High-resolution feature based central venous catheter tip detection network in X-ray images
文献类型：期刊论文
| 作者 | Wang, Yuhan2; Lam, Hak Keung2; Hou, Zeng-Guang1,3 ; Li, Rui-Qi1,3; Xie, Xiao-Liang1,3; Liu, Shi-Qi1,3
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| 刊名 | MEDICAL IMAGE ANALYSIS
 |
| 出版日期 | 2023-08-01 |
| 卷号 | 88页码:13 |
| ISSN号 | 1361-8415 |
| 关键词 | Catheter tip X-ray based detection Position detection Convolutional Neural Network (CNN) |
| DOI | 10.1016/j.media.2023.102876 |
| 通讯作者 | Lam, Hak Keung(hak-keung.lam@kcl.ac.uk) |
| 英文摘要 | Hospital patients can have catheters and lines inserted during the course of their admission to give medicines for the treatment of medical issues, especially the central venous catheter (CVC). However, malposition of CVC will lead to many complications, even death. Clinicians always detect the malposition based on position detection of CVC tip via X-ray images. To reduce the workload of the clinicians and the percentage of malposition occurrence, we propose an automatic catheter tip detection framework based on a convolutional neural network (CNN). The proposed framework contains three essential components which are modified HRNet, segmentation supervision module, and deconvolution module. The modified HRNet can retain high resolution features from start to end, ensuring the maintenance of precise information from the X-ray images. The segmentation supervision module can alleviate the presence of other line-like structures such as the skeleton as well as other tubes and catheters used for treatment. In addition, the deconvolution module can further increase the feature resolution on the top of the highest-resolution feature maps in the modified HRNet to get a higher-resolution heatmap of the catheter tip. A public CVC Dataset is utilized to evaluate the performance of the proposed framework. The results show that the proposed algorithm offering a mean Pixel Error of 4.11 outperforms three comparative methods (Ma's method, SRPE method, and LCM method). It is demonstrated to be a promising solution to precisely detect the tip position of the catheter in X-ray images. |
| WOS关键词 | TRACKING ; POSITION |
| 资助项目 | King's College London, United Kingdom ; EPSRC Tier-2 capital grant[EP/P020259/1] ; China Scholarship Council |
| WOS研究方向 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001037518600001 |
| 资助机构 | King's College London, United Kingdom ; EPSRC Tier-2 capital grant ; China Scholarship Council |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53834]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Lam, Hak Keung |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Kings Coll London, Dept Engn, London WC2R 2LS, England 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yuhan,Lam, Hak Keung,Hou, Zeng-Guang,et al. High-resolution feature based central venous catheter tip detection network in X-ray images[J]. MEDICAL IMAGE ANALYSIS,2023,88:13. |
| APA | Wang, Yuhan,Lam, Hak Keung,Hou, Zeng-Guang,Li, Rui-Qi,Xie, Xiao-Liang,&Liu, Shi-Qi.(2023).High-resolution feature based central venous catheter tip detection network in X-ray images.MEDICAL IMAGE ANALYSIS,88,13. |
| MLA | Wang, Yuhan,et al."High-resolution feature based central venous catheter tip detection network in X-ray images".MEDICAL IMAGE ANALYSIS 88(2023):13. |
入库方式： OAI收割
来源：自动化研究所
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