Deep recurrent neural network-based cardiac function automatic analysis method

Abstract

The invention relates to a deep recurrent neural network-based cardiac function automatic analysis method and belongs to the technical field of medical image analysis. The method includes the following steps that: S1, a cardiac nuclear magnetic resonance film is acquired, and the cardiac nuclear magnetic resonance film is pre-processed; S2, a recurrent neural network model of multi-task learning is constructed, and underlying general image features are extracted; S3, the extracted underlying general image features are inputted into the two-layer long- and short-memory recurrent neural network,space-time dependence relations are constructed; S4, a target loss function is constructed; S5, and parameters in the recurrent neural network are trained and optimized through a stochastic gradientdescent method according to the loss function constructed in step the S4; and S6, after the training of the recurrent neural network model is completed, the pre-processed cardiac nuclear magnetic resonance film is inputted into the trained recurrent neural network, and thirteen parameters in cardiac function analysis are measured. With the method of the invention adopted, the manual delineation ofventricular structures is not required, and end-to-end cardiac function analysis can be automatically performed.

Description

technical field [0001] The invention belongs to the technical field of medical image analysis, and relates to an end-to-end automatic cardiac function assessment method and system Background technique [0002] Accurately analyzing the patient's cardiac function status and diagnosing heart disease as early as possible are of great significance to improving the treatment effect of the disease and reducing medical costs. Among the many imaging methods, Cardiac Magnetic Resonance (CMR) has the highest soft tissue contrast, and it can simultaneously monitor the anatomical structure, motor function, and tissue characteristic changes of the heart through multi-parameter, multi-plane, and multi-sequence imaging. One-stop" observation, thus becoming the gold standard for cardiac function evaluation. Although CMR examination has unique value in the diagnosis of heart disease, risk stratification and prognosis judgment, it also has the following shortcomings: 1) CMR sequences are many...

AI Technical Summary

Problems solved by technology

Although CMR examination has unique value in the diagnosis of heart disease, risk stratification and prognosis judgment, it also has the following shortcomings: 1) CMR sequences are many, fusion is difficult, and data processing is very time-consuming; 2) There is a shortage of high-level clinical imaging diagnosticians Big

These two factors seriously restrict its clinical application

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