Automatic identification method of vulnerable plaques of cardiovascular optical-coherence-tomography (OCT) image

Abstract

The invention discloses an automatic identification method of vulnerable plaques of a cardiovascular optical-coherence-tomography (OCT) image. The method includes the following steps: a) collecting cardiovascular OCT images; b) standardizing the cardiovascular OCT images; c) carrying out vulnerable-plaque identification on images after standardization, d) adopting a stacked auto-encoding method touse a sample set to construct deep learning models; e) carrying out classification identification; f) adopting a bilinear interpolation method to carry out Cartesian coordinate system transformation;g) carrying out quadrant dividing on an image after coordinate system transformation; and h) judging whether two or more connected regions exist in the same quadrant in an image after quadrant dividing. Characteristics of the cardiovascular OCT images are combined, the sample data set is reconstructed, and the problem of limited sample numbers is overcome. In addition, the learning models are often impacted by a training set, but the method randomly extracts samples in a data set for many times to train the learning models, and forms an ensemble learning model through a voting strategy. A large number of experiments prove that the technology can achieve higher identification precision.

Description

technical field [0001] The invention relates to the technical fields of image analysis and machine learning, in particular to a method for automatic identification of vulnerable plaques in cardiovascular optical coherence tomography OCT images. Background technique [0002] Cardiovascular disease is the main cause of morbidity and death. With the advancement of image analysis and machine learning technology, the diagnosis and treatment of cardiovascular disease have developed rapidly. Optical coherence tomography (Optical Coherence Tomography, OCT) is a new medical imaging technology, which has been widely used in clinic. This technology has also been applied in cardiovascular imaging. It can distinguish the structure of blood vessel walls, accurately display the characteristics of atherosclerotic plaques, and identify vulnerable plaques. It plays a very important role in the diagnosis, identification, treatment and evaluation of cardiovascular lesions. . [0003] Before i...

AI Technical Summary

Problems solved by technology

In the field of medical image analysis, the number of labeled samples is limited, which brings great challenges to the application of deep learning in medical image analysis.

[0005] At present, through investigation and research, it is found that there is no automatic recognition technology for vulnerable plaques in cardiovascular OCT images using artificial intelligence technology at home and abroad.

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