Slicer DentalSegmentator
3D Slicer extension for fully-automatic segmentation of CT and CBCT dental volumes.
After loading and selecting the volume to process, this module generates the following segmentations :
- Maxilla & Upper Skull
- Mandible
- Upper Teeth
- Lower Teeth
- Mandibular canal
DentalSegmentator model
DentalSegmentator is based on nnU-Net framework. It has been trained on 470 dento-maxillo-facial CT and CBCT scans, and evaluated on a hold-out test dataset of 256 CT and CBCT scans from 7 institutions.
The results obtained on our highly diversified dataset demonstrate that our tool can provide fully automatic and robust multiclass segmentation for dento-maxillo-facial (CB)CT scans, independantly of the field of view of the scan.
If you use DentalSegmentator for your work, please cite our paper and nnU-Net:
Dot G, et al. DentalSegmentator: robust open source deep learning-based CT and CBCT image segmentation. Journal of Dentistry (2024) doi:10.1016/j.jdent.2024.105130
Isensee F, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211. doi:10.1038/s41592-020-01008-z
We also deeply encourage you to use this badge when displaying models created with this extension:
Using the extension
Here is a video tutorial showing the installation process and demonstrating the main capabilities of the extension.
This extension is compatible with the latest 3D Slicer Stable Release (version 5.8.0, or later), downloadable from the official website.
The plugin can be installed in Slicer using
the extension manager.
It can be found using the search bar by typing “DentalSegmentator”.
After the install process and restart of Slicer, the extension can be found in the module file explorer under Segmentation>DentalSegmentator.
It can also be found by using the find module button and searching for the keyword DentalSegmentator.
To use the extension, load a dental CT or CBCT by either drag and dropping the data in 3D Slicer or by using the
DATA or DCM load buttons.
To test the extension, you can use 3D Slicer’s CBCT Dental Surgery volumes. These volumes can be found in the
Sample Data module.
After loading the data, the data will be displayed in the 2D views.
Switch module to the DentalSegmentator module and select the volume in the first drop down menu.
Click on the Apply button to start the segmentation.
During the first launch, the module’s dependencies will be installed. These dependencies include :
- The AI model weights
- Light the torch
- PyTorch
- nnUNet V2
After the install, the volume will be transferred and sent to the nnUNet V2 library for processing.
If your device doesn’t include CUDA, the processing may be very long and a dialog will ask for confirmation before
starting the segmentation process.
During execution, the processing can be canceled using the Stop button.
The progress will be reported in the console logs.
After the segmentation process has run, the segmentation will be loaded into the application.
The segmentation results can be modified using the Segment Editor tools.
The segmentation can be exported using the Export segmentation menu and selecting the export format to use.
The Surface smoothing slider allows to change the 3D view surface smoothing algorithm.
Troubleshooting
General troubleshooting steps
- Verify that the PyTorch extension is installed and functional
- Confirm that the model files are present in the expected location
- Restart Slicer and reload the module after install
- Test with the sample data provided in the repository
- Review the logs for Python errors or missing dependencies
MacOS GPU acceleration
Due to an ongoing issue with Mac devices and Pytorch, GPU acceleration is not available for now on those devices.
Linux Processing hang
On Linux or WSL system, the inference can get stuck at the stage : “done with volume”.
This indicates that the process has run out of memory. 32 GB of RAM is recommended to run this extension. If you run
into this issue, you can create a SWAP file on SSD with 16 GB which should solve the problem.
Torch CUDA not properly installed
On Windows, the torch CUDA dependency may not properly be detected and installed.
To solve this problem, you can use the PyTorch Utils extension, uninstall the version of PyTorch and install
a new version of PyTorch by setting the Computation backend compatible with your hardware.
The PyTorch version should be greater than 2.0.0 for nnUNet compatibility.
Failed to download / find weights
If the weights are not correctly installed, you can install them manually.
To do so, go to https://github.com/gaudot/SlicerDentalSegmentator and select the latest release.
Download the latest .zip file from the release.
Navigate to your DentalSegmentator folder (this folder can be also found in the module finder window).
Unzip the weight file in the DentalSegmentator\Resources\ML folder.
Create a download_info.json file containing the path to the downloaded zip file for future reference :
{
“download_url”: “https://github.com/gaudot/SlicerDentalSegmentator/releases/download/v1.0.0-alpha/Dataset111_453CT_v100.zip”
}
Command-line interface
If you want tu use DentalSegmentator via nnU-Net command-line interface use, the pretrained model is available on Zenodo platform.
Contributing
This project welcomes contributions. If you want more information about how you can contribute, please refer to
the CONTRIBUTING.md file.
Acknowledgments
Authors: G. Dot (Université Paris Cité, AP-HP, Arts-et-Métiers), L. Gajny (Arts-et-Métiers), R. Fenioux (Kitware SAS), T. Pelletier (Kitware SAS)
Supported by the FFO (Fédération Française d’Orthodontie) and the Fondation des Gueules Cassées.
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