Vertebral Endplate Rim Detection Using Edge Templates
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Solution Overview
Problem
Existing methods for detecting anatomical structures, such as vertebral endplate rims, in medical images are inefficient and require significant computational resources, particularly in surgical planning and intraoperative assessment.
Innovation Solution
A system and method that utilizes image processing algorithms to identify sharp edges and interpolate to detect vertebral endplate rims without relying on machine learning, using gradient direction changes and templates for accurate segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning methods are used to detect anatomical structures, then detection accuracy may be improved, but computational resources and training data requirements increase significantly
Solution Approach 1:
The patent replaces machine learning-based detection systems with traditional image processing algorithms including gradient calculation, edge detection, and template matching. This substitution eliminates the need for extensive training data and complex computational resources while maintaining detection capability for anatomical structures such as vertebral endplate rims.
Solution Approach 2:
The patent employs simple, computationally inexpensive algorithms that can be executed quickly without requiring expensive hardware or extensive training datasets. The approach uses basic image processing operations that are resource-efficient and can be performed with minimal computational infrastructure.
2Device complexity
If traditional image processing methods are used, then computational resources are reduced, but detection accuracy and efficiency decrease
Solution Approach 1:
The patent segments the detection process into distinct stages: gradient calculation to identify edge directions, edge detection to locate anatomical boundaries, template matching to recognize specific structures, and interpolation to refine boundary positions. This segmentation allows each stage to be optimized independently, improving overall detection efficiency while maintaining low computational requirements.
Solution Approach 2:
The patent performs preliminary gradient calculation and edge detection before template matching, preparing the image data in advance to facilitate faster and more accurate structure detection. This preliminary processing organizes the information in a way that accelerates subsequent detection steps.
3Measurement precision
If complex detection algorithms are used, then detection precision improves, but processing time increases
Solution Approach 1:
The patent replaces time-consuming machine learning inference processes with efficient traditional image processing algorithms. Gradient-based edge detection and template matching can be executed rapidly using standard image processing libraries, achieving high detection precision without the computational overhead of neural networks.
Solution Approach 2:
The patent adjusts image processing parameters such as gradient threshold values, template sizes, and interpolation intervals to optimize the balance between detection precision and processing speed. By carefully selecting these parameters, the system achieves accurate detection while minimizing processing time.
Data Source
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AI summary
A method of detecting a vertebral endplate rim according to embodiments of the present disclosure includes receiving, at a processor, a binary mask of a vertebra; applying a Laplacian filter to the binary mask to yield a processed image; identifying, in the processed image, a set of edges having a sharpness that exceeds a predetermined threshold; deformably registering a template to a plurality of points corresponding to at least some of the edges in the set of edges; excluding some of the plurality of points based on at least one predetermined criterion; and generating additional points by interpolating among a remaining subset of the plurality of points to yield a set of points defining an endplate rim of the vertebra.