Neural network model training method and system for ultrasonic displacement estimation

A neural network model and neural network technology, applied in the neural network model training method and system field of ultrasonic displacement estimation, can solve the problem of insufficient robustness and accuracy, inability to fully mine and utilize data, and inability to establish connections with other data and other issues to achieve the effect of improving utilization, enhancing accuracy and robustness

Inactive Publication Date: 2019-03-22
SHENZHEN UNIV
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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 ult

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  • Neural network model training method and system for ultrasonic displacement estimation
  • Neural network model training method and system for ultrasonic displacement estimation
  • Neural network model training method and system for ultrasonic displacement estimation

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[0040] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0041] figure 1 It shows a neural network model training method for ultrasonic displacement estimation provided by an embodiment of the present invention, including:

[0042] S101, input the target block and the search area block of the marked data into the twin neural network to be trained, wherein the twin neural network envelopes the first branch and the second branch, and the target block is input into the first a branch, the search area block input to the second branch;

[0043] S102, the Siamese neural network extracts deep semantic features of the target block and the search area block by ...

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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...

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Application Information

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IPC IPC(8): G06N3/08G06N3/04G06T7/00
CPCG06N3/084G06T7/0012G06T2207/10132G06T2207/30096G06N3/045
Inventor 陆敏华闭祖悦毛睿
Owner SHENZHEN UNIV
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