基于三重注意力机制的新冠肺炎病灶分割模型

doi:10.3969/j.issn.0255-8297.2022.01.010

Abstract

Abstract: 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.

Key words: deep learning, corona virus disease 2019 (COVID-19), lesion segmentation, triple attention mechanism (TAM), conditional generative adversarial network

CLC Number:

TP391.41

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.

References

[1] Zhang Z, Romero A, Muckley M J, et al. Reducing uncertainty in undersampled MRI reconstruction with active acquisition[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:2049-2058.
[2] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2014:580-587.
[3] Ronneberger O, Fischer P, Brox T. U-Net:convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and ComputerAssisted Intervention. Cham:Springer, 2015:234-241.
[4] Khened M, Kollerathu V A, Krishnamurthi G. Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers[J]. Medical Image Analysis, 2019, 51:21-45.
[5] Kang H, Xia L, Yan F, et al. Diagnosis of coronavirus disease 2019(COVID-19) with structured latent multi-view representation learning[J]. IEEE Transactions on Medical Imaging, 2020, 39(8):2606-2614.
[6] Chen J, Wu L, Zhang J, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography[J]. Scientific Reports, 2020, 10(1):1-11.
[7] Zhou Z, Siddiquee M M R, Tajbakhsh N, et al. UNet++:a nested U-Net architecture for medical image segmentation[C]//The 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018 and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018 Held in Conjunction with MICCAI 2018.[S.l.]:Springer Verlag, 2018:3-11.
[8] Zheng B, Liu Y, Zhu Y, et al. MSD-Net:multi-scale discriminative network for COVID-19 lung infection segmentation on CT[J]. IEEE Access, 2020, 8:185786-185795.
[9] Yang Q, Li Y, Zhang M, et al. Automatic segmentation of COVID-19 CT images using improved MultiResUNet[C]//2020 Chinese Automation Congress, 2020:1614-1618.
[10] Wu Y H, Gao S H, Mei J, et al. JCS:an explainable COVID-19 diagnosis system by joint classification and segmentation[J]. IEEE Transactions on Image Processing, 2021, 30:3113-3126.
[11] Fan D P, Zhou T, Ji G P, et al. Inf-Net:automatic COVID-19 lung infection segmentation from ct images[J]. IEEE Transactions on Medical Imaging, 2020, 39(8):2626-2637.
[12] Ouyang X, Huo J, Xia L, et al. Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia[J]. IEEE Transactions on Medical Imaging, 2020, 39(8):2595-2605.
[13] Xu X, Jiang X, Ma C, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia[J]. Engineering, 2020, 6(10):1122-1129.
[14] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016:770-778.
[15] Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2017:4700-4708.
[16] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[J/OL]. (2016-04-30)[2021-06-25]. https://arxiv.org/abs/1511.07122v2.
[17] Woo S, Park J, Lee J Y, et al. CBAM:convolutional block attention module[C]//Proceedings of European Conference on Computer Vision, 2018:3-19.
[18] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916.
[19] Mirza M, Osindero S. Conditional generative adversarial nets[J/OL]. (2014-11-06)[2021-06-25]. https://arxiv.org/abs/1411.1784.
[20] Yang X, He X, Zhao J, et al. COVID-CT-dataset:a CT scan dataset about COVID-19[J/OL]. (2020-06-17)[2021-06-25]. https://arxiv.org/abs/2003.13865.

Segmentation Model of COVID-19 Lesions Based on Triple Attention Mechanism

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 13

Recommended Articles

Metrics

Comments

[1]	ZHU Li, YANG Qing, WU Tao, LI Chen, LI Ming. Emotional Analysis of Brain Waves Based on CNN and Bi-LSTM [J]. Journal of Applied Sciences, 2022, 40(1): 1-12.
[2]	KANG Huixian, YI Biao, WU Hanzhou. Recent Advances in Text Steganography and Steganalysis [J]. Journal of Applied Sciences, 2021, 39(6): 923-938.
[3]	OU Qiaofeng, XIAO Jiabing, XIE Qunqun, XIONG Bangshu. Multi-target Detection and Recognition for Vehicle Inspection Images Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(6): 939-951.
[4]	LI Wenju, HE Maoxian, ZHANG Yaoxing, CHEN Huiling, LI Peigang. Crack Detection of Track Slab Based on Convolutional Neural Network and Voting Mechanism [J]. Journal of Applied Sciences, 2021, 39(4): 627-640.
[5]	GUO Yubo, LU Jun, DUAN Pengqi. Automatic Classification of Bamboo Flute Playing Skills Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(4): 685-694.
[6]	HAO Yan, SHI Huiyu, HUO Shoujun, HAN Dan, CAO Rui. Emotion Classification Based on EEG Deep Learning [J]. Journal of Applied Sciences, 2021, 39(3): 347-346.
[7]	DU Chengze, DUAN Youxiang, SUN Qifeng. Seismic Fault Identification Method Based on ResUNet and Dense CRF Model [J]. Journal of Applied Sciences, 2021, 39(3): 367-366.
[8]	WANG Wanguo, MU Shiyou, LIU Yue, LIU Guangxiu, LANG Fenling. Research on Insulator Self Exploding Detection in UAV Inspection Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(2): 222-231.
[9]	LIU Zhiyu, ZHANG Shufen, LIU Yang, LUO Changyin, LI Min. Data Augmentation Method Based on Image Gradient [J]. Journal of Applied Sciences, 2021, 39(2): 302-311.
[10]	WANG Canjun, LIAO Xin, CHEN Jiaxin, QIN Zheng, LIU Xuchong. Research on Facial Modification Detection Algorithm Based on Convolutional Neural Network [J]. Journal of Applied Sciences, 2019, 37(5): 618-630.
[11]	ZHU Yiming, CHEN Fan, HE Hongjie, CHEN Hongyou. Orthogonal GAN Information Hiding Model Based on Secret Information Driven [J]. Journal of Applied Sciences, 2019, 37(5): 721-732.
[12]	LI Fang, ZHONG Yingchun, QI Jian, LUO Peng. Automatic Extraction of Regions of Peripheral Nerve Internal Fascicular Groups from MicroCT Images [J]. Journal of Applied Sciences, 2019, 37(3): 359-368.
[13]	YANG Bin, ZHANG Tao, CHEN Xian-yi. Local Blur Detection of Digital Images Based on Deep Learning [J]. Journal of Applied Sciences, 2018, 36(2): 321-330.