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Liver tumor segmentation method and system based on convolutional neural network

A convolutional neural network, liver tumor technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of large amount of calculation, slow operation, cumbersome operation, etc., and achieve enhanced robustness and high segmentation accuracy. , the effect of simple operation

Active Publication Date: 2020-09-04
XIAN UNIV OF TECH
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Problems solved by technology

Although the accuracy can be improved, the structure of the two networks has the problems of generating a large amount of calculation, cumbersome operations, and slow operation speed

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  • Liver tumor segmentation method and system based on convolutional neural network
  • Liver tumor segmentation method and system based on convolutional neural network
  • Liver tumor segmentation method and system based on convolutional neural network

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Embodiment Construction

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0057] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0058] figure 1 It is a schematic flowchart of the convolutional neural network-based liver tumor segmentation method of the present invention. Such as figure 1 As shown, the liver tumor segmentation method base...

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Abstract

The invention relates to a liver tumor segmentation method and system based on a convolutional neural network. The method comprises the following steps: constructing a U-shaped structure encoder / decoder network; wherein the encoder / decoder network with the U-shaped structure comprises a contraction path and an expansion path; residual blocks are added into convolution layers of down-sampling and up-sampling of the U-shaped structure encoder / decoder network to form a jump connection structure; adding a weight attention mechanism module into down-sampling of the U-shaped structure encoder / decoder network, and combining the weight attention mechanism module with the jump connection structure to form a convolutional neural network model; training the convolutional neural network model by adopting a data training set to obtain a trained convolutional neural network model; and inputting a to-be-segmented liver image into the trained convolutional neural network model to obtain a liver segmentation result image and a tumor segmentation result image. According to the invention, the tumor segmentation accuracy can be improved.

Description

technical field [0001] The present invention relates to the field of medical image processing, in particular to a convolutional neural network-based liver tumor segmentation method and system. Background technique [0002] Liver cancer is a malignant tumor with a high mortality rate. Early detection and treatment will significantly improve the survival rate of liver cancer patients. Related literature shows that the five-year relative survival rate of advanced liver cancer is only 18%, while the five-year relative survival rate of early-diagnosed liver cancer increases to 70%. Often, early liver cancer shows up as a tumor on a CT (computed tomography) scan that is on or inside the liver. Accurate liver tumor segmentation is an essential prerequisite step for computer-aided diagnosis of liver diseases, however, due to the large number of image slices present in CT scans, diagnostic readout of CT scans in a short period of time is critical for radiologists. Language is a hug...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0012G06T7/10G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30056
Inventor 石争浩薛世梁
Owner XIAN UNIV OF TECH
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