High-resolution feature based central venous catheter tip detection network in X-ray images

doi:10.1016/j.media.2023.102876

	High-resolution feature based central venous catheter tip detection network in X-ray images
	Wang, Yuhan 1; Lam, Hak Keung 1; Hou, Zeng-Guang2,3 ; Li, Rui-Qi2,3 ; Xie, Xiao-Liang2,3 ; Liu, Shi-Qi2,3
发表期刊	MEDICAL IMAGE ANALYSIS
ISSN	1361-8415
	2023-08-01
卷号	88 页码:13
通讯作者	Lam, Hak Keung(hak-keung.lam@kcl.ac.uk)
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.
关键词	Catheter tip X-ray based detection Position detection Convolutional Neural Network (CNN)
DOI	10.1016/j.media.2023.102876
关键词[WOS]	TRACKING ; POSITION
收录类别	SCI
语种	英语
资助项目	King's College London, United Kingdom ; EPSRC Tier-2 capital grant[EP/P020259/1] ; China Scholarship Council
项目资助者	King's College London, United Kingdom ; EPSRC Tier-2 capital grant ; China Scholarship Council
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001037518600001
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/173211/53835
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.Kings Coll London, Dept Engn, London WC2R 2LS, England 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 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.