Slicer Automated Dental Tools

The Slicer Automated Dental Tools extension provides automatic dental and craniofacial analysis capabilities. It features a user-friendly graphical interface on 3D Slicer, enabling users to perform complex tasks without any coding expertise.

Compatible with both stable and nightly versions of 3D Slicer. Latest versions supported: 5.7.0 (nightly) and 5.6.1 (stable)

Overview

Slicer automated dental tools is an extension that allows users to perform automatic segmentation, landmark identification and Automatic Orientation on CBCT scans and Intra Oral Scan (IOS) using machine learning tools where the learning mdoels are continously updated.

Features

• Simple: Perform time consuming task with a few clicks.
• Automatic: Automate important Dental and Cranio Facial analysis tasks.
• Flexible: The user can choose which models to use to perform the automated task. New models can be added easily.

Modules

Name	Description
AMASSS	Perform automatic segmentation of CBCT scan. AMASSS is an acronym for Automatic Multi-Anatomical Skull Structure Segmentation.
ALI	Perform automatic landmark identification on either CBCT or IOS scans. ALI is an acronym for Automatic Landmark Identification.
ASO	Perform automatic orientation either on IOS or CBCT files.
AReg	Perform automatic registration on IOS or CBCT files.
AutoCrop3D	Automatically crop a folder of CBCT scans with the same region of interest.
AutoMatrix	Automatically apply one or different matrix to a folder of IOS or CBCT scans.
MRI2CBCT	Contains the steps to perform the registration of MRI and CBCT scans.
FlexReg	Registration of IOS patient per patient with customizable patch creation.
DOCShapeAXI	Automatic classification of 3D Shape. DOC-ShapeAXI is an acronym for Dental Oral and Craniofacial Shape Analysis eXplainability and Interpretability.
BatchDentalSegmentator	DentalSegmentator in batch for mixed or permanent dentition

These modules provide a convenient user interface, are available through the Automated Dental Tools module category, and share common features :

Input

• All modules can work with one file or a whole sample (folder) as input.
• If the input is a single file already loaded, the result of the predicton will directly show up on the slice views.

Output

• By selecting the "Group output in a folder" checkbox, all the ouput files will be grouped in a single folder for each patient.
• All modules allows the user to save the output in the input folder, or by unchecking the "Save prediction in scan folder" the user can choose a custom output folder.
• The "Prediction ID" field is for the user to choose what will appear on the output file name. ("Pred" by default)

Additionally, the following modules are implemented as python scripted command-line (CLI) modules available in the Automated Dental Tools.Advanced module category and are used internally by the modules described above.

Name	Description
AMASSS_CLI	Perform automatic segmentation of CBCT scans
ALI-CBCT	Perform automatic landmark identification of CBCT scans
ALI-IOS	Perform automatic landmark identification of IOS scans
ASO-CBCT	Perform automatic orientation of CBCT scans
ASO-IOS	Perform automatic orientation of IOS scans
AReg-CBCT	Perform automatic registration of CBCT scans
AReg-IOS	Perform automatic registration of IOS scans
MRI2CBCT_ORIENT_CENTER_MRI	Perform orientation and centering of MRI scans
MRI2CBCT_RESAMPLE_CBCT_MRI	Perform resample of MRI and CBCT scans
MRI2CBCT_REG	Perform registration of MRI-CBCT scans
FlexReg_CLI	Perform creation of patch and registration on IOS scans.
DOCShapeAXI	Perform automatic classification of 3D Shape.
BatchDentalSegmentator	DentalSegmentator in batch for mixed or permanent dentition

Requirements

• In addition of the Slicer System requirements, for best performance, 12GB of memory is recommended.
• ⚠️ Trained networks are required to be manually downloaded. See requirements section specific to each module.

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AMASSS Module

AMASSS module will allow you to segment CBCT scan using AMASSS algortihm.

Prerequisites

• Download the trained models for AMASSS using the Download latest models button in the module Input section.

Module structure

Input file: The input has to be an oriented CBCT. It can be a single CBCT scan loaded on slicer or a folder containg CBCTs with the following extention:

.nrrd / .nrrd.gz
.nii  / .nii.gz
.gipl / .gipl.gz

Available sample data for testing: MG_test_scan.nii.gz

Load models: The user has to indicate the path of the folder containing the trained models for AMASSS.

