedocs.io/en/latest/quick_start.html) |\n\n</div>\n\n\n## \ud83d\udcf0 News\n\n- **07/2025**: \ud83e\udde9 New ITK-SNAP extension released! Try nnInteractive directly in ITK-SNAP \ud83d\udc49 [Quick Start](https://itksnap-dls.readthedocs.io/en/latest/quick_start.html)\n- **06/2025**: \ud83c\udfc6 We\u2019re thrilled to announce that `nnInteractive` **won the 1st place** in the [CVPR 2025 Challenge on Interactive 3D Segmentation](https://www.codabench.org/competitions/5263/). Huge shoutout to the organizers and all contributors!\n- **05/2025**: `nnInteractive` presents an **official baseline** at **CVPR 2025** in the _Foundation Models for Interactive 3D Biomedical Image Segmentation Challenge_ ([Codabench link](https://www.codabench.org/competitions/5263/)) \u2192 see [`nnInteractive/inference/cvpr2025_challenge_baseline`](nnInteractive/inference/cvpr2025_challenge_baseline)\n- **04/2025**: \ud83c\udf89 The **community contributed a 3D Slicer integration** \u2013 thank you! \ud83d\udc49 [SlicerNNInteractive](https://github.com/coendevente/SlicerNNInteractive)\n- **03/2025**: \ud83d\ude80 `nnInteractive` **launched** with native support for **napari** and **MITK**\n\n---\n\n\n## What is nnInteractive?\n\n> Isensee, F.\\*, Rokuss, M.\\*, Kr\u00e4mer, 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 \\\n> *: equal contribution\n\nLink: [](https://arxiv.org/abs/2503.08373)\n\n\n##### Abstract:\n\nAccurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation \nmodels like SAM have revolutionized interactive segmentation, their 2D design and domain shift limitations make them \nill-suited for 3D medical images. Current adaptations address some of these challenges but remain limited, either \nlacking volumetric awareness, offering restricted interactivity, or supporting only a small set of structures and \nmodalities. Usability also remains a challenge, as current tools are rarely integrated into established imaging \nplatforms and often rely on cumbersome web-based interfaces with restricted functionality. We introduce nnInteractive, \nthe first comprehensive 3D interactive open-set segmentation method. It supports diverse prompts\u2014including points, \nscribbles, boxes, and a novel lasso prompt\u2014while leveraging intuitive 2D interactions to generate full 3D \nsegmentations. Trained on 120+ diverse volumetric 3D datasets (CT, MRI, PET, 3D Microscopy, etc.), nnInteractive \nsets a new state-of-the-art in accuracy, adaptability, and usability. Crucially, it is the first method integrated \ninto widely used image viewers (e.g., Napari, MITK), ensuring broad accessibility for real-world clinical and research \napplications. Extensive benchmarking demonstrates that nnInteractive far surpasses existing methods, setting a new \nstandard for AI-driven interactive 3D segmentation.\n\n<img src=\"imgs/figure1_method.png\" width=\"1200\">\n\n\n## Installation\n\n### Prerequisites\n\nYou need a Linux or Windows computer with a Nvidia GPU. 10GB of VRAM is recommended. Small objects should work with \\<6GB.\n\n##### 1. Create a virtual environment:\n\nnnInteractive supports Python 3.10+ and works with Conda, pip, or any other virtual environment. Here\u2019s an example using Conda:\n\n```\nconda create -n nnInteractive python=3.12\nconda activate nnInteractive\n```\n\n##### 2. Install the correct PyTorch for your system\n\nGo to the [PyTorch homepage](https://pytorch.org/get-started/locally/) and pick the right configuration.\nNote that since recently PyTorch needs to be installed via pip. This is fine to do within your conda environment.