Method and system for automatically detecting and evaluating cardiovascular implanted stents on basis of OCT (optical coherence tomography)

Abstract

The invention provides a method and a system for automatically detecting and evaluating cardiovascular implanted stents on the basis of OCT (optical coherence tomography). The method includes steps of acquiring to-be-detected coronary artery OCT images; preprocessing the to-be-detected coronary artery OCT images; carrying out sliding window traversal on preprocessed images; extracting characteristics of sliding window images; judging whether extracted characteristic values of the sliding window images are higher than characteristic thresholds corresponding to classifier models or not; detecting regions where stents in the sliding window images are located and the centers of the stents; detecting contours of the inner walls of coronary arteries in the preprocessed images; computing the shortest distances from the centers of the stents to the contours of the inner walls of the coronary arteries to obtain wall attaching conditions or covering conditions of the stents; displaying the wall attaching conditions or covering conditions of the stents. The method and the system have the advantages that the system can work in complicated imaging environments without being affected by noise such as blood artifact, and the method and the system are high in stent detection accuracy and good in robustness.

Description

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AI Technical Summary

Benefits of technology

This technology allows medical professionals to easily detect and evaluate how well their heart valve works when they use an artificial device called prosthetic devices or bioprostheses (BP). These BPPs help prevent damage from blockages caused due to diseases like arteriosclerosis. They also improve patient outcomes through better control over placement of these devices within patients' bodies.

Problems solved by technology

This patented technical problem addressed in this patent relates to improving the precision and reliability of automatically identifying and monitoring medical conditions related to cardiovascula occlusion without requiring expensive equipment like angioplasty procedures or invasive surgeries. Current techniques involve manual identification and evaluation from x ray fluoroscopy images, but these require significant skill level expertise and may result in errors when performing certain types of operations. There exists a challenge in achieving accurate and reliable automated systems capable of accurately distinguishing between normal and pathological processes associated with cardiac vasospasm while minimizing patient risk and complications.

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