Automated CBCT Parsing Pipeline for Dental Anatomical Localization
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Solution Overview
Problem
Current cone beam computed tomography (CBCT) technologies in dental diagnostics are hindered by time consumption and complexity in software usage, requiring specialized training, and existing image interpretation methods are not robust for anatomical localization and condition classification beyond the maxilla and mandible.
Innovation Solution
An automated parsing pipeline system utilizing a V-Net-based fully convolutional neural network for anatomical localization and a DenseNet 3-D convolutional neural network for condition classification, which processes volumetric image data to identify and classify anatomical structures within a defined field of view, employing voxel parsing and pre-processing techniques for accurate localization and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CBCT technology is used for dental diagnostics, then diagnostic capability is improved, but time consumption and software complexity increase
Solution Approach 1:
The system performs preliminary automated parsing of CBCT images into anatomical structures before diagnostic interpretation. The voxel parsing engine pre-processes the volumetric data to segment and label anatomical regions, so that when a dentist views the images, the diagnostic work is already partially completed with structures automatically identified and organized, reducing the time needed for manual analysis.
Solution Approach 2:
The system enables self-service diagnostics by allowing CBCT images to automatically interpret themselves through the automated parsing pipeline. The voxel-based segmentation and anatomical structure identification occur without human intervention, with the system independently completing the complex task of distinguishing anatomical regions, thereby eliminating the need for extensive specialized training and reducing time consumption.
2Reliability
If CBCT technology is used for dental diagnostics, then diagnostic capability is improved, but software complexity and training requirements increase
Solution Approach 1:
The system performs automated self-parsing of CBCT volumetric data into anatomical structures without requiring operator intervention or specialized knowledge. The voxel parsing engine independently segments the image volume, identifies anatomical boundaries, and classifies structures, allowing any dental professional to utilize advanced CBCT diagnostics regardless of their training level in interpreting complex 3D imaging software.
3Productivity
If automated parsing pipeline is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of CBCT image interpretation into distinct functional modules: volumetric data reception, voxel parsing, anatomical structure identification, and classification. This modular segmentation allows each component to be optimized independently while working together to achieve high diagnostic productivity, making the overall complex system manageable through functional decomposition.
Data Source
AI summary
An automated parsing pipeline system and method for anatomical localization and condition classification is disclosed. The system comprises an input even source, a memory unit and processor including a volumetric image processor, a voxel parsing engine, localization layer and a detection module. The volumetric image processor is configured to receive volumetric image from the input source and parse the received volumetric image. The voxel parsing engine is configured to assign each voxel a distant anatomical structure. The localization layer is configured to crop a defined anatomical structure with surroundings. The detection module is configured to classify conditions for each defined anatomical structure within the cropped image. The disclosed system and method provide accurate localization of a tooth and detects several common conditions in each tooth.