\n\nFor Ubuntu with a Nvidia GPU, pick 'stable', 'Linux', 'Pip', 'Python', 'CUDA12.6' (if all drivers are up to date, otherwise use and older version):\n\n```\npip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126\n```\n\n##### 3. Install this repository\nEither install via pip:\n`pip install nninteractive`\n\nOr clone and install this repository:\n```bash\ngit clone https://github.com/MIC-DKFZ/nnInteractive\ncd nnInteractive\npip install -e .\n```\n\n## Getting Started\nHere is a minimalistic script that covers the core functionality of nnInteractive:\n\n```python\nimport os\nimport torch\nimport SimpleITK as sitk\nfrom huggingface_hub import snapshot_download # Install huggingface_hub if not already installed\n\n# --- Download Trained Model Weights (~400MB) ---\nREPO_ID = \"nnInteractive/nnInteractive\"\nMODEL_NAME = \"nnInteractive_v1.0\" # Updated models may be available in the future\nDOWNLOAD_DIR = \"/home/isensee/temp\" # Specify the download directory\n\ndownload_path = snapshot_download(\n repo_id=REPO_ID,\n allow_patterns=[f\"{MODEL_NAME}/*\"],\n local_dir=DOWNLOAD_DIR\n)\n\n# The model is now stored in DOWNLOAD_DIR/MODEL_NAME.\n\n# --- Initialize Inference Session ---\nfrom nnInteractive.inference.inference_session import nnInteractiveInferenceSession\n\nsession = nnInteractiveInferenceSession(\n device=torch.device(\"cuda:0\"), # Set inference device\n use_torch_compile=False, # Experimental: Not tested yet\n verbose=False,\n torch_n_threads=os.cpu_count(), # Use available CPU cores\n do_autozoom=True, # Enables AutoZoom for better patching\n use_pinned_memory=True, # Optimizes GPU memory transfers\n)\n\n# Load the trained model\nmodel_path = os.path.join(DOWNLOAD_DIR, MODEL_NAME)\nsession.initialize_from_trained_model_folder(model_path)\n\n# --- Load Input Image (Example with SimpleITK) ---\ninput_image = sitk.ReadImage(\"FILENAME\")\nimg = sitk.GetArrayFromImage(input_image)[None] # Ensure shape (1, x, y, z)\n\n# Validate input dimensions\nif img.ndim != 4:\n raise ValueError(\"Input image must be 4D with shape (1, x, y, z)\")\n\nsession.set_image(img)\n\n# --- Define Output Buffer ---\ntarget_tensor = torch.zeros(img.shape[1:], dtype=torch.uint8) # Must be 3D (x, y, z)\nsession.set_target_buffer(target_tensor)\n\n# --- Interacting with the Model ---\n# Interactions can be freely chained and mixed in any order. Each interaction refines the segmentation.\n# The model updates the segmentation mask in the target buffer after every interaction.\n\n# Example: Add a **positive** point interaction\n# POINT_COORDINATES should be a tuple (x, y, z) specifying the point location.\nsession.add_point_interaction(POINT_COORDINATES, include_interaction=True)\n\n# Example: Add a **negative** point interaction\n# To make any interaction negative set include_interaction=False\nsession.add_point_interaction(POINT_COORDINATES, include_interaction=False)\n\n# Example: Add a bounding box interaction\n# BBOX_COORDINATES must be specified as [[x1, x2], [y1, y2], [z1, z2]] (half-open intervals).\n# Note: nnInteractive pre-trained models currently only support **2D bounding boxes**.\n# This means that **one dimension must be [d, d+1]** to indicate a single slice.\n\n# Example of a 2D bounding box in the axial plane (XY slice at depth Z)\n# BBOX_COORDINATES = [[30, 80], [40, 100], [10, 11]] # X: 30-80, Y: 40-100, Z: slice 10\n\nsession.add_bbox_interaction(BBOX_COORDINATES, include_interaction=True)\n\n# Example: Add a scribble interaction\n# - A 3D image of the same shape as img where one slice (any axis-aligned orientation) contains a hand-drawn scribble.\n# - Background must be 0, and scribble must be 1.\n# - Use session.preferred_scribble_thickness for optimal results.