In order to solve the problem of low intensity contrast between infected areas and normal tissues, A corona virus disease 2019 (COVID-19) segmentation model TMNet is proposed based on triple attention mechanism (TAM), and applied to conditional generative adversarial network in this paper. The MultiConv module in TM-Net can automatically extract rich features of infected areas in lung slices. These features contain different types of lesion information. The designed TAM, which integrates spatial, channel and positional attention modules, can accurately locate lesions in the infected area. By composing of three types of loss functions, the loss function of TM-Net can minimize the differences between prediction graphs and real labels, thus optimizing the TM-Net. Experiment and evaluations conducted on COVID-19 data sets show that the average dice similarity coefficient (DSC) of ground glass opacities (GGO) and consolidation of TM-Net are 1.4% and 0.5% higher than the results of attention U-Net and R2U-Net, respectively, proving the accuracy improvement of TM-Net in COVID-19 lesions segmentation.
LEI Qianhui, PAN Lili, SHAO Weizhi, HU Haipeng, HUANG Yao
. Segmentation Model of COVID-19 Lesions Based on Triple Attention Mechanism[J]. Journal of Applied Sciences, 2022
, 40(1)
: 105
-115
.
DOI: 10.3969/j.issn.0255-8297.2022.01.010
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