3D Tooth Boundary Curve Segmentation for Partial Dental Scans

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Solution Overview

Problem

Conventional tooth segmentation methods in dental scans suffer from accuracy issues due to manual boundary designation, mask inaccuracies, and limitations in separating partial teeth, leading to increased time and effort, and excessive memory usage.

Innovation Solution

An automated method using artificial intelligence neural networks for tooth detection and boundary generation, combined with mesh parameterization, to accurately segment teeth by generating a tooth boundary curve.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual tooth boundary designation is used, then operator control is maintained, but segmentation accuracy decreases and time consumption increases

Engineering Contradiction:

Improvesegmentation accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automated tooth segmentation where the computer automatically identifies and segments teeth from scan data using deep learning algorithms, eliminating the need for manual operator intervention in boundary designation while maintaining high segmentation accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of operator-based boundary drawing with an automated deep learning system that processes scan data through neural networks to automatically generate tooth segmentations, significantly reducing time consumption while improving consistency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of manufacture

If conventional mask-based segmentation is used, then simple implementation is achieved, but segmentation accuracy deteriorates with boundary errors and invasions

Engineering Contradiction:

Improveimplementation simplicityVSAvoidboundary accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent replaces the conventional mask-based segmentation approach with a deep learning-based automated segmentation system that uses neural networks to identify tooth boundaries, eliminating manual mask drawing errors while maintaining implementation feasibility through automated processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces deep learning algorithms as an intermediary between the scan data and final segmentation, where the neural network acts as a mediator that accurately identifies tooth boundaries and generates precise segmentations without direct manual intervention

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If deep learning model for partial teeth is created, then segmentation capability improves, but memory usage and processing speed deteriorate

Engineering Contradiction:

Improvesegmentation capabilityVSAvoidmemory usage

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent develops a universal deep learning model that can handle both complete and partial tooth scans with the same architecture, eliminating the need for separate models for different tooth conditions and optimizing memory usage through model reusability

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12579653B2Automated method for tooth segmentation of three dimensional scan data using tooth boundary curve and computer readable medium having program for performing the method

Publication Date: 2026.03.17 IMAGOWORKS INC

AI summary

An automated method includes detecting a tooth of the scan data using a first artificial intelligence neural network, extracting a tooth scan data from the scan data based on a result of a tooth detection, generating a tooth mapped data corresponding to a predetermined space based on the tooth scan data, generating the tooth boundary curve by inputting the tooth mapped data to a second artificial intelligence neural network and mapping the tooth boundary curve to the scan data.