Segmentation selection: The user can choose the structure to segment using the selection table. Depending on the type of CBCT to segment, the user can select the "Use small FOV models" checkbox to use on higher definition scans.

Output option: By selecting the "Generate surface files" checkbox. The user will also get a surface model of the segmentation that will be saved in a "VTK files" folder and will be automatically loaded in slicer at the end of the prediction if working on a single file.

Advanced option:

• You can increase/decrease the precision of the segmentation (going above 50 will drastically increase the prediction time and is not necesary worth it, going under 50 will make the prediction much faster but less accurate)
• If the user whant to generate surface files, he can choose the smothness applied on the model.
• Depending on your computer power, you can increase the CPU and GPU usage to increase the predictio speed.

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ALI Module

ALI module provide a convenient user interface allowing to identify landmarks on different type of scans:

• CBCT scan
• IOS scan

ALI-CBCT

The implementation is based on the ALI-CBCT algortihm originally developed by Maxime Gillot at https://github.com/Maxlo24/ALI_CBCT.

Prerequisites

• Download the trained models for ALI-CBCT using the Download latest models button in the module Input section.

Module structure

Input file: The input has to be an oriented CBCT. It can be a single CBCT scan loaded on slicer or a folder containg CBCTs with the following extention:

.nrrd / .nrrd.gz
.nii  / .nii.gz
.gipl / .gipl.gz

Available sample data for testing: MG_test_scan.nii.gz

Load models: The user has to indicate the path of the folder containing the trained models for ALI-CBCT.

Landmark selection: Once the folder containing the trained models is loaded. The user can choose the landmark he want to identify with the table showing the available landmarks:

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ALI-IOS

The implementation is based on the ALI-IOS algortihm originally developed by Baptiste Baquero at https://github.com/baptistebaquero/ALIDDM.

Prerequisites

• Download the trained models for ALI-IOS using the Download latest models button in the module Input section.

Module structure

Input file: The input has to be an oriented IOS segmented with the Universal Numbering System. This segmentation can be automatically done using the SlicerJawSegmentation extention. The input can be a single IOS loaded on slicer or a folder containg IOS with the following extention: The array name of labels in the vtk surface is: "Universal_ID" or "predictionid" or "PredictionID"

.vtk

Available sample data for testing: T1_01_L_segmented.vtk and T1_01_U_segmented.vtk

Load models: The user has to indicate the path of the folder containing the trained models for ALI-IOS.

Landmark selection: For the IOS landmarks, the user has to choose which tooth he need the landmakrs on by checking the label of the tooth on the left table. Once the folder containing the trained models is loaded. The user can choose the landmark he want to identify with the table on the right showing the available landmarks:

ASO Module

If you want more information and a descriptive tutorial of this tool, take a look at this github page: Automated Standardized Orientation

ASO module provide a convenient user interface allowing to orient different type of scans:

• CBCT scan
• IOS scan

How the module works?

2 Modes Available (Semi or Fully Automated)

• Semi-Automated (to only run the landmark-based registration with landmark and scans as input)
• Fully-Automated (to perform Pre Orientation steps, landmark Identification and ASO with only scans as input)

Mode	Input
Semi-Automated	Scans, Landmark files
Fully-Automated	Scans, ALI Models, Pre ASO Models (for CBCT files), Segmentation Models (for IOS files)

Input file:

Input Type	Input Extension Type
CBCT	.nii, .nii.gz, .gipl.gz, .nrrd, .nrrd.gz
IOS	.vtk .stl .vtp .off .obj

Test Files Available: You can either download them using the link or by using the Test Files button.

Module Selected	Download Link to Test Files	Information
Semi-CBCT	Test Files	Scan and Fiducial List for this Reference
Fully-CBCT	Test File	Only Scan
Semi-IOS	Test Files	Mesh and Fiducial List Reference
Fully-IOS	Test Files	Only Mesh Reference

Reference:

The user has to choose a folder containing a Reference Gold File with an oriented scan with landmarks. You can either use your own files or download ours using the Download Reference button in the module Input section.

