CBCT Tooth Dissection Curve Generation via Minimum Intensity Points
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Solution Overview
Problem
Current methods for tooth segmentation in dental images, particularly in 3D CBCT volumes, face challenges in accurately and robustly dissecting teeth without cutting through the region of interest, leading to inefficient feature extraction and diagnosis.
Innovation Solution
A method that generates a dissection curve between objects in a volume image by accessing image slices, identifying regions of interest, defining starting points, and connecting points of minimum intensity, integrating human operator input with computer processing for accurate tooth segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tooth segmentation methods are used, then productivity is improved, but manufacturing precision deteriorates due to inaccurate dissection curves cutting through tooth regions
Solution Approach 1:
The patent introduces an intermediary geometric primitive (such as a line or curve) that bridges the gap between automated processing and manual precision. The system automatically generates candidate dissection curves, then uses operator input to define geometric primitives that serve as mediators to adjust and refine these curves, ensuring they pass through correct anatomical landmarks without cutting through tooth regions.
Solution Approach 2:
The system implements feedback by allowing operators to review and adjust automatically generated dissection curves. The geometric primitive defined by the operator provides feedback to the algorithm, which then refines the curve placement. This iterative process continues until the dissection curve accurately separates teeth without cutting through them, combining automated efficiency with manual precision.
2Manufacturing precision
If manual operator input is required for dissection, then manufacturing precision is improved, but productivity deteriorates due to increased time consumption
Solution Approach 1:
The system performs preliminary automated processing to generate candidate dissection curves and identify potential geometric primitives before operator intervention. This preliminary action reduces the operator's workload to only the critical decision-making steps, maintaining high precision while minimizing time consumption. The bulk of the processing is already completed automatically.
Solution Approach 2:
Instead of requiring complete manual tracing of dissection curves, the system performs partial automation by generating candidate curves and allowing operators to make selective adjustments only where needed. This partial action approach achieves the necessary precision without the time cost of full manual processing.
3Device complexity
If simple intensity-based contour estimation is used, then device complexity is reduced, but measurement precision deteriorates in cutting through tooth regions
Solution Approach 1:
The geometric primitive serves as an intermediary between simple intensity-based contour estimation and accurate tooth dissection. While the initial contour estimation can remain simple and intensity-based, the geometric primitive defined by the operator mediates the refinement process, guiding the dissection curve to pass through correct anatomical landmarks without requiring complex algorithms.
Data Source
AI summary
A method of generating a dissection curve between a first and a second object in a volume image. The method accesses volume image data of a subject as a set of image slices and identifies a region of the volume image data that includes at least the first and second objects. At least one starting point in the volume image data is defined for the dissection curve according to a geometric primitive entered by an operator. Successive dissection curve points are identified according to points of minimum intensity in successive image slices. The dissection curve that connects the identified plurality of successive dissection curve points is displayed.