Heart nuclear magnetic resonance image key point detection method based on convolutional neural network

A convolutional neural network and nuclear magnetic resonance image technology, applied in neural learning methods, biological neural network models, image enhancement, etc., can solve problems affecting detection efficiency, noise interference, uneven distribution of pixel values, etc., to ensure accuracy , Reduce the false positive rate and ensure the effect of accurate prediction

Active Publication Date: 2020-05-12
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

In the prior art, for threshold-based methods, morphological processing-based and shape feature-based detection methods, noise interference in MRI images, uneven distribution of pixel values, differences in the shape of the left ventricle, and high similarity between the v

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  • Heart nuclear magnetic resonance image key point detection method based on convolutional neural network
  • Heart nuclear magnetic resonance image key point detection method based on convolutional neural network
  • Heart nuclear magnetic resonance image key point detection method based on convolutional neural network

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[0050] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0051] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0052] Such as figure 1 As shown, a method for detecting key points in cardiac MRI images based on convolutional neural networks includes the following steps:

[0053] S1. Collect the cardiac MRI image to be detected and the training original image, and manually mark key points on the training orig...

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Abstract

The invention discloses a heart nuclear magnetic resonance image key point detection method based on a convolutional neural network, and the method comprises the steps of constructing the convolutional neural network, efficiently detecting the regions of a plurality of key points through a pixel-by-pixel classifier, predicting the distances from pixels to the centers of the key points, and guaranteeing the precise prediction of the key points. The false positive rate of detection is reduced and the accuracy of key point detection is ensured by removing false detection key points, removing leftand right ventricular intersection points, which are not between the left ventricle and the right ventricle, in the fusion information graph and obtaining the local maximum value in the fusion information graph according to the local maximum value search method.

Description

technical field [0001] This field belongs to the field of key point detection of cardiac nuclear magnetic resonance images, and in particular relates to a method for detecting key points of cardiac nuclear magnetic resonance images based on a convolutional neural network. Background technique [0002] As tomography (CT) and magnetic resonance imaging (MR) are widely used in disease diagnosis, treatment planning and clinical research, medical image computer-aided diagnosis (CAD) has become a part of the daily work of doctors in clinical diagnosis and treatment planning. An important step of the technology, and anatomical structure key point detection is an important research hotspot of this technology. Key point detection technology can help doctors quickly locate lesions, organs and other objects of interest, improving diagnostic efficiency. The key point detection algorithm has achieved good detection results in medical image processing, but it still has certain limitation...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06T7/136
CPCG06T7/136G06N3/08G06T2207/10081G06N3/045G06F18/2411G06F18/25G06F18/214
Inventor 李纯明谢李鹏
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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