Honeycomb lung focus segmentation method based on SAA-Unet network

A honeycomb lung and network technology, applied in the field of image processing, can solve the problems of complex texture, large deformation, irregular shape, etc., and achieve the effect of accurate segmentation

Pending Publication Date: 2022-03-11
TAIYUAN UNIV OF TECH
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Problems solved by technology

At the same time, due to the characteristics of irregular shape, uneven gray scale, complex texture, and large deformation, the existing deep learning-based segmentation methods, especially the convolutional neural network, are difficult to detect in the image of honeycomb lung. Segmentation is less accurate
[0003] At present, the evaluation of honeycomb lung ma

Method used

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  • Honeycomb lung focus segmentation method based on SAA-Unet network
  • Honeycomb lung focus segmentation method based on SAA-Unet network
  • Honeycomb lung focus segmentation method based on SAA-Unet network

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[0029] Such as Figure 1 to 3 As shown, the segmentation method based on the SAA-Unet network based on the SAA-UNET network is specifically performed in accordance with the following steps:

[0030] Step S1: Gets the CT video data of the honeycomb patients in different age groups, performs binarization, feature labeling, etc., and performs pretreatment operations such as image enhancement, and achieves the expansion of data sets;

[0031] Step S2: Training set, test set and verification set by preset ratio, to fully verify the generalization of the model;

[0032] Step S3: Build the underlying U-NET network, replace the SoftMax activation function in the last layer of the network uses 1 × 1 convolution and SigmoID activation function;

[0033] Step S4: Improved the construction of the underlying U-NET network to obtain an improved U-NET network based on division attention and attention mechanism, using the division of attention modules in the encoder phase to extract the deep layer...

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Abstract

The invention discloses a honeycomb lung focus segmentation method based on an SAA-Unet network, and belongs to the technical field of image processing. The technical problem to be solved is to provide the improvement of the honeycomb lung focus segmentation method based on the SAA-Unet network. According to the technical scheme for solving the technical problem, U-Net is used as a basic network, feature information in a honeycomb lung lesion part is deeply excavated, the generalization ability of a main task is improved, and honeycomb lung lesion features are extracted more accurately; meanwhile, in order to improve the model segmentation accuracy, the problem of feature loss of the image in the convolution and deconvolution processes is solved by using a division attention module, and finally, the weight value of an important lesion region is improved by fusing high-level and low-level feature information by using an attention mechanism, so that the segmentation accuracy of the network model on the honeycomb lung lesion region is realized; the method is applied to honeycomb lung focus segmentation.

Description

technical field [0001] The invention discloses a method for segmenting cellular lung lesions based on a SAA-Unet network, belonging to the technical field of image processing. Background technique [0002] Cellular lung disease is a common disease that seriously threatens human health. It has the characteristics of long course of disease, high fatality rate, poor prognosis, and low survival rate, which leads to low survival rate and cure rate of patients with honeycomb lung disease. With the continuous development of computer computing power, computer-aided medical treatment has been accepted by medical workers for its accuracy and convenience, but how to accurately segment honeycomb lung from CT slices has become an important and difficult point. At the same time, due to the characteristics of irregular shape, uneven gray scale, complex texture, and large deformation, the existing deep learning-based segmentation methods, especially the convolutional neural network, are dif...

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Application Information

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IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06T7/11G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30061G06T2207/30096G06T2207/20132G06N3/045
Inventor 李钢张玲张海轩卫建建李鹏博李宇
Owner TAIYUAN UNIV OF TECH
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