https://github.com/kitwaremedical/slicernnunet
3D Slicer nnUNet integration to streamline usage for nnUNet based AI extensions.
https://github.com/kitwaremedical/slicernnunet
3d-slicer-extension machine-learning medical-imaging nnunet
Last synced: about 1 year ago
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3D Slicer nnUNet integration to streamline usage for nnUNet based AI extensions.
- Host: GitHub
- URL: https://github.com/kitwaremedical/slicernnunet
- Owner: KitwareMedical
- License: bsd-3-clause
- Created: 2024-03-22T16:28:23.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-15T08:31:25.000Z (over 1 year ago)
- Last Synced: 2025-03-24T12:55:33.891Z (over 1 year ago)
- Topics: 3d-slicer-extension, machine-learning, medical-imaging, nnunet
- Language: Python
- Homepage:
- Size: 682 KB
- Stars: 33
- Watchers: 8
- Forks: 6
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Slicer nnUNet
## Table of contents
* [Introduction](#introduction)
* [Acknowledgments](#acknowledgments)
* [Using the extension](#using-the-extension)
* [Changelog](#changelog)
* [Expected Weight Folder Structure](#expected-weight-folder-structure)
* [Contributing](#contributing)
## Introduction
This module allows to install and run nnUNet trained models in 3D Slicer with only the training folder.
It streamlines development and integration of new nnUNet models into the 3D Slicer environment.
## Acknowledgments
This module was originally co-financed by the
Fédération Française d\'Orthodontie (FFO) as part of the
Dental Segmentator
developments and the Cure Overgrowth Syndromes
(COSY) RHU Project.
The installation steps are based on the work done in the
Slicer Total Segmentator extension.
This extension interfaces 3D Slicer with the
nnUNet library.
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring
method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.
## Using the extension
This extension can be installed directly using Slicer's extension manager.
Once installed, navigate to `Segmentation>nnUNet` in the modules drop down menu or search directly
for `nnUnet`.
Once in the widget, you can install nnUNet's dependencies by clicking on the "nnUNet Install" button.
This area will display the current version of nnUNet in 3D Slicer's environment.
If nnUNet is not yet installed, the current version will display None.
It can be installed by clicking on the install Button.
After the install is complete, the current version should display the latest nnUNet version or the version as set in
the "To install" field.
Note that the extension may require a 3D Slicer restart before running the inference.
Once the nnUNet is correctly installed, click on the 'nnUNet Run Settings' button to set the path to the Model to use.
This path will be saved for further usage after the first segmentation.
The provided model path should contain the nnUNet 'dataset.json' file.
> [!NOTE]
> To test this extension, you can download the
> Total Segmentator NNUNet weights
> and use the `CTChest` Sample.
> [!WARNING]
> The model weight folder structure should follow the nnUNet expected weight structure for this module to work
> (see the [expected weight folder structure](#expected-weight-folder-structure) section)
Select the volume on which to run the model using the volume input editor.
Then click on the `Apply` button.
The logs console will display all the information regarding running the input model.
Once the model has finished running, the segmentation will be loaded into 3D Slicer with its associated labels.
It can then be viewed and edited in the `Segment Editor` module.
## Expected weight folder structure
To properly run the nnUNet prediction, the nnUNet weight folder structure should be preserved.
nnUNet structure should look like the following :
* : The dataset id should be either a string beginning by Dataset or an integer
* ____ : default configuration folder name is `nnUNetTrainer__nnUNetPlans__3d_fullres`
* fold_<> : Fold folder from 0 to n containing the training weights
* : By default the weight name is `checkpoint_final.pth` it can also be modified using the `Parameter` class
* dataset.json : Configuration file containing dataset labels
If this structure is not preserved, the inference will raise an error.
The `Parameter` class provides an `isValid` method to check if the provided model structure is valid.
Please read [the official nnUNet documentation](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/how_to_use_nnunet.md#3d-u-net-cascade)
for more information.
## Contributing
This project welcomes contributions. It follows Slicer's rules for contributions.
If you want more information about how you can contribute, please refer to
the [CONTRIBUTING.md file](https://github.com/Slicer/Slicer/blob/main/CONTRIBUTING.md).