Dental Image Conversion Using Pre-Trained Generative Models
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Solution Overview
Problem
It is challenging for specialists to gain patient trust by showing the state after treatment or the treatment process in medical imaging, particularly in dental treatments, due to the limitations of existing technologies.
Innovation Solution
A deep learning-based method and device for converting dental images to virtually create desired shapes, such as virtual teeth or dental implants, by using a pre-trained generative model that reflects the target type of the conversion area, including normal, post-treatment, abnormal, and under-treatment states, while addressing data bias through training with annotated semantic areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a deep learning-based generative model is used to convert dental images to show treatment outcomes, then the ability to visualize treatment processes and outcomes is improved, but the complexity of the system increases
Solution Approach 1:
The system pre-trains multiple generative models corresponding to different target types (normal state, post-treatment state, abnormal state, under-treatment state) before actual use. This preliminary preparation allows the system to quickly generate treatment outcome visualizations without complex real-time computations, resolving the contradiction between visualization capability and system complexity.
Solution Approach 2:
The conversion process is divided into distinct segments: receiving dental image, receiving user input for conversion area and target type, masking the conversion area, and generating output image using the appropriate pre-trained model. This segmentation simplifies the overall complex process into manageable steps, reducing system complexity while maintaining visualization capability.
2Measurement precision
If the conversion area is masked and processed through a generative model, then the accuracy of showing treatment outcomes is improved, but the processing time increases
Solution Approach 1:
Multiple generative models are pre-trained in advance for different target types (normal state, post-treatment state, abnormal state, under-treatment state). During actual conversion, the system only needs to select the appropriate pre-trained model and apply it to the masked conversion area, significantly reducing processing time while maintaining high conversion accuracy.
3Reliability
If virtual areas with abnormal findings are generated to balance data distribution, then the generalization performance of the model is improved, but the complexity of data processing increases
Solution Approach 1:
The system performs data balancing in advance by generating virtual areas with abnormal findings during the training phase. This preliminary data preparation creates a more balanced training dataset that improves model generalization performance. During actual operation, the system simply uses the pre-processed balanced data without additional complex processing, resolving the contradiction between reliability and data processing complexity.
Data Source
AI summary
Disclosed are a method and an apparatus for converting a part of a dental image. The method for converting a part of a dental image may comprise the steps of: receiving a dental image; receiving a user input corresponding to a conversion region of the dental image and a target type; and generating an output dental image in which the conversion region has been converted into the target type by using a pre-trained generation model.