基于注意力机制和Inf-Net的新冠肺炎图像分割方法

doi:10.16180/j.cnki.issn1007-7820.2023.02.004

Abstract

Abstract:

COVID-19 pandemic has recently ravaged the world, seriously affecting the life and health of human society. CT imaging technology is an important diagnostic method for detecting COVID-19. Automatic and accurate segmentation of the lesion is of great significance for diagnosis, treatment and prognosis. In view of the segmentation of new coronary pneumonia lesions, an improved automatic segmentation method based on the Inf-Net algorithm is proposed, which introduces the channel attention module to improve feature representation and attention gate model to better fuse edge information. The experimental results on COVID-19 CT segmentation datasets show that the Dice similarity coefficient, sensitivity and specificity of the proposed method are 75.1%, 75.4% and 95.4%, respectively, and the algorithm performance is also better than some mainstream methods.

Key words: COVID-19, computerized tomography image, medical image segmentation, semantic segmentation, attention mechanism, Inf-Net network, convolutional neural network, deep learning

CLC Number:

TP391

ZUO Bin,LI Feifei. An Effective Segmentation Method for COVID-19 CT Image Based on Attention Mechanism and Inf-Net[J].Electronic Science and Technology, 2023, 36(2): 22-28.

Figures/Tables 9

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Table 1.

Comparison of experiments results"

网络	Dice	灵敏度	特异率	S_α	$E φ m e a n$	MAE
U-Net^[14]	0.439	0.534	0.858	0.622	0.625	0.186
Attention U-Net^[7]	0.583	0.637	0.921	0.744	0.739	0.112
Gated-UNet^[23]	0.623	0.658	0.926	0.725	0.814	0.102
Dense-UNet^[24]	0.515	0.594	0.840	0.655	0.662	0.184
U-Net++^[15]	0.581	0.672	0.902	0.722	0.720	0.120
Inf-Net^[6]	0.682	0.692	0.943	0.781	0.838	0.082
mInf-Net (Ours)	0.690	0.740	0.923	0.771	0.842	0.092
Semi-mInf-Net (Ours)	0.751	0.754	0.954	0.799	0.904	0.064

Table 1.

Figure 7.

Table 2.

Ablation studies of the proposed method"

网络	Dice	灵敏度	特异率	S_α	$E φ m e a n$	MAE
Backbone	0.739	0.725	0.960	0.800	0.894	0.064
Backbone+CAM	0.742	0.727	0.959	0.790	0.897	0.064
Backbone+AG	0.750	0.758	0.953	0.798	0.904	0.064
Backbone+CAM+AG	0.751	0.754	0.954	0.799	0.904	0.064

Table 2.

References 24

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An Effective Segmentation Method for COVID-19 CT Image Based on Attention Mechanism and Inf-Net

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