Input Type	Reference Gold Files
CBCT	CBCT Reference Files
IOS	IOS Reference Files

Landmark selection

The user has to decide which landmarks he will use to run ASO.

Input Type	Landmarks Available
CBCT	Cranial Base, Lower Bones, Upper Bones, Lower and Upper Teeth
IOS	Upper and Lower Jaw

The landmark selection is handled in the Landmark Reference Section:

Models Selection

For the Fully-Automated Mode, models are required as input, use the Select Button to automatically download, extract and select the selected models.

AReg Module

AReg module provide a convenient user interface allowing to orient different type of scans:

• CBCT scan
• IOS scan

How the module works?

3 Modes Available

• Semi-Automated (to only run the automatic registration with masks segmentation and scans as input - only for CBCT)
• Fully-Automated (to perform the automatic registration with only scans as input)
• Orientation and Registration (to perform the automatic orientation using ASO process and the automatic registration with only scans as input)

Mode	Input
Semi-Automated (only for CBCT)	Scans, Masks segmentation
Fully-Automated	Scans, Segmentation Models
Orientation and Registration	Scans, ALI Models (for CBCT files), Segmentation Models

For CBCT, a final step is added and consist of segmenting different skeletal structures using AMASSS process within each mode.

Input file:

Input Type	Input Extension Type
CBCT	.nii, .nii.gz, .gipl.gz, .nrrd, .nrrd.gz
IOS	.vtk .stl .vtp .off .obj

Test Files Available: You can either download them using the link or by using the Test Files button.

Module Selected	Download Link to Test Files	Information
Semi-CBCT	Test Files	Scan and Masks segmentation
Fully-CBCT	Test File	Only Oriented Scan
Orientation and Registration for CBCT	Test File	Only Scan
Fully-IOS	Test Files	Only Mesh )
Orientation and Registration for IOS	Test Files	Only Mesh

Pipelines

CBCT - AReg Pipeline

IOS - Patch prediction

Some Results

Models Selection

For the Fully-Automated Mode, models are required as input, use the Select Button to automatically download, extract and select the selected models.

AutoCrop3D Module

AutoCrop3D stands for "Scans automatically cropped" according to a Region Of Interest (ROI).

Useful Information

This module has the same utility as "Crop Volume", which offers more interactive feedbacks to the user, but "AutoCrop3D" allows you to easily trim the ROI of all the cases in your study. Then you can load your files into Slicer to view them. More Information about Crop Volume

It solves the issue of files that may be too heavy for "Crop Volume Sequence" More Information about Crop volume Sequence.

How does the module work?

Input:

Input	Input Type	Input Extension Type	Files to Test
File or Folder	CBCT	.nii, .nii.gz, .gipl.gz, .nrrd, .nrrd.gz	Segmentation.zip
Region of Interest*	Volume	.json	ROI.mrk.zip

You can choose a suffix which will be added to the existing name of your file(s).

If you want to generate VTK files from your Segmentations, the Segmentation files must have "Seg" somewhere in the name. For example: patientName_Seg.nii.gz

How to create a Region Of Interest?

Use the module "Volume Rendering" to create your ROI and then save it as a .json file.

1. Upload a scan file
2. Choose the option Display ROI
3. Change the ROI as you like
4. Save it

AutoMatrix Module

The AutoMatrix module provides a user interface to apply a matrix to a folder for different types of scans or segmentations:

• CBCT
• IOS

How the module works?

How to name the new files and where they are stored

The module will replicate the folder structure from your input folder inside the output folder. Each new file will keep the original filename, followed by the suffix you specify. If the "Add matrix name to filename" option is enabled, the matrix filename will also be appended.

Example:

With matrix name	Without matrix name
Input file: patient1_T1_MA.nii.gz	Input file: patient1_T1_MA.nii.gz
Input matrix: patient1_matrix1.tfm	Input matrix: Matrix_mirror.tfm
Suffix: _apply	Suffix: _mir
Output file: patient1_T1_MA_apply_matrix1.nii.gz	Output file: patient1_T1_MA_mir.nii.gz

4 Modes available

Sometimes a LinkName is required. See the information below.

