Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism
文献类型:期刊论文
作者 | Cao FD(曹飞道)1,2,3,4![]() ![]() |
刊名 | SYMMETRY-BASEL
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出版日期 | 2021 |
卷号 | 13期号:5页码:1-15 |
关键词 | chest X-ray images U-Net variational auto-encoder three-terminal attention mechanism lung segmentation |
ISSN号 | 2073-8994 |
产权排序 | 1 |
英文摘要 | Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the screening and diagnosis of related diseases. There are many opacities in the lungs in the CXRs of patients, which makes the lungs difficult to segment. In order to solve this problem, this paper proposes a segmentation algorithm based on U-Net. This article introduces variational auto-encoder (VAE) in each layer of the decoder-encoder. VAE can extract high-level semantic information, such as the symmetrical relationship between the left and right thoraxes in most cases. The fusion of the features of VAE and the features of convolution can improve the ability of the network to extract features. This paper proposes a three-terminal attention mechanism. The attention mechanism uses the channel and spatial attention module to automatically highlight the target area and improve the performance of lung segmentation. At the same time, the three-terminal attention mechanism uses the advanced semantics of high-scale features to improve the positioning and recognition capabilities of the attention mechanism, suppress background noise, and highlight target features. Experimental results on two different datasets show that the accuracy (ACC), recall (R), F1-Score and Jaccard values of the algorithm proposed in this paper are the highest on the two datasets, indicating that the algorithm in this paper is better than other state-of-the-art algorithms. |
WOS关键词 | COMPUTER-AIDED DIAGNOSIS ; RADIOGRAPHS ; TUBERCULOSIS ; NETWORK |
资助项目 | National Natural Science Foundation of China[62041302] |
WOS研究方向 | Science & Technology - Other Topics |
语种 | 英语 |
WOS记录号 | WOS:000654572500001 |
资助机构 | National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [62041302] |
源URL | [http://ir.sia.cn/handle/173321/28928] ![]() |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Zhao HC(赵怀慈) |
作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, Liaoning, China 2.Key Laboratory of Opto-Electronic Information Process, Shenyang 110016, Liaoning, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, Liaoning, China |
推荐引用方式 GB/T 7714 | Cao FD,Zhao HC. Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism[J]. SYMMETRY-BASEL,2021,13(5):1-15. |
APA | Cao FD,&Zhao HC.(2021).Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism.SYMMETRY-BASEL,13(5),1-15. |
MLA | Cao FD,et al."Automatic Lung Segmentation Algorithm on Chest X-ray Images Based on Fusion Variational Auto-Encoder and Three-Terminal Attention Mechanism".SYMMETRY-BASEL 13.5(2021):1-15. |
入库方式: OAI收割
来源:沈阳自动化研究所
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