Carotid artery ultrasound image plaque classification detection method and system

A technology for ultrasonic image and classification detection, which is applied in ultrasonic/sonic/infrasonic diagnosis, acoustic diagnosis, infrasonic diagnosis, etc. It can solve the problems of insufficient accuracy and increased marking workload, so as to ensure accuracy and save workload. Effect

Pending Publication Date: 2022-02-25
SHANDONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In carotid artery ultrasound images, there are plaques of different sizes and types. Previous techniques generally use fully supervised learning with all plaque labels or unsupervised learning without labels, which will increase the workload of labeling or the accuracy rate is not enough.

Method used

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  • Carotid artery ultrasound image plaque classification detection method and system
  • Carotid artery ultrasound image plaque classification detection method and system
  • Carotid artery ultrasound image plaque classification detection method and system

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Experimental program
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Embodiment 1

[0032] like figure 1 As shown, the present embodiment provides a carotid artery ultrasound image plaque classification detection method, which specifically includes the following steps:

[0033] S101: Acquiring video information of carotid artery transverse section and carotid artery longitudinal section video information.

[0034] In a specific implementation, the video information of the cross-section of the carotid artery and the video information of the longitudinal section of the carotid artery are acquired by ultrasonic equipment.

[0035] It should be noted that the ultrasound acquisition module includes, but is not limited to, ultrasound acquisition equipment, handheld ultrasound equipment, and 5G remote ultrasound acquisition equipment.

[0036] S102: Extract key continuous frames from the video information of the carotid artery transverse section and the video information of the carotid artery longitudinal section, and perform feature enhancement on the extracted ke...

Embodiment 2

[0056] Such as image 3 As shown, the present embodiment provides a carotid artery ultrasound image plaque classification detection system, which specifically includes the following modules:

[0057] Video information acquisition module 201, which is used to acquire video information of carotid artery transverse section and carotid artery longitudinal section video information;

[0058] A feature enhancement module 202, which is used to extract key continuous frames from the video information of the carotid artery transverse section and the video information of the carotid artery longitudinal section, and perform feature enhancement on the extracted key continuous frames;

[0059] The pixel-level segmentation module 203 is used to predict the identity of the carotid artery cross-section image and carotid artery longitudinal section image after feature enhancement, and then track the images corresponding to each identity and perform pixel-level segmentation to achieve identity ...

Embodiment 3

[0064] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the method for classifying and detecting carotid artery ultrasound image plaques as described above are implemented.

[0065] Among them, the computer-readable storage medium is such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like.

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Abstract

The invention belongs to the field of image classification detection, and provides a carotid artery ultrasound image plaque classification detection method and system. The method comprises the following steps: collecting carotid artery cross section video information and carotid artery longitudinal section video information; key continuous frame extraction is carried out on the carotid artery cross section video information and the carotid artery longitudinal section video information, and feature enhancement is carried out on the extracted key continuous frames; performing identity prediction on the carotid artery cross section image and the carotid artery longitudinal section image after feature enhancement, tracking images corresponding to identities, and performing pixel-level segmentation to realize association of identity information and segmentation results in a time domain; according to a segmentation result, traversing each column of mask pixels, determining coordinates through color difference marks, and determining the size, area and stenosis rate of plaques corresponding to identity information associated with the segmentation result; and determining the plaque type according to the plaque size, the plaque area and the stenosis rate so as to output early warning prompts of different degrees.

Description

technical field [0001] The invention belongs to the field of image classification and detection, and in particular relates to a carotid artery ultrasound image plaque classification and detection method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Carotid artery plaque can lead to a variety of brain diseases. Because there are different types of plaque, once the vulnerable plaque is damaged, it will cause irreversible damage to the human body. In order to detect the type of plaque early so as to prevent different diseases in time In view of the damage caused by plaque changes, a carotid plaque classification and detection system is needed to assist diagnosis and improve diagnostic efficiency. [0004] In carotid artery ultrasound images, there are plaques of different sizes and types. Previous techniques generally use fully...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/764G06V10/26G06K9/62A61B8/00
CPCA61B8/5223G06F18/24
Inventor 刘治隋小瑜曹艳坤
Owner SHANDONG UNIV
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