Deep Learning Tooth Segmentation with U-Shape Normalization
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Solution Overview
Problem
Conventional tooth segmentation methods in dental scans require manual operator input, leading to decreased accuracy and significant time consumption due to reliance on manual boundary designation and axis alignment.
Innovation Solution
An automated tooth segmentation method using deep learning, involving U-shape normalization and teeth/gum normalization operations, to preprocess dental scan data before inputting it into a convolutional neural network for accurate labeling and boundary extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operator designation of tooth boundary and axis alignment is used, then flexibility and adaptability are maintained, but accuracy decreases and time consumption increases
Solution Approach 1:
The system performs self-service by automatically designating tooth boundaries and performing axis alignment without operator intervention. The deep learning model autonomously completes the segmentation task, eliminating the need for manual boundary designation and axis alignment operations, thereby resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual operation system with an automated deep learning-based computer vision system. The manual designation of tooth boundaries and axis alignment performed by operators is substituted by an automated neural network model that processes scan data and generates segmentation results automatically, achieving both high accuracy and efficiency.
2Measurement precision
If manual operator designation of tooth boundary is used, then adaptability to different cases is maintained, but skill requirement increases and accuracy decreases
Solution Approach 1:
The system performs self-service by automatically designating tooth boundaries without operator intervention. The deep learning model autonomously completes the segmentation task, eliminating the need for manual boundary designation and axis alignment operations, thereby resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual operation system with an automated deep learning-based computer vision system. The manual designation of tooth boundaries and axis alignment performed by operators is substituted by an automated neural network model that processes scan data and generates segmentation results automatically, achieving both high accuracy and ease of operation.
Data Source
AI summary
A method of automated tooth segmentation of a three dimensional scan data using a deep learning, includes determining a U-shape of teeth in input scan data and operating a U-shape normalization operation to the input scan data to generate first scan data, operating a teeth and gum normalization operation, in which the first scan data are received and a region of interest (ROI) of the teeth and gum is set based on a landmark formed on the tooth, to generate second scan data, inputting the second scan data to a convolutional neural network to label the teeth and the gum and extracting a boundary between the teeth and the gum using labeled information of the teeth and the gum.