Artery plaque ultrasound image self-supervision segmentation method based on image restoration

An ultrasound image, arterial plaque technology, applied in the intersection of artificial intelligence and medical imaging, can solve the problem of inability to segment arterial plaque ultrasound images, and achieve the effect of improving accuracy

Pending Publication Date: 2021-07-30
HUBEI UNIV OF TECH
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Problems solved by technology

[0005] The present invention proposes a self-supervised segmentation method of ultrasonic image of arterial plaque based on image restoration, which is used to solve or at least partly solve the technical problem that the ultrasonic image segmentation of arterial plaque cannot be realized in the case of a small number of labeled samples in the prior art

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  • Artery plaque ultrasound image self-supervision segmentation method based on image restoration
  • Artery plaque ultrasound image self-supervision segmentation method based on image restoration
  • Artery plaque ultrasound image self-supervision segmentation method based on image restoration

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Embodiment Construction

[0030] The inventor of the present application has found through a large amount of research and practice:

[0031] How to use unlabeled samples to improve the accuracy, consistency and generalization ability of segmentation in the case of a small number of labeled samples has become a key problem to be solved urgently in the application of deep learning in ultrasonic image segmentation of arterial plaques. Self-supervised learning has been proposed to be used for the learning of few-label samples. It uses unlabeled samples to construct self-supervised auxiliary learning tasks, mines the inherent characteristic representation of samples and the regularity hidden behind the data, and uses them for subsequent learning tasks of few-label samples. . Self-supervised learning can be applied to image recognition, image segmentation, speech recognition and other fields, but due to the particularity of arterial plaque ultrasound images, low contrast, high noise and other characteristics...

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Abstract

The invention provides an artery plaque ultrasound image self-supervision segmentation method based on image restoration, which comprises the following steps: (1) preprocessing an artery ultrasound image training data set, (2) training a self-supervision auxiliary task network based on image restoration, and (3) migrating an auxiliary task model obtained in the step (2) to an artery plaque ultrasound image segmentation task, (4) training an artery plaque ultrasonic image segmentation convolutional neural network, and (5) segmenting an artery plaque ultrasonic test image by using the model obtained in the step (4), and outputting a result. The invention discloses an artery plaque ultrasonic image self-supervised segmentation method based on image restoration for the first time, artery plaque ultrasonic image segmentation under the condition of a small number of label samples is realized, and the accuracy of automatic measurement of artery plaques is improved. The method can be applied to an arterial ultrasound image auxiliary diagnosis system to monitor the growth and fading conditions of plaques, and is of great significance to early warning of heart and cerebral vessels.

Description

technical field [0001] The invention relates to the intersection field of artificial intelligence and medical imaging, in particular to a self-supervised segmentation method of ultrasonic image of arterial plaque based on image restoration. Background technique [0002] The rupture of carotid plaque is one of the main causes of cardiovascular and cerebrovascular diseases. The automatic measurement of arterial plaque load is of great significance for early warning of cardiovascular and cerebrovascular diseases. The measurement of arterial plaque load requires automatic segmentation of plaque contours. Traditional methods are used for ultrasonic image segmentation of arterial plaques, such as: level set, snake model, Bayesian model, etc. These methods often need to obtain an initial contour in advance, which makes the method more sensitive to manual experience or image quality, which limits the clinical application of such methods. With the extensive development of deep learn...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/12G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06T7/12G06N3/08G06T2207/10132G06T2207/20081G06T2207/30101G06N3/045G06F18/22G06F18/2415
Inventor 周然杨智欧阳瀚甘海涛叶志伟
Owner HUBEI UNIV OF TECH
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