Mode	Download Link to Test Files	Information
File patient and file matrix	Test Files	LinkName is not required. The matrix is applied to the input file. This new file is save in the output folder.
File patient and folder matrix	Test Files	LinkName required. For each matrix applied, a new file is created.
Folder patient and file matrix	Test Files	LinkName is not required. The matrix will be applied to all files in the input folder.
Folder patient and folder matrix	Test Files	LinkName required. For each matrix applied, a new file is created. You can have more than one file with the same patient name.

When a LinkName is required :

The name of the matrix files and the name of the seg/scan files must begin with the same patient name. After the patient name, you must put and underscore before any other information. Example : Name file patient : patient1_T1_MA.nii.gz Name matrix files : patient1_left_MA.tfm

Mirror :

There is button "Mirror" that will automatically download the matrix mirror and put in input.

Input file:

Input Type	Input Extension Type	Input Matrix Extension
CBCT	.nii.gz	.tfm .npy .h5 .mat .txt
IOS	.vtk .stl .vtp .off .obj	.tfm .npy .h5 .mat .txt
Landmark	.mrk.json	.tfm .npy .h5 .mat .txt

MRI2CBCT Module

The MRI2CBCT module provides a user interface to perform the registration between MRI and CBCT scans.

How does the module work?

Preprocessing Step:

1. Orient and Center CBCT:

   • Input: CBCT folder path
   • Download Models: You can download the models required for segmentation and orientation by clicking on "Download."

2. Orient and Center MRI:

   • Bilateral mode: When enabled, additional options become available:
     This option is intended for bilateral TMJ scans, which contains two slices between left and right separated by a large distance.When such DICOM volumes are saved as NIfTI, the original slice spacing is lost or misrepresented, which distorts the image.
   
   Enabling bilateral mode lets you select a loaded DICOM node to automatically retrieve the true acquisition spacing (from DICOM metadata) and apply it before orientation. This ensures that the image is correctly resampled and anatomically accurate. > If **Bilateral** is disabled, the spacing from the input scan is used as-is. - **Input:** MRI folder path - **Axis Direction:** You need to choose the new direction for each axis.

3. Left and Right Cropping:

   • Inputs: Folder paths for CBCT and/or MRI.

Resampling Step:

• Options: You can resample T1 and/or T2 scans for MRI, CBCT, and Segmentation independently.
• Slice/Number of Slices: Decide the new slices/number of slices or retain the same size as before running it.
• Spacing: Choose the new spacing or keep the same spacing as in the input.
• Center Image: When enabled, padding is added equally in all directions to center the content.

Approximation:

This step aligns the MRI and CBCT volumes to prepare for precise registration.

• Inputs: Resampled MRI and CBCT volumes.

Cropping (TMJ Region):

Currently, cropping is done using the AutoCrop method. In the future, this step will be replaced by a trained model that automatically identifies and extracts the TMJ region of interest.

Registration:

• Inputs: CBCT, CBCT segmentation, and MRI scans after completing the preprocessing steps, including manual approximation and cropping.
• Normalization Options: Select the normalization method and specify the percentile to apply to both MRI and CBCT scans. Default values are provided for convenience.

FlexReg Module

FlexReg is a module that allows you to register patient-specific Intra Oral Scans. It lets you create custom patches for registration.

How does the module work?

1. Load your files:

   • Choose your path by clicking on "Select" and load it by clicking on "View".

2. Draw your own patch:

   • Butterfly Patch:
     • You can draw a butterfly patch using the parameters and select your own teeth. Note that this patch, with the default values, is called “ButterflyPatch” and is created when you run AREG_IOS:

       

   • Custom Patch:
     • You can draw the patch using landmarks and customize it:

     • 

   • You can combine multiple patches by creating them one by one. All your patches will be used as a single patch for the registration.

   • 

3. Registration:

   • After creating your patch for both the fixed and moving scans, choose the output folder and the suffix you would like. If you have a lower arch to move, you can select it.
   • Register them by clicking on "Registration".

DOCShapeAXI Module

This integrates advanced deep learning algorithms enabling the classification of nasopharynx airways obstruction, mandibular condyles, and alveolar bone defect in cleft. The tool facilitates the automated classification of medical shapes, assisting in clinical diagnosis and decision-making. The extension was originally developped https://github.com/lucieDLE/DOC-ShapeAXI/

Prerequisites

• This extension works with Linux and Windows.
• This extension requires the installation of SlicerConda. Follows the instructions for more information.

