3D Jaw Model Segmentation Using Deep Neural Networks
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Solution Overview
Problem
Current methods for segmenting 3D digital models of jaws, such as those used in dental applications, face challenges with low accuracy for non-standard models, sensitivity to noise, and difficulty in obtaining smooth gingival lines, leading to high requirements for the quality of the digital model.
Innovation Solution
A method utilizing a trained deep artificial neural network, specifically convolutional neural networks, for segmenting 3D digital models of jaws, which includes simplification of the model through vertex contraction and optimization of boundaries using a fuzzy clustering algorithm, to improve segmentation accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation is used, then segmentation precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs automatic segmentation through neural network algorithms, enabling the computer to segment 3D digital jaw models autonomously without requiring manual intervention. This self-service approach resolves the contradiction by eliminating manual operations while maintaining high segmentation precision through learned patterns from training data.
Solution Approach 2:
The patent replaces manual mechanical segmentation operations with automated neural network-based computational methods. The deep learning model processes 3D digital models automatically, substituting human manual work with intelligent algorithms that achieve both high precision and efficiency simultaneously.
2Productivity
If curvature calculation or skeleton extraction technology is used, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces traditional mechanical segmentation methods (curvature calculation, skeleton extraction) with neural network-based intelligent algorithms. This substitution enables automatic processing with high productivity while achieving superior precision through learned features from training data, resolving the contradiction between efficiency and accuracy.
Solution Approach 2:
The neural network approach fundamentally changes the processing parameters from geometric calculations (curvature, skeleton) to learned feature representations. This parameter transformation enables the system to achieve both high speed processing and high precision segmentation by adapting to various jaw model characteristics through training.
3Productivity
If traditional segmentation methods are used, then productivity is improved, but reliability deteriorates
Solution Approach 1:
The neural network model is trained on diverse 3D digital jaw models including non-standard cases (missing teeth, extra teeth, irregular shapes), enabling it to handle multiple variations universally. This multi-functionality ensures reliable segmentation across different jaw model types while maintaining high productivity through automatic processing.
Solution Approach 2:
The system uses training feedback from labeled 3D digital jaw models to continuously improve segmentation accuracy. By learning from correct segmentations of various jaw types including non-standard cases, the neural network adapts and improves its reliability for different scenarios while maintaining efficient automatic operation.
Data Source
AI summary
A method for segmenting 3D digital model of jaw is provided. The method includes: obtaining a first 3D digital model of jaw; and segmenting the first 3D digital model of jaw using a trained deep artificial neural networks.