Dental Image Segmentation Using Convolutional Neural Networks
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Solution Overview
Problem
Current dental imaging technologies, such as CBCT and intra-oral optical scanning, struggle to accurately and automatically segment individual anatomical features like teeth, roots, and jaw bones from 3D images, limiting their clinical utility for treatment planning and follow-up.
Innovation Solution
The implementation of machine learning techniques, specifically convolutional neural networks, for automated segmentation of 3D dental images, allowing for the creation of segmented data files that can be viewed and manipulated by dental practitioners, combining CBCT and intra-oral scan data to enhance image processing and segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image filtering and processing techniques are used, then the system can enhance image contrast and saturation, but it cannot accurately segment individual anatomical features like teeth, roots, and jaw bones
Solution Approach 1:
The patent replaces conventional mechanical image filtering and processing techniques with machine learning-based segmentation. Specifically, it uses trained machine learning models (such as convolutional neural networks) to automatically identify and segment anatomical features from 3D dental images, substituting the manual or rule-based filtering approach with an intelligent system that learns patterns from data to achieve accurate feature separation.
2Measurement precision
If manual segmentation by skilled operators is performed, then accurate segmentation can be achieved, but it requires significant time and operator expertise
Solution Approach 1:
The patent implements self-service segmentation by training machine learning models to perform anatomical feature segmentation autonomously without requiring skilled operators. The system uses trained models that automatically process 3D dental images and identify features such as teeth, roots, and jaw bones, enabling the system to serve itself in the segmentation task and eliminate dependence on manual expert intervention.
Solution Approach 2:
The patent applies preliminary action by pre-training machine learning models on large datasets of annotated dental images before deployment. This preliminary training phase allows the models to learn anatomical patterns and segmentation boundaries in advance, so that when actual segmentation is needed, the pre-trained models can quickly and accurately process new images without requiring real-time expert involvement.
3Loss of information
If CBCT imaging is used to generate 3D images, then comprehensive dental anatomy can be visualized, but the images cannot be accurately segmented into individual anatomical features
Solution Approach 1:
The patent applies segmentation by using machine learning models to divide the comprehensive 3D dental anatomy from CBCT images into distinct anatomical features. The trained models identify and separate individual teeth, roots, jaw bones, and other structures, transforming the unified 3D volume into segmented components that can be individually analyzed while preserving all original anatomical details.
Data Source
AI summary
A system and method are disclosed for representing and studying anatomy in the oral region such as parts of a subject's teeth and adjoining tissues. Types of inputs are used to form segmented outputs representing the teeth and can include segmented crown and root portions of the teeth. Machine learning methods are used for optimum and accurate results and to generate data objects corresponding to respective anatomical features of the subject.