User-Assisted Tooth Segmentation in Dental CBCT Images
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Solution Overview
Problem
Current methods for tooth segmentation in dental CBCT images face challenges in accurately separating teeth, particularly when they touch each other, due to limitations in selecting threshold values and distinguishing between tooth and non-tooth tissue, leading to poor dissection and incorrect separation lines.
Innovation Solution
A user-assisted segmentation method that involves acquiring image data, displaying views, identifying boundary points, forming foreground and background seed curves, and applying segmentation based on these curves to enhance the separation of teeth, integrating human operator skills with computational capabilities for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic segmentation methods using thresholding and region growing are used, then the segmentation process is fast and automated, but the accuracy of tooth separation is poor and dissection between teeth is incorrect
Solution Approach 1:
The patent introduces separating curves as intermediary elements that manually define tooth boundaries. These curves act as mediators between the automated segmentation process and the desired accurate separation, allowing user-defined geometric constraints to guide the segmentation algorithm in correctly dividing touching teeth while maintaining automation for the overall process.
Solution Approach 2:
The patent applies preliminary geometric constraints and separating curves before the final segmentation occurs. By pre-defining boundary curves and geometric primitives that represent tooth separators, the system prepares the segmentation process with accurate spatial guidance, ensuring that when the automated algorithm executes, it follows pre-established correct separation paths rather than relying solely on intensity thresholds.
2Adaptability or versatility
If multiple threshold values are selected for segmentation, then the segmentation covers different tissue regions, but it is very difficult to select the correct threshold values for proper segmentation
Solution Approach 1:
The patent enables the system to automatically determine appropriate threshold values and segmentation parameters based on the image data and user-defined geometric constraints. Rather than requiring manual selection of multiple thresholds, the algorithm self-adjusts by using the separating curves and intensity information within defined regions to automatically identify tissue boundaries, making the process easier to operate while maintaining versatility.
Solution Approach 2:
The patent dynamically adjusts segmentation parameters including threshold values based on the geometric constraints and image characteristics. The system changes parameters adaptively during processing, using the user-defined separating curves to guide threshold selection and region definition, thereby eliminating the need for manual threshold selection while maintaining the ability to segment different tissue regions.
3Reliability
If separating planes are projected along dental arch points, then the method attempts to separate touching teeth, but the separation lines frequently cut through the teeth instead of properly separating them
Solution Approach 1:
The patent inverts the conventional approach by instead of projecting separating planes from the dental arch and hoping they correctly divide teeth, it allows users to directly define separating curves along the actual tooth boundaries. This inversion of the separation definition approach ensures that separators follow true anatomical boundaries rather than geometric approximations, dramatically improving reliability.
Solution Approach 2:
The patent applies local geometric constraints and separating curves at specific tooth boundary locations rather than using global separating planes. By allowing user-defined curves to precisely follow local tooth contours and contact points, the system achieves accurate local separation for each tooth interface, ensuring that separation lines conform to actual tooth geometry rather than imposing external geometric models.
Data Source
AI summary
A method for segmenting a feature of interest from a volume image acquires image data elements from the image of a subject. At least one view of the acquired volume is displayed. One or more boundary points along a boundary of the feature of interest are identified according to one or more geometric primitives defined by a user with reference to the displayed view. A foreground seed curve defined according to the one or more identified boundary points and a background seed curve encompassing and spaced apart from the foreground seed curve are formed. Segmentation is applied to the volume image according to foreground values that are spatially bounded within the foreground seed curve and according to background values that lie outside the background seed curve. An image of the segmented feature of interest is displayed.