Knowledge Commons of Institute of Automation,CAS
A shape-guided deep residual network for automated CT lung segmentation | |
Yang, Lei1,2; Gu, Yuge1,2; Huo, Benyan1,2; 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 | |
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. |
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