Bladder ultrasonic image segmentation method and device based on UNet convolutional neural network

A convolutional neural network, ultrasound image technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problems of inaccurate segmentation boundaries and difficult bladder ultrasound image segmentation, and achieve the effect of improving accuracy

Pending Publication Date: 2020-07-17
深圳市嘉骏实业有限公司
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the difficulty in segmenting bladder ultrasound images due to noise interference, and the technical problem of inaccurate segmentation boundaries

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  • Bladder ultrasonic image segmentation method and device based on UNet convolutional neural network

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Embodiment Construction

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0042] It should be understood that when used in this specification and the appended claims, the terms "including" and "including" indicate the existence of the described features, wholes, steps, operations, elements and / or components, but do not exclude one or The existence or addition of multiple other features, wholes, steps, operations, elements, components, and / or collections thereof.

[0043] It should also be understood that the terms used in this...

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Abstract

The invention provides a bladder ultrasonic image segmentation method and device based on a UNet convolutional neural network, and the method comprises the steps: obtaining a bladder ultrasonic imageof a human body through ultrasonic scanning equipment; constructing a training set and a test set of the bladder ultrasonic images, wherein the training set and the test set comprise all bladder ultrasonic image data subjected to labeling and data enhancement processing; constructing a UNet convolutional neural network model, wherein the UNet convolutional neural network model comprises a lower sampling layer and an upper sampling layer; training the constructed UNet network model by using the training set image data of the bladder ultrasonic image to generate a network model, and testing themodel effect by using the test set image data of the bladder ultrasonic image; and segmenting the actual bladder ultrasonic image acquired by the ultrasonic equipment by using the trained UNet networkmodel. According to the invention, the interference of noise on bladder ultrasonic image segmentation can be eliminated, and the accuracy of bladder volume calculation is improved.

Description

Technical field [0001] The invention relates to a bladder ultrasound image segmentation method and device, in particular to a bladder ultrasound image segmentation method and device based on UNet convolutional neural network. Background technique [0002] Image segmentation plays an important role in imaging diagnosis. Automatic segmentation can help doctors determine the size and volume of organs or lesions, and can quantitatively evaluate the effects before and after treatment. In addition, the identification of organs and lesions is also a daily task of imaging doctors. If they are all manually segmented, it will bring heavy workload to the doctor. [0003] The bladder is a muscular cystic organ that stores urine in the human body. The volume of the bladder reflects the amount of urine that the human body can store, and it is also an important parameter for clinical applications in urology. In the diagnosis and treatment of the urinary system, the measurement results of the hum...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0012G06T7/10G06T2207/20081G06T2207/20084G06T2207/10132G06T2207/30004
Inventor 徐文龙张官喜
Owner 深圳市嘉骏实业有限公司
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