SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images

doi:10.1016/j.patcog.2021.108109

	SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images
	Zhao, Shixuan 1; Li, Zhidan 1; Chen, Yang 2; Zhao, Wei 3; Xie, Xingzhi 3; Liu, Jun 3,4; Zhao, Di 5; Li, Yongjie 1
	2021-11-01
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
卷号	119 页码:12
摘要	Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial-and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability. (c) 2021 Elsevier Ltd. All rights reserved.
关键词	COVID-19 Convolutional neural network Segmentation Lung opacification Attention mechanism
DOI	10.1016/j.patcog.2021.108109
收录类别	SCI
语种	英语
资助项目	Key Area R&D Program of Guangdong province[2018B03033801] ; National Key Research and Development Project[2018ZX10723203-001-002] ; Key Emergency Project of Pneumonia Epidemic of novel coronavirus infection[2020SK3006] ; Emergency Project of Prevention and Control for COVID-19 of Central South University[160260005] ; Changsha Scientific and Technical bureau, China[kq2001001]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000687338900005
出版者	ELSEVIER SCI LTD
引用统计	被引频次：33[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17129
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Jun; Zhao, Di; Li, Yongjie
作者单位	1.Univ Elect Sci & Technol China, Sch Life Sci & Technol, MOE Key Lab Neuroinformat, Chengdu, Peoples R China 2.Sichuan Univ, West China Hosp, West China Biomed Big Data Ctr, Chengdu, Peoples R China 3.Cent South Univ, Xiangya Hosp 2, Dept Radiol, 139 Middle Renmin Rd, Changsha, Hunan, Peoples R China 4.Qual Control Ctr, Dept Radiol, Changsha, Hunan, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Shixuan,Li, Zhidan,Chen, Yang,et al. SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images[J]. PATTERN RECOGNITION,2021,119:12.
APA	Zhao, Shixuan.,Li, Zhidan.,Chen, Yang.,Zhao, Wei.,Xie, Xingzhi.,...&Li, Yongjie.(2021).SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images.PATTERN RECOGNITION,119,12.
MLA	Zhao, Shixuan,et al."SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images".PATTERN RECOGNITION 119(2021):12.