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Lung X-ray image segmentation method and system, computer equipment and storage medium

A light image and lung technology, applied in image analysis, calculation, image enhancement, etc., can solve the problems of not considering the importance, not enough data enhancement, not enough training samples, etc., to enhance feature extraction and expression ability, improve Segmentation accuracy, the effect of improving accuracy

Active Publication Date: 2021-04-13
SOUTH CHINA AGRI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Numerical results show that this model works well in lung segmentation and has certain application value, but the model does not consider the importance of feature maps of different channels, and the importance of different spatial positions of the same channel on the segmentation results. Convolution The size of the kernel is fixed, resulting in the same receptive field of the same layer of the model, and insufficient data enhancement, resulting in insufficient training samples, resulting in the following shortcomings: 1) Lack of segmentation of the edge of the lungs, such as segmentation There are holes in some parts of the contour; 2) The effect of the method is not ideal for fine structures such as the tiny blood vessels in the lungs, and some fine structures are not segmented

Method used

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  • Lung X-ray image segmentation method and system, computer equipment and storage medium
  • Lung X-ray image segmentation method and system, computer equipment and storage medium
  • Lung X-ray image segmentation method and system, computer equipment and storage medium

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Experimental program
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Embodiment 1

[0065] Such as Figure 3 ~ Figure 5 As shown, the present embodiment provides a lung X-ray image segmentation method, the method includes the following steps:

[0066] S301. Acquire a lung X-ray image data set.

[0067] Specifically, the sample data in the lung X-ray image data set can be obtained through collection, for example, by collecting X-ray images of the lungs through an X-ray machine, or can be obtained from a database search, such as storing the X-ray images of the lungs in the database in advance , which can be obtained by searching the lung X-ray image from the database.

[0068] S302. Perform preprocessing on the lung X-ray image data set to obtain a training set.

[0069] Specifically, the sample data in the lung X-ray image dataset is preprocessed by screening, image denoising, and size cropping, and the preprocessed lung X-ray image dataset is divided into a training set and a test set, with a ratio of 5 :2.

[0070] Further, after step S302, it may also i...

Embodiment 2

[0103] Such as Figure 9 As shown, this embodiment provides a lung X-ray image segmentation system, the system includes an acquisition unit 901, a preprocessing unit 902, an enhancement unit 903, a training unit 904 and a segmentation unit 905, and the specific functions of each unit are as follows:

[0104] The obtaining unit 901 is configured to obtain a lung X-ray image data set.

[0105] The preprocessing unit 902 is configured to preprocess the lung X-ray image data set to obtain a training set.

[0106] The enhancement unit 903 is used to perform random scaling, random position cropping, random horizontal / vertical flipping, random angle rotation, and random brightness / saturation / contrast changes on the training data in the training set to obtain an enhanced training set .

[0107] The training unit 904 is used to input the training set into the RIAMU-Net model for training to obtain the trained RIAMU-Net model; wherein, the RIAMU-Net model is based on the U-Net model, ...

Embodiment 3

[0111] Such as Figure 10 As shown, this embodiment provides a computer device, which may be a server, a computer, etc., and includes a processor 1002 connected through a system bus 1001 , a memory, an input device 1003 , a display 1004 and a network interface 1005 . Wherein, the processor 1002 is used to provide calculation and control capabilities, and the memory includes a non-volatile storage medium 1006 and an internal memory 1007, the non-volatile storage medium 1006 stores an operating system, a computer program and a database, and the internal memory 1007 is The operating system in the non-volatile storage medium 1006 and the operation of the computer program provide an environment. When the computer program is executed by the processor 1002, the reacquisition video detection method of the above-mentioned embodiment 1 is implemented, as follows:

[0112] Obtain a lung X-ray image dataset;

[0113] Preprocessing the lung X-ray image data set to obtain a training set; ...

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Abstract

The invention discloses a lung X-ray image segmentation method and system, computer equipment and a storage medium. The method comprises the following steps: acquiring a lung X-ray image data set; preprocessing the lung X-ray image data set to obtain a training set; inputting the training set into an RIAMU-Net model to be trained to obtain a trained RIAMU-Net model, wherein the RIAMU-Net model is based on the U-Net model, each layer of the encoder comprises a Res-inception module, and each layer of the decoder comprises an attention mechanism module and a Res-inception module; and segmenting the lung X-ray image data to be segmented by using the trained RIAMU-Net model to obtain a segmented image. Based on the U-Net model, the structure of the model is improved, so that the features of the X-ray image can be better extracted, the lung image can be more accurately segmented, and the lung edge segmentation effect is improved.

Description

technical field [0001] The invention relates to a lung X-ray image segmentation method, system, computer equipment and storage medium, belonging to the field of lung X-ray image segmentation. Background technique [0002] Medical images are an important basis for the diagnosis of many diseases. Therefore, in the process of disease diagnosis, many medical images are produced, which brings a data basis for the research of medical image segmentation algorithms. The traditional image segmentation method uses one or several artificially selected features in the image, resulting in the traditional method often having low accuracy when segmenting images whose features are not obvious. Image segmentation plays an important role in medical imaging and many other applications. The goal of the segmentation process is to define certain properties in the image that can be used to make the parts of the segmented image less different. The definition of these properties should satisfy a ge...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06T7/11G06N3/08G06T2207/10116G06T2207/20081G06T2207/20132G06T2207/20084G06T2207/30061G06N3/045Y02T10/40
Inventor 李西明徐康郭玉彬杜治国温嘉勇陈志浩王璇
Owner SOUTH CHINA AGRI UNIV