Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery

doi:10.1109/ACCESS.2020.2980005

	Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery
	Qiu, Huaiyu 1; Li, Zhen 2; Yang, Yu 2,3; Xin, Chen 4; Bian, Gui-Bin2,5
发表期刊	IEEE ACCESS
ISSN	2169-3536
	2020
卷号	8 页码:50648-50658
通讯作者	Yang, Yu(2120170273@bit.edu.cn)
摘要	Robotic-assisted platforms are expected to guarantee the accuracy of surgical operation and accelerate its learning curve. Iris tracking can guide the robotic manipulator during the operation. However, few researches focused on it during surgery. It is a big challenge due to the deformation of the iris and occlusion caused by instruments. A novel real-time iris tracking method based on a regression network are proposed to meet the speed and accuracy requirements of the ophthalmic robotic system. It utilizes the low-level visual features and high-level semantic meanings from different layers to capture the discriminative representation of the iris target. Then the bottleneck layers are added to improve computation efficiency. Furthermore, a multi