A shape-guided deep residual network for automated CT lung segmentation

doi:10.1016/j.knosys.2022.108981

	A shape-guided deep residual network for automated CT lung segmentation
	Yang, Lei1,2 ; Gu, Yuge 1,2; Huo, Benyan 1,2; Liu, Yanhong1,2 ; Bian, Guibin1,3
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
	2022-08-17
卷号	250 页码:10
通讯作者	Huo, Benyan(huoby@zzu.edu.cn) ; Liu, Yanhong(liuyh@zzu.edu.cn)
摘要	Automatic lung segmentation is an effective method for the precise computer-aided diagnosis of lung diseases. However, CT lung scans are always complex due to issues such as weak texture, poor contrast, and variation of appearances and positions, which will affect the lung segmentation accuracy. Recently, due to strong feature expression ability, many deep convolution neural networks (DCNNs) have been proposed for application in medical image segmentation to provide an end-to-end segmentation scheme, especially the U-shape network (U-Net) and its variants. However, accurate lung segmentation methods based on DCNNs still face a certain challenge because of the insufficient process of boundary information, restricted receptive field, etc. To address these issues, with the encoder-decoder framework, a novel shape-guided deep residual network is proposed in this paper for automatic CT lung segmentation. The proposed network is composed of two stream networks: the main stream network and the shape stream network. An effective deep attention residual network is built to act as the mainstream network for lung segmentation. Meanwhile, an attention fusion block is proposed to embed into the mainstream network for multiscale feature extraction of local feature maps. Based on the mainstream network, a shape stream network is proposed to serve as significant guidance for the mainstream network to accurately compute lung shape boundaries. Multiple public CT lung image sets are adopted to qualitatively and quantitatively analyze the segmentation performance on CT scans. Experimental results indicate that the proposed shape-guided deep residual network outperforms related advanced image segmentation methods on medical image analysis. (C) 2022 Elsevier B.V. All rights reserved.
关键词	Deep network architecture Medical image analysis Shape stream network Residual unit Attention fusion unit
DOI	10.1016/j.knosys.2022.108981
关键词[WOS]	CHEST RADIOGRAPHS ; U-NET ; IMAGE
收录类别	SCI
语种	英语
资助项目	National Key Research & Development Project of China[2020YFB1313701] ; National Natural Science Foundation of China[62003309] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008]
项目资助者	National Key Research & Development Project of China ; National Natural Science Foundation of China ; Outstanding Foreign Scientist Support Project in Henan Province of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000833283600014
出版者	ELSEVIER
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49834
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Huo, Benyan; Liu, Yanhong
作者单位	1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Gu, Yuge,Huo, Benyan,et al. A shape-guided deep residual network for automated CT lung segmentation[J]. KNOWLEDGE-BASED SYSTEMS,2022,250:10.
APA	Yang, Lei,Gu, Yuge,Huo, Benyan,Liu, Yanhong,&Bian, Guibin.(2022).A shape-guided deep residual network for automated CT lung segmentation.KNOWLEDGE-BASED SYSTEMS,250,10.
MLA	Yang, Lei,et al."A shape-guided deep residual network for automated CT lung segmentation".KNOWLEDGE-BASED SYSTEMS 250(2022):10.