Neural network model training method and system for ultrasonic displacement estimation

Abstract

The invention is applicable to the field of image recognition, A neural network model training method for ultrasonic displacement estimation is provided, includes inputting target block and search block into twin neural network, The two branches of the twin neural network perform feature extraction using DenseNet with shared weights, The characteristic graph is obtained, and the convolution calculation is carried out on the characteristic graph obtained from the two branches of the twin neural network, and the scores of the obtained cross-correlation numbers are compared with the real values,and the difference results are propagated backward, so as to realize the adjustment of the weights of each layer of the twin neural network and the optimization of the network. The embodiment of the invention utilizes the depth neural network to extract the deep semantic features of the ultrasonic radio frequency data, enhances the accuracy and robustness of the displacement estimation, establishes the connection between the currently processed data and the existing other data, fully utilizes and mines the features of the existing data, and improves the utilization rate of the data.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to a neural network model training method and system for ultrasonic displacement estimation. Background technique [0002] Soft tissue lesions are often accompanied by changes in tissue stiffness. For example, breast or prostate solid tumors are firmer than normal tissue, while fluid cysts are less firm than normal tissue. Ultrasound elastography can image tissue mechanical properties (such as tissue elasticity, tissue hardness), and provide richer functional information for clinical disease diagnosis. [0003] Traditional displacement estimation algorithms for ultrasound elastography are based on block matching algorithms in the time and frequency domains of the RF data matrix. The block matching algorithm performs motion estimation based on translational motion. First, a rectangular area block is set in the current frame of the ultrasound image sequence, an...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a neural network model training method and system for ultrasonic displacement estimation. The processed data is connected with other existing data, and the data cannot be fully mined and utilized

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