3D Facial Scan And Medical Image Registration Using Deep Learning Landmarks
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Solution Overview
Problem
Existing methods for registering 3D facial scan mesh model data and 3D volumetric medical image data require manual intervention, are time-consuming, and can be inaccurate due to differences in data structures and quality, especially with low-quality CBCT images or metal noise, and often result in inconsistent results.
Innovation Solution
An automated registration method using deep learning, specifically through convolutional neural networks, to extract scan and volume landmarks from 3D facial scan and volumetric medical image data, respectively, without requiring user input or data structure conversion, enabling fast and accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual registration process is used to align 3D facial scan data and 3D volumetric medical image data, then registration accuracy can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs automated landmark detection and registration without requiring manual user input. The deep learning model automatically identifies corresponding landmarks on both 3D facial scan data and 3D volumetric medical image data, and the registration process executes autonomously based on detected landmarks, eliminating the need for manual intervention while maintaining high accuracy
Solution Approach 2:
The patent replaces the manual mechanical registration process with an automated computational system. Instead of manual manipulation and visual alignment, the system uses deep learning-based landmark detection followed by automated transformation calculation, substituting human-operated mechanical alignment with algorithm-driven computational registration
2Adaptability or versatility
If data structure conversion (voxelization or mesh extraction) is performed to unify different data structures, then registration compatibility is improved, but computing load and processing time increase
Solution Approach 1:
The patent extracts only the essential landmark points from both 3D facial scan data and 3D volumetric medical image data using deep learning models. Instead of converting entire data structures, the system selectively extracts key landmark coordinates that are sufficient for registration, significantly reducing computational complexity while maintaining registration compatibility
Solution Approach 2:
The system changes the representation parameters from complete 3D mesh or volumetric data to simplified landmark coordinate sets. By transforming the problem from registering entire complex data structures to aligning discrete landmark points, the patent reduces computational load while preserving the essential geometric information needed for accurate registration
3Measurement precision
If segmentation and 3D reconstruction processes are applied to extract facial regions from volumetric medical images, then registration precision is improved, but processing time and operational complexity increase
Solution Approach 1:
The patent performs preliminary landmark detection directly on the original 3D volumetric medical image data and 3D facial scan data before any registration operations. The deep learning models pre-identify all necessary landmark points, eliminating the need for subsequent segmentation and reconstruction steps that would add operational complexity
Solution Approach 2:
The system creates simplified landmark coordinate representations as copies of the essential geometric features from the original complex 3D data. Instead of performing complex segmentation and reconstruction, the patent uses landmark detection to create simplified point-based copies that capture the necessary geometric information for precise registration
Data Source
AI summary
An automated registration method of 3D facial scan data and 3D volumetric medical image data using deep learning, includes extracting scan landmarks from the 3D facial scan data using a convolutional neural network, extracting volume landmarks from the 3D volumetric medical image data using the convolutional neural network and operating an initial registration of the 3D facial scan data and the 3D volumetric medical image data using the scan landmarks and the volume landmarks.