Automatic OCT TCFA Detection With Gradient and Triangle Thresholding
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Solution Overview
Problem
Current methods for detecting thin-cap fibroatheroma (TCFA) in Optical Coherence Tomography (OCT) images are time-consuming, prone to human error, and lack precision, posing a risk of incorrect analysis that can lead to adverse medical outcomes.
Innovation Solution
A method using mathematical algorithms to automatically detect TCFA by processing OCT images, combining 'Gradient Guessing' and 'Triangle Thresholding' algorithms to approximate the fibrous cap border, followed by smoothing and measuring cap thickness, with the option to adjust thresholds for fibrous cap definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of OCT images is performed by specialists, then detection accuracy can be maintained, but analysis time becomes excessively long and human error occurs
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated image processing system that uses mathematical algorithms (gradient guessing and triangle thresholding) to detect TCFA. This substitution eliminates human labor while maintaining detection accuracy through objective computational methods that can process images rapidly without fatigue or distraction.
Solution Approach 2:
The system enables self-service by allowing the OCT image analysis to be performed automatically without requiring specialist intervention. The automated algorithms independently process the images, detect lipid plaques, measure fibrous cap thickness, and generate diagnostic results, making the system self-sufficient and eliminating dependency on manual analysis.
2Reliability
If manual analysis is performed, then detailed examination is possible, but human error can lead to incorrect procedures
Solution Approach 1:
The patent eliminates human error by replacing manual analysis with automated computational algorithms. The system uses objective mathematical methods (gradient guessing and triangle thresholding) that consistently apply the same detection criteria without fatigue, distraction, or subjective bias, thereby eliminating the source of human error entirely.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated detection results can be reviewed and adjusted. The algorithm provides measurable, quantifiable results (cap thickness measurements, lipid plaque detection) that can be verified and corrected if needed, creating a feedback loop that maintains high reliability while allowing for quality control.
3Productivity
If automated detection systems are implemented, then analysis time is reduced, but precision and accuracy may be compromised
Solution Approach 1:
The patent achieves both speed and precision by using sophisticated mathematical algorithms that can process images rapidly while maintaining high measurement accuracy. The gradient guessing and triangle thresholding methods are computationally efficient yet produce precise measurements of fibrous cap thickness, eliminating the trade-off between speed and precision that plagues simpler automated systems.
Solution Approach 2:
The system allows for adjustable parameters and thresholds that can be optimized for different clinical scenarios. By changing detection parameters and threshold values, the system can adapt to maintain precision across various image qualities and patient conditions while consistently delivering fast automated results.
4Stability of the object's composition
If fixed detection criteria are used, then consistency is maintained, but adaptability to changing medical definitions is lost
Solution Approach 1:
The patent implements a dynamic detection system where the algorithms and threshold values can be adjusted to reflect changing medical definitions of TCFA. The system maintains consistency through standardized computational methods while allowing updates to detection criteria, thresholds, and algorithm parameters to accommodate evolving clinical understanding and guidelines.
Solution Approach 2:
The system enables parameter changes by allowing modification of detection thresholds, cap thickness criteria, and algorithm settings. This flexibility permits the system to adapt to new medical definitions and guidelines while maintaining the structured, consistent approach of automated computational analysis.
Data Source
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AI summary
The invention is a method and set-up to detect and mark lipid plaques with thin fibrous cap (TCFA) on Optical Coherence Tomography (OCT) images. Subject of the invention is also a data carrier capable of realizing the invented method.