Method for generating medical ultrasonic image data based on adversarial network

An ultrasound image, network generation technology, applied in the field of deep learning and medical imaging, to achieve the effect of scientific and reasonable design, improved model adaptability, and reduced false negative rate

Pending Publication Date: 2020-09-29
TIANJIN UNIV
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However, these conventional methods still have many limitations

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  • Method for generating medical ultrasonic image data based on adversarial network
  • Method for generating medical ultrasonic image data based on adversarial network
  • Method for generating medical ultrasonic image data based on adversarial network

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Embodiment Construction

[0044] The present invention will be described in further detail below through specific examples. The following examples are only descriptive and not restrictive, and cannot limit the protection scope of the present invention.

[0045] A method for generating medical ultrasound image data based on a confrontation network, characterized in that: the steps of the method are:

[0046] S1, preprocess the data:

[0047] a. Divide the image, extract the nodules, save it as an image, and reconstruct the data set;

[0048] b. Take 300 ultrasound images of benign nodules and 300 malignant nodules respectively for image segmentation to obtain a new ROI region image;

[0049] c. Adjust the image resolution to 112*112, and divide the 600 images in the data set into a training set of 400 images and a test set of 200 images;

[0050] S2. Use WGAN to enhance the data in the ROI area of ​​thyroid ultrasound images, and evaluate the generated images under the guidance of the doctor: a. Add labels to the ...

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Abstract

The invention relates to a method for generating medical ultrasonic image data based on an adversarial network, and the method comprises the steps: enabling the adversarial network to be applied to anROI (region of interest) data set of thyroid ultrasonic data, and proving that a WGAN (generative adversarial network) can generate an ultrasonic image with better quality; expanding the benign thyroid ultrasonic data ROI data set and the malignant thyroid ultrasonic data ROI data set by using the WGAN in the data set; and comparing the performance of the VGG-16 network on the enhanced data set and the original data set, and proving the feasibility of performing thyroid ultrasound data set data enhancement through the generative adversarial network. The method is scientific and reasonable indesign, generates medical ultrasonic image data through the generative adversarial network, enriches a data set, and improves the adaptability of the model and the identification capability of a target.

Description

Technical field [0001] The invention belongs to the field of deep learning and medical imaging, and relates to common knowledge and common processing methods of medical imaging and GAN related theoretical methods of deep learning, and particularly relates to a method for generating medical ultrasound imaging data based on a confrontation network. Background technique [0002] Generative Adversarial Network (GAN) was proposed by Goodfellow in 2014. Because GAN can fully fit the data distribution of the training set to generate realistic natural images, its powerful performance immediately triggered a large number of deep learning researchers to invest in this field. [0003] At present, the application field of GAN is not only limited to image generation, it also exhibits surprising effects in image segmentation, image annotation, and image super-resolution. Due to the superiority of the generative adversarial network, in recent years, many scholars have introduced it into medical ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G16H30/20G06N3/04G06N3/08
CPCG06T7/0012G16H30/20G06N3/08G06T2207/20081G06T2207/20104G06N3/045
Inventor 李雪威马金鸣于瑞国于健刘志强高洁周琨
Owner TIANJIN UNIV
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