Vulnerable Plaque Identification via Polar Coordinate OCT and Faster RCNN
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Solution Overview
Problem
Traditional methods for identifying vulnerable plaques in OCT images are time-consuming and lack accuracy, posing a challenge in efficiently and correctly determining their position for cardiovascular health management.
Innovation Solution
A vulnerable plaque identification method that transforms OCT images from a Cartesian to a polar coordinate system and utilizes a trained Faster RCNN architecture to mark and identify vulnerable plaques, enabling quick and accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to identify vulnerable plaques in OCT images, then doctors can analyze plaque positions, but the process takes a long time and accuracy does not reach expectations
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based image processing system. The system transforms OCT images from Cartesian to polar coordinate systems and applies Faster RCNN neural network architecture to automatically identify vulnerable plaques, eliminating manual analysis while improving both speed and accuracy through algorithmic detection
Solution Approach 2:
The patent changes the coordinate system parameter from Cartesian to polar coordinates, which transforms the image representation to better suit circular coronary artery structures. This parameter transformation enables more accurate detection of vulnerable plaques by aligning the coordinate system with the natural geometry of blood vessels
2Productivity
If traditional identification methods are used, then doctors can assess cardiovascular health, but the process is time-consuming and reduces diagnostic efficiency
Solution Approach 1:
The patent replaces time-consuming manual diagnostic processes with automated computer-based image analysis. The system rapidly processes OCT images through coordinate transformation and neural network classification, delivering diagnostic results in seconds rather than minutes or hours, thereby significantly improving diagnostic efficiency
Solution Approach 2:
The patent performs preliminary automated analysis of OCT images by pre-processing them into polar coordinate systems and applying trained neural networks before clinical review. This preliminary action filters and highlights vulnerable plaques, allowing doctors to focus on confirmed cases rather than manually examining every image, thus improving overall diagnostic throughput
Data Source
AI summary
The present disclosure publishes a vulnerable plaque identification method. The method includes: receiving an angiocarpy image sent by a terminal device; transforming the angiocarpy image in an original Cartesian coordinate system into a polar coordinate system to form a polarization image; constructing a faster RCNN architecture and accomplishing a training; inputting the polarization image into the faster RCNN architecture accomplished the training to identify the polarization image, and outputting the image with the marked vulnerable plaques; feed backing the image with the marked vulnerable plaques to the terminal device. The present disclosure also publishes an application server and a computer readable medium. The vulnerable plaque identification method, the application server, and the computer readable medium can quickly and correctly conform the position of the vulnerable plaque.