3D Dental Mesh Segmentation Without 2D Projection Errors
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Solution Overview
Problem
Existing dental and orthodontic systems face challenges in accurately converting 2D dental images to 3D mesh geometry due to loss of accuracy in mapping and inefficient data processing, often requiring additional machine learning models to disambiguate conflicting labels, leading to complexity and errors.
Innovation Solution
Direct labeling of 3D mesh elements without intermediate 2D images, utilizing machine learning techniques such as MeshCNN and neural networks for improved segmentation and validation, including pre-processing to generate feature vectors from mesh elements, and employing federated learning to train models locally while preserving data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D images are projected onto 3D mesh geometry to label portions of the mesh, then the labeling process can be performed, but accuracy is lost in the mapping and processing becomes inefficient
Solution Approach 1:
Instead of projecting 2D image labels onto 3D mesh geometry (traditional approach), the patent inverts the process by directly labeling 3D mesh elements using machine learning models that operate natively on 3D data. This eliminates the 2D-to-3D projection step that causes accuracy loss and processing inefficiency.
Solution Approach 2:
The patent introduces an intermediary machine learning model (such as MeshCNN or point cloud networks) that acts as a mediator between the input data and the 3D mesh labeling process. This intermediary processes 3D features directly and generates labels without requiring 2D image projections, thereby maintaining accuracy and improving efficiency.
2Reliability
If multiple projection operations are performed to label 3D mesh elements, then coverage is improved, but conflicting labels are generated requiring additional machine learning models to disambiguate
Solution Approach 1:
The patent inverts the traditional multi-projection approach by using a single machine learning model that directly processes 3D mesh data and assigns labels. This eliminates the need for multiple projection operations and the subsequent complexity of resolving conflicting labels from different projections.
Solution Approach 2:
The patent extracts and removes the problematic multi-projection labeling step from the system. By using direct 3D mesh labeling through machine learning, it eliminates the source of conflicting labels and the need for additional disambiguation models, thereby reducing system complexity while maintaining reliability.
3Manufacturing precision
If 2D to 3D conversion is performed using existing systems, then the conversion can be completed, but the mapping accuracy is lost and processing is inefficient
Solution Approach 1:
The patent inverts the conventional 2D-to-3D conversion pipeline by using machine learning models that operate directly on 3D mesh data from the beginning. This eliminates the sequential 2D image processing followed by projection onto 3D geometry, thereby maintaining manufacturing precision and reducing conversion time.
Solution Approach 2:
The patent replaces the mechanical 2D-to-3D projection system with a machine learning-based direct 3D processing system. This substitution eliminates the inherent inaccuracies and time losses associated with geometric projection, achieving both higher precision and faster conversion.
Data Source
AI summary
Systems and techniques for training one or more neural networks to automatically generate tooth segmentation data used in digital dentistry are disclosed including predicting one or more segmentation labels pertaining to aspects of dental geometry, generating an accuracy score that specifies a difference between the one or more predicted representations and one or more respective reference and modifying at least one aspect of the neural network based on the accuracy score.