Deep learning-based ejection fraction calculation method and system

A technology of ejection fraction and deep learning, applied in the field of deep learning, can solve the problems of heart volume error, dependence, and reduced efficiency of medical staff, so as to avoid human error, improve work efficiency, and improve calculation accuracy.

A technology of ejection fraction and deep learning, applied in the field of deep learning, can solve the problems of heart volume error, dependence, and reduced efficiency of medical staff, so as to avoid human error, improve work efficiency, and improve calculation accuracy.

CN111466894AActive Publication Date: 2020-07-31上海深至信息科技有限公司
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  • Deep learning-based ejection fraction calculation method and system
  • Deep learning-based ejection fraction calculation method and system
  • Deep learning-based ejection fraction calculation method and system

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[0060] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The present invention is not limited to this embodiment, and other embodiments may also belong to the scope of the present invention as long as they conform to the gist of the present invention.

[0061] In a preferred embodiment of the present invention, based on the above-mentioned problems in the prior art, a method for calculating ejection fraction based on deep learning is now provided. First, an ultrasound device is used to continuously collect at least one complete cardiac Periodic continuous heart body data and output;

[0062] like figure 1 As shown, subsequently, the following steps are performed for each frame of cardiac volume data in the continuous cardiac volume data:

[0063] Step S1, using the pre-trained neural network segmentation model to segment the heart volume data into the left ventricle of the heart to obtain correspon...

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Abstract

The invention provides a deep learning-based ejection fraction calculation method and system, and relates to the technical field of deep learning. The method comprises the steps: carrying out heart left ventricle segmentation on heart body data through a pre-trained neural network segmentation model to obtain corresponding left ventricle segmentation marker data; performing binarization processingon the left ventricle segmentation marker data to obtain left ventricle binarization body data with first voxel values and second voxel values; counting the voxel quantity sum of the first voxel values in the left ventricle binarization body data, and storing the voxel quantity sum as the left ventricle volume corresponding to the heart body data into a pre-generated volume queue; and after all frames of heart body data of the continuous heart body data are processed, respectively extracting the maximum value and the minimum value of the left ventricular volume stored in the volume queue, andperforming calculation to obtain the ejection fraction of the heart part. The method has the beneficial effects of effectively improving the calculation accuracy and improving the working efficiencyof medical personnel.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a method and system for calculating ejection fraction based on deep learning. Background technique [0002] With the development of economy and the improvement of people's living standards, the incidence of cardiovascular disease is also increasing, which is the number one threat to national health. Although the fatality rate of cardiovascular disease is high, it is also highly preventable and curable. The key is early heart disease diagnosis, screening and prediction. Ultrasound equipment has real-time requirements and has high specificity for heart diagnosis. Among the echocardiographic indicators, ejection fraction is one of the important indicators for judging the type of heart failure. Ejection fraction refers to stroke volume as a percentage of ventricular end-diastolic volume, and the normal value is 50-70%. Examination by echocardiography is one of the im...

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Application Information

Patent Timeline
31 Jul 2020
Publication
CN111466894A
IPC
A61B5/02; A61B8/08
CPC
A61B5/02028; A61B8/0883; A61B8/5207; A61B8/5223
Inventors
朱瑞星; 黄孟钦