Dental Segmentation Accuracy via Iterative Boundary Loops
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Solution Overview
Problem
Existing digital models of a patient's dentition face challenges in accurately segmenting individual teeth and gingiva, leading to imprecise dental and orthodontic treatment planning.
Innovation Solution
A computer-implemented method for improving segmentation accuracy by determining a first restriction boundary for a tooth, generating a loop path around the tooth's surface, estimating a loop path metric, and iteratively adjusting the boundary until a final adjusted boundary is achieved for precise segmentation between teeth and gingiva.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation methods are used to divide digital models into individual teeth, then the process is simple and quick, but the segmentation accuracy is poor and boundaries are imprecise
Solution Approach 1:
The patent applies segmentation by dividing the digital dental model into multiple discrete tooth objects using identified boundary loops. The method segments teeth by detecting interproximal surfaces and creating separation loops at the gingival margin, transforming a continuous surface model into discrete individual tooth segments for precise treatment planning
Solution Approach 2:
The patent performs preliminary actions by first identifying restriction boundaries and candidate boundary loops before final segmentation. The method pre-processes the digital model to locate interproximal surfaces and establish initial boundary candidates, then refines these boundaries through metric evaluation and optimization before completing the final tooth segmentation
2Manufacturing precision
If the segmentation boundary is adjusted iteratively to improve accuracy, then the segmentation precision improves, but the processing time increases
Solution Approach 1:
The patent implements feedback by calculating loop path metrics (such as curvature, area, and perimeter) for candidate segmentation boundaries and using these metrics to evaluate and adjust boundary accuracy. The system iteratively refines boundary loops based on metric feedback, comparing calculated metrics against expected ranges to optimize segmentation precision while controlling processing time through efficient metric calculations
Data Source
AI summary
Methods and systems for improving segmentation of a digital model of a patient's dentition into component teeth.