Dual-Model Teeth Detection for Orthodontic 3D Modeling
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Solution Overview
Problem
Current dental image processing methods for orthodontic treatments face challenges in accurately detecting and locating teeth, leading to potential misalignment and ineffective treatment plans due to inaccurate 3D models, which can result in discomfort, longer treatment times, and the need for repeat imaging and re-treatment.
Innovation Solution
The method involves applying a first model to determine preliminary locations of teeth and a second model to identify adhesion locations between them, followed by a refinement process using a sliding window inference and neural network-based segmentation models like SegResNet to generate precise 3D models of the teeth and jaw, reducing computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-model segmentation is used for teeth detection, then the process is simple, but the detection accuracy and boundary precision are insufficient
Solution Approach 1:
The patent divides the segmentation task into two distinct models: a first model for detecting tooth locations and a second model for identifying adhesion locations between teeth. This segmentation of functionality allows each model to specialize in specific aspects of the detection task, improving overall accuracy while managing complexity through modular design
Solution Approach 2:
The patent introduces adhesion location detection as an intermediary step between initial tooth detection and final refined location determination. The second model identifies adhesion locations between teeth, which then serve as input for refining the preliminary locations, enabling more precise boundary detection through a multi-stage process
2Manufacturing precision
If high-accuracy 3D models are generated through detailed imaging, then treatment precision improves, but computational resources and time increase
Solution Approach 1:
The patent performs preliminary detection of tooth locations using the first model before conducting the more computationally intensive adhesion location analysis with the second model. This preliminary action allows the system to focus computational resources only on regions where teeth are detected, reducing overall processing time while maintaining high accuracy in the final 3D models
Solution Approach 2:
By segmenting the 3D modeling process into two distinct modeling stages (first model for locations, second model for adhesion), the patent enables parallel processing and optimization of each stage independently, reducing total computational time while achieving high manufacturing precision in the final orthodontic appliances
3Reliability
If comprehensive tooth detection is performed, then treatment planning accuracy improves, but the risk of complications from errors increases
Solution Approach 1:
The patent introduces adhesion location detection as an intermediary verification step that identifies potential errors in preliminary tooth location detection. By detecting where teeth appear to be adhered together in the image, the system can refine boundary measurements and reduce detection errors, thereby improving treatment plan reliability and reducing the risk of complications from inaccurate treatment planning
Solution Approach 2:
The two-model system implements feedback where the second model's adhesion location detection results are used to refine the preliminary locations from the first model. This feedback loop allows the system to correct detection errors and improve the overall reliability of tooth location determination, reducing harmful factors in treatment planning
Data Source
AI summary
The present disclosure relates to an apparatus, comprising: processing circuitry configured to obtain an image, apply a first model and a second model to the obtained image, determine, based on the first model, preliminary locations of objects in the obtained image, determine, based on the second model, adhesion locations disposed between the objects in the obtained image, and determine, based on the preliminary locations and the adhesion locations, refined locations of the objects in the obtained image.