Supported Dataset

Currently, the extension supports only three types of datasets:

• Nasopharynx Airway Obstruction: Three models are available for this dataset.
  • Severity model: classifies 4 levels of airway obstruction ranging from 0 to 3, where 0 is the lowest level and 3 is the greatest level.
  • Binary model: classifies between 0 and 1, where 0 is the absence and 1 is the presence of adenoid hypertrophy-related obstruction.
  • Regression model: directly predicts the ratio of obstruction ranging between 0 and 100%.
• Mandibular Condyle:
  • Severity model: classifies the condylar changes into 4 levels of severity ranging from 0 to 3.
• Alveolar Bone Defect in Cleft:
  • Severity model: classifies the defect into 4 levels of severity ranging from 0 to 3.

How does the module work?

1. Select a Data Type: nasopharynx airways obstruction, mandibular condyles, and alveolar bone defect in cleft.

Note

This step allow to select automatically the appropriate classification model.

2. Select an Input folder: a folder that contains 3D models stored as VTK files.
3. Select an Output directory: a folder that will contain all the outputs (model, predictions results stored in a CSV file, and explainability visualizations).
4. Run the module: Click on the Run Prediction button.

Note

The progress will be display in the UI with a progress bar.

Viewing the Results

All the results are saved in the Output directory specified by the user. you can find:

A. the csv file containing the prediction made by the network.

B. a folder named Explainability containing the visualization results:

1. Open the folder and drag and drop a shape in Slicer
2. Go to the module Models
3. In the Display drop-down menu, click on the Scalar section
4. check the visibility box and change the color table to ColdToHotRainbow
5. Set the Data Scalar Range to Auto if not set
6. Change the Active Scalar to the class you want to visualize

BatchDentalSeg

3D Slicer extension for fully-automatic segmentation of CT and CBCT dental volumes in batch using Dentalsegmentator model (for adults scans) or PediatricDentalsegmentator model (for children scans).

After selecting the folder with the volumes to process and the model that fit better your scans, this module generates the following segmentations for each volumes:

• 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 from 7 institutions by Dot G, et al, here is their paper and nnUnet paper:

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

PediatricDentalSegmentator model

As DentalSegmentator, PediatricDentalSegmentator is also based on nnUnet. It has been trained on 513 dento-maxillo-facial CBCT scans including scans withs primary teeth.

Using the extension

This extension is compatible with 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's a module in the Automated Dental Tools extension so it can be can be found using the search bar by typing "Automated Dental Tools".

After the installation process and restart of Slicer, the extension can be found in the module file explorer under Automated Dental Tools>BatchDentalSegmentator. It can also be found by using the find module button and searching for the keyword BatchDentalSegmentator.

To use the extension, click on Select Folder to select the folder where your scans are.

Then choose the model that you want to use (DentalSegmentator for scans of subjects with permanent dentition) or PediatricDentalSegmentator (for scans of subjects with mixed dentition)

Finally click on the Apply button to start the segmentations.

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.

Exemple of using

Acknowledgements

Authors: Maxime Gillot (University of Michigan), Baptiste Baquero (UoM), Luc Anchling (UoM), Nathan Hutin (UoM),Jeanne Claret (UoM),Gaelle Leroux (UoM), Enzo Tulissi (UoM), Alban Gaydamour (UoM),Gauthier Dot(AP-HP),Laurent GAJNY (ENSAM), Roman FENIOUX (KITWARE SAS), Thibault PELLETIER (KITWARE SAS), Lucia Cevidanes (UoM), Juan Carlos Prieto (UNC), David Allemang (Kitware), Jean-Christophe Fillion-Robin (Kitware), Connor Bowley (Kitware), James Butler (Kitware).

Supported by NIDCR R01DE 024450, AA0F Grabber Family Teaching and Research Award and by Research Enhancement Award Activity 141 from the University of the Pacific, Arthur A. Dugoni School of Dentistry.

License

This software is licensed under the terms of the Apache Licence Version 2.0.