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

doi:10.1016/j.compmedimag.2020.101734

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy
	Zhou, Yan-Jie 1,3; Xie, Xiao-Liang 1,3; Zhou, Xiao-Hu 3; Liu, Shi-Qi 3; Bian, Gui-Bin 3; Hou, Zeng-Guang 1,2,3
刊名	COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
	2020-07-01
卷号	83 页码:9
关键词	Guidewire Catheter Segmentation Deep learning X-ray fluoroscopy
ISSN号	0895-6111
DOI	10.1016/j.compmedimag.2020.101734
通讯作者	Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
英文摘要	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.
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]
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000552807300004
资助机构	National Key Research and Development Plan of China ; National Natural Science Foundation of China ; CAMS Innovation Fund for Medical Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40195]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Hou, Zeng-Guang
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
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.