\nsession.add_scribble_interaction(SCRIBBLE_IMAGE, include_interaction=True)\n\n# Example: Add a lasso interaction\n# - Similarly to scribble a 3D image with a single slice containing a **closed contour** representing the selection.\nsession.add_lasso_interaction(LASSO_IMAGE, include_interaction=True)\n\n# You can combine any number of interactions as needed. \n# The model refines the segmentation result incrementally with each new interaction.\n\n# --- Retrieve Results ---\n# The target buffer holds the segmentation result.\nresults = session.target_buffer.clone()\n# OR (equivalent)\nresults = target_tensor.clone()\n\n# Cloning is required because the buffer will be **reused** for the next object.\n# Alternatively, set a new target buffer for each object:\nsession.set_target_buffer(torch.zeros(img.shape[1:], dtype=torch.uint8))\n\n# --- Start a New Object Segmentation ---\nsession.reset_interactions() # Clears the target buffer and resets interactions\n\n# Now you can start segmenting the next object in the image.\n\n# --- Set a New Image ---\n# Setting a new image also requires setting a new matching target buffer\nsession.set_image(NEW_IMAGE)\nsession.set_target_buffer(torch.zeros(NEW_IMAGE.shape[1:], dtype=torch.uint8))\n\n# Enjoy!\n```\n\n## nnInteractive SuperVoxels\n\nAs part of the `nnInteractive` framework, we provide a dedicated module for **supervoxel generation** based on [SAM](https://github.com/facebookresearch/segment-anything) and [SAM2](https://github.com/facebookresearch/sam2). This replaces traditional superpixel methods (e.g., SLIC) with **foundation model\u2013powered 3D pseudo-labels**.\n\n\ud83d\udd17 **Module:** [`nnInteractive/supervoxel/`](nnInteractive/supervoxel)\n\nThe SuperVoxel module allows you to:\n\n- Automatically generate high-quality 3D supervoxels via axial sampling + SAM segmentation and SAM2 mask propagation.\n- Use the generated supervoxels as **pseudo-ground-truth labels** to train promptable 3D segmentation models like `nnInteractive`.\n- Export `nnUNet`-compatible `.pkl` foreground prompts for downstream use.\n\nFor detailed installation, configuration, and usage instructions, check the [SuperVoxel README](nnInteractive/supervoxel/README.md).\n\n\n## Citation\nWhen using nnInteractive, please cite the following paper:\n\n> Isensee, F.\\*, Rokuss, M.\\*, Kr\u00e4mer, 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 \\\n> *: equal contribution\n\nLink: [](https://arxiv.org/abs/2503.08373)\n\n\n# License\nNote that while this repository is available under Apache-2.0 license (see [LICENSE](./LICENSE)), the [model checkpoint](https://huggingface.co/nnInteractive/nnInteractive) is `Creative Commons Attribution Non Commercial Share Alike 4.0`! \n\n# Changelog\n\n### 1.1.1 - 2025-08-01\n\n- We now detect whether linux kernel 6.11 is used and disable pin_memory in that case. See also [here](https://github.com/MIC-DKFZ/nnInteractive/issues/18)\n- \u2705 API compatible with 1.0.1 and 1.1.0\n\n### 1.1.0 - 2025-08-01\n\n- Reworked inference code. It's now well-structured and easier to follow.\n- Fixed bugs that \n - sometimes caused blocky predictions\n - may cause failure to update segmentation map if changes were minor and AutoZoom was triggered\n- \u2705 API compatible with 1.0.1\n\n## Acknowledgments\n\n<p align=\"left\">\n <img src=\"imgs/Logos/HI_Logo.png\" width=\"150\"> \n <img src=\"imgs/Logos/DKFZ_Logo.png\" width=\"500\">\n</p>\n\nThis repository is developed and maintained by the Applied Computer Vision Lab (ACVL)\nof [Helmholtz Imaging](https://www.helmholtz-imaging.de/) and the \n[Division of Medical Image Computing](https://www.dkfz.de/en/medical-image-computing) at DKFZ.\n",
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