Patch-Loaded Multi-Planar Reconstruction for Dental CBCT Imaging
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Solution Overview
Problem
Current dental imaging technologies, such as cone beam computed tomography (CBCT), face challenges including time consumption, complexity in software usage, limited training for dental professionals, and a lack of automated anatomical localization and pathology detection/classification, particularly in generating accurate 3D teeth segmentation masks with minimal image analysis training and visual ambiguities.
Innovation Solution
An automated parsing pipeline system and method that utilizes deep learning models for anatomical localization and condition classification, constructing panoramas with Elements of Interest (EoI) emphasized, and generating accurate 3D teeth segmentation masks, incorporating a voxel parsing engine and localization layer for parsing volumetric images into patches, allowing for efficient processing and visualization of dental images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CBCT imaging is used, then 3D visualization capability is provided, but time consumption increases and software complexity increases
Solution Approach 1:
The patent segments the CBCT imaging process into automated components: deep learning models automatically segment and classify anatomical structures, generate segmentation masks, and create panoramic views. This automation eliminates manual segmentation time while maintaining 3D visualization precision.
Solution Approach 2:
The system performs self-service through automated deep learning algorithms that independently complete imaging analysis tasks. The neural networks automatically process volumetric data, identify anatomical structures, generate segmentation masks, and create panoramic views without requiring manual intervention, thereby reducing time consumption while maintaining measurement precision.
2Productivity
If deep learning models are applied for automated anatomical localization, then productivity increases, but device complexity increases
Solution Approach 1:
The patent employs a universal deep learning framework that performs multiple functions through a single integrated system. The neural networks simultaneously localize anatomical structures, generate segmentation masks, classify pathologies, and create panoramic views, thereby increasing productivity without proportionally increasing complexity through modular architecture design.
Solution Approach 2:
The patent introduces deep learning models as intermediary components between the CBCT scanner and the final diagnostic output. These intermediaries automatically process the volumetric data, performing segmentation, localization, and classification tasks that would otherwise require complex manual workflows, thus improving productivity while managing system complexity through specialized automated modules.
3Measurement precision
If manual segmentation and verification is performed, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
The patent implements feedback mechanisms where the deep learning models continuously refine their segmentation outputs. The system generates initial segmentation masks that are then verified and adjusted through feedback loops, allowing automated processing to achieve precision comparable to manual verification while eliminating the time-consuming manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual segmentation process with automated deep learning algorithms. The neural networks perform segmentation, localization, and classification tasks that were previously done manually, maintaining measurement precision through sophisticated computational methods while eliminating the time loss associated with manual processing.
4Measurement precision
If complete volumetric image processing is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent segments the volumetric image processing into focused regions of interest. Instead of processing the entire volumetric dataset, the deep learning models identify and process only the relevant anatomical structures and regions, maintaining localization accuracy while reducing the computational energy required to process the complete volume.
Solution Approach 2:
The patent applies local quality processing by focusing computational resources on specific anatomical regions and structures. The deep learning models dynamically adjust their processing intensity based on the local characteristics of the image data, applying higher precision where needed and reducing computation in less critical areas, thereby maintaining measurement precision while optimizing energy consumption.
Data Source
AI summary
A method for generating a patch-loaded Multi-Planar Reconstruction (MPR), comprising the steps of: parsing a volumetric image into a plurality of patches for storage; and loading the stored patch corresponding to the targeted region of interest requested by a user for display in at least one of a plane of the generated load-patched MPR.