Pulp Region Segmentation Using Curvature Shape Recognition
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Solution Overview
Problem
Current methods for segmenting a tooth's pulp region from 2D or 3D images are time-consuming, subjective, and inadequate for visualizing complex root canal shapes, particularly in endodontic treatment planning, due to high variability in tooth shapes and the need for manual user interaction.
Innovation Solution
A computer-implemented method using curvature-based shape recognition and grayscale thresholding to iteratively segment the pulp region from a seed point, incorporating curvature-based shape recognition and grayscale thresholding to detect tubular and sheet-like structures, and handle intricate canal shapes, bifurcations, and obstructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user interaction is used to determine root canal positions and outline pathways, then segmentation accuracy can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs automatic segmentation of the pulp region using curvature-based shape recognition and grayscale thresholding algorithms that process the image independently without requiring manual user interaction to determine root canal positions or outline pathways, thereby eliminating time-consuming manual operations while maintaining segmentation accuracy through sophisticated automated image processing
Solution Approach 2:
The manual mechanical process of clicking control points and outlining root canals is replaced by an automated computational system using curvature-based shape recognition and grayscale thresholding algorithms that automatically detect and segment the pulp region, converting a manual interactive process into an automated computational one
2Productivity
If statistical pulp shape models are used for segmentation, then processing speed can be improved, but accuracy deteriorates due to high variability in tooth shapes
Solution Approach 1:
Instead of applying a single statistical pulp shape model that assumes uniform characteristics across all teeth, the system uses curvature-based shape recognition that adapts to local variations in tooth geometry, analyzing curvature properties at each point in the image to accommodate the high variability in tooth shapes while maintaining both processing speed and segmentation accuracy
Solution Approach 2:
The system changes the approach from using fixed statistical models to dynamically computing curvature-based parameters at different spatial scales, allowing the segmentation to adapt to the specific geometric characteristics of each tooth while maintaining efficient processing through algorithmic optimization
3Device complexity
If simple grayscale thresholding is used, then computational complexity is reduced, but ability to detect complex canal shapes and bifurcations deteriorates
Solution Approach 1:
The image processing is divided into multiple stages: initial grayscale thresholding to separate the pulp region from surrounding structures, followed by curvature-based shape recognition at different spatial scales to detect complex canal shapes and bifurcations. This segmented approach allows each stage to focus on specific features, maintaining computational efficiency while enhancing detection capability for intricate structures
Solution Approach 2:
The system enhances the simple grayscale thresholding by adding a curvature dimension, computing curvature-based shape recognition at different spatial scales. This additional dimensional information allows the system to detect complex canal shapes and bifurcations that would be invisible to simple intensity-based thresholding alone, without proportionally increasing computational complexity
Data Source
AI summary
Methods, systems, and computer programs are disclosed for segmenting, from an image, a tooth's pulp region comprising a pulp chamber and root canals. Curvature-based shape recognition is performed at different spatial scales using smoothed versions of the image. An indication of a point or region is received, located in the pulp chamber and referred to as “seed”. The seed is used as initial segmentation mask. An update procedure, iteratively carried out, comprises: (i) determining candidate image elements for updating the segmentation mask, comprising: (i−1) in the first n iteration(s), a grayscale thresholding taking as reference the current segmentation mask; and, (i−2) in at least one iteration, taking the curvature-based shape recognition into account; (ii) retaining, among the candidate image elements, a region of connected candidate image elements that comprises the seed; and (iii) using the retained region to update the segmentation mask.