Automated Tooth Segmentation in CBCT Volumes
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Solution Overview
Problem
Current methods for tooth segmentation from CBCT data are not robust enough for widespread application, as they often require manual intervention, struggle with accurate separation of teeth, and do not allow for effective visualization or manipulation of teeth for treatment planning in orthodontia.
Innovation Solution
An automated method for tooth segmentation and alignment detection in CBCT images, which includes acquiring volume image data, estimating average tooth height, detecting separation curves, defining individual tooth sub-volumes, and displaying segmented teeth, allowing for digital manipulation and alignment assessment without operator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tooth segmentation methods are implemented, then productivity and ease of operation are improved, but reliability and measurement precision deteriorate due to lack of robustness and accurate separation
Solution Approach 1:
The patent applies segmentation by dividing the tooth segmentation task into multiple stages: initial automated segmentation, followed by refinement through operator intervention on uncertain regions. The system segments teeth automatically first to improve productivity, then identifies uncertain segments that require manual review to maintain reliability.
Solution Approach 2:
The system performs partial automation by applying automated segmentation only to regions where it can achieve sufficient accuracy, while leaving complex or ambiguous regions for manual processing. This partial action approach balances productivity gains from automation with reliability maintenance through selective manual intervention.
2Ease of operation
If fully automated segmentation is used, then ease of operation is improved, but measurement precision worsens due to inability to accurately separate teeth in complex cases
Solution Approach 1:
The patent segments the processing workflow into automated and manual components. The automated system handles straightforward cases to improve ease of operation, while uncertain cases are segmented out for manual review to maintain measurement precision.
Solution Approach 2:
The system introduces an intermediary review stage where operator expertise mediates between automated segmentation results and final accurate segmentation. This intermediary layer allows the system to maintain ease of operation through automation while preserving measurement precision through expert oversight when needed.
3Measurement precision
If manual intervention is required for accurate segmentation, then measurement precision is improved, but productivity and ease of operation worsen
Solution Approach 1:
Manual intervention is applied partially only to uncertain or complex segmentation cases rather than all cases. This selective manual action maintains measurement precision for difficult cases while preserving productivity by avoiding manual processing of straightforward cases that the automated system can handle accurately.
Solution Approach 2:
The automated segmentation system serves itself by initially processing all cases, then automatically identifying which cases require manual intervention. This self-service approach minimizes the burden on operators by having the system itself determine when human expertise is needed, thereby maintaining productivity while ensuring measurement precision.
4Productivity
If current automated methods are deployed, then productivity is improved, but reliability worsens due to lack of robustness for widespread application
Solution Approach 1:
The patent segments the deployment strategy into automated processing for routine cases and manual oversight for complex cases. This segmented approach allows the system to achieve high productivity for the majority of cases while maintaining reliability through targeted manual intervention in challenging scenarios.
Solution Approach 2:
The system prepares for potential failures by having a manual review process ready as a cushion against automated segmentation errors. This beforehand cushioning ensures that even if the automated system encounters edge cases or fails, reliability is maintained through the pre-established manual intervention capability.
Data Source
AI summary
A method of automatic tooth segmentation, the method executed at least in part on a computer system acquires volume image data for either or both upper and lower jaw regions of a patient and identifies image content for a specified jaw from the acquired volume image data. For the specified jaw, the method estimates average tooth height for teeth within the specified jaw, finds a jaw arch region, detects one or more separation curves between teeth in the jaw arch region, defines an individual tooth sub volume according to the estimated average tooth height and the detected separation curves, segments at least one tooth from within the defined sub-volume, and displays the at least one segmented tooth.