Graph-Based 3D Tooth Segmentation for Low-Input Orthodontic Planning
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Solution Overview
Problem
Existing automated tooth segmentation systems struggle with accurately segmenting individual teeth and dental features from 3D meshes due to high memory requirements and the need for significant technician input, leading to misidentification or omission of critical dental components.
Innovation Solution
Implementing a machine learning neural network using graph-based representations of 3D meshes, combined with convolutional neural networks like U-Net and graph convolution techniques, to automatically and accurately segment teeth and dental features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional automated tooth segmentation systems are used, then processing can be performed with standard computational resources, but segmentation accuracy deteriorates and significant technician input is required
Solution Approach 1:
The system enables automated segmentation that performs self-service by automatically identifying and segmenting teeth and dental features without requiring technician intervention. The neural network processes 3D mesh data independently, generating accurate segmentations autonomously.
Solution Approach 2:
The patent replaces manual technician operations with an automated neural network system. The mechanical process of manual segmentation is substituted by a computational system using 3D convolutional neural networks and graph-based approaches to perform segmentation automatically.
2Measurement precision
If high-resolution 3D mesh segmentation is performed using traditional methods, then detailed tooth identification is achieved, but memory requirements increase enormously
Solution Approach 1:
The patent applies segmentation by dividing the 3D mesh processing into distinct computational stages: graph-based representation creation, feature extraction, and neural network processing. This segmented approach allows detailed tooth identification while managing memory requirements through systematic data processing.
Solution Approach 2:
The patent transforms the 3D mesh data into a graph-based representation, changing the dimensional structure from continuous 3D space to a discrete graph structure with nodes and edges. This dimensional transformation enables efficient processing of high-resolution dental models with reduced memory requirements.
3Measurement precision
If automated segmentation systems are implemented without graph-based approaches, then system complexity is reduced, but segmentation accuracy and ability to capture dental features deteriorates
Solution Approach 1:
The patent introduces a graph-based representation as an intermediary between the raw 3D mesh data and the neural network processing. This intermediate graph structure captures topological relationships and geometric features, enabling accurate dental feature segmentation while organizing system complexity in a manageable intermediate form.
Data Source
AI summary
Provided herein are systems and methods for automatically segmenting a 3D model of a patient's teeth. A patient's dentition may be scanned. The scan data may be converted into a 3D model, including a graph-based representation of the 3D model. The graph-based representation can be input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components. Trained machine learning models can also be used to segment graph-based representations of a 3D model of a patient's teeth.