Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy

doi:10.1016/j.compmedimag.2020.101734

	Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy
	Zhou, Yan-Jie1,3 ; Xie, Xiao-Liang1,3 ; Zhou, Xiao-Hu1 ; Liu, Shi-Qi1 ; Bian, Gui-Bin1 ; Hou, Zeng-Guang1,2,3
发表期刊	COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
ISSN	0895-6111
	2020-07-01
卷号	83 页码:9
通讯作者	Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
摘要	In endovascular and cardiovascular surgery, real-time and accurate segmentation and tracking of interventional instruments can aid in reducing radiation exposure, contrast agent and processing time. Nevertheless, this task often comes with the challenges of the elongated deformable structures with low contrast in noisy X-ray fluoroscopy. To address these issues, a novel efficient network architecture, termed pyramid attention recurrent networks (PAR-Net), is proposed for real-time guidewire segmentation and tracking. The proposed PAR-Net contains three major modules, namely pyramid attention module, recurrent residual module and pre-trained MobileNetV2 encoder. Specifically, a hybrid loss function of both reinforced focal loss and dice loss is proposed to better address the issues of class imbalance and misclassified examples. Quantitative and qualitative evaluations on clinical intraoperative images demonstrate that the proposed approach significantly outperforms simpler baselines as well as the best previously published result for this task, achieving the state-of-the-art performance. (C) 2020 Elsevier Ltd. All rights reserved.
关键词	Guidewire Catheter Segmentation Deep learning X-ray fluoroscopy
DOI	10.1016/j.compmedimag.2020.101734
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2019YFB1311700] ; National Natural Science Foundation of China[61533016] ; National Natural Science Foundation of China[U1613210] ; National Natural Science Foundation of China[61421004] ; CAMS Innovation Fund for Medical Sciences[2018 -12M -AI -004]
项目资助者	National Key Research and Development Plan of China ; National Natural Science Foundation of China ; CAMS Innovation Fund for Medical Sciences
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000552807300004
出版者	PERGAMON-ELSEVIER SCIENCE LTD
七大方向——子方向分类	多模态智能
引用统计	被引频次：13[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/173211/40196
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Hou, Zeng-Guang
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhou, Yan-Jie,Xie, Xiao-Liang,Zhou, Xiao-Hu,et al. Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy[J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,2020,83:9.
APA	Zhou, Yan-Jie,Xie, Xiao-Liang,Zhou, Xiao-Hu,Liu, Shi-Qi,Bian, Gui-Bin,&Hou, Zeng-Guang.(2020).Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy.COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,83,9.
MLA	Zhou, Yan-Jie,et al."Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy".COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 83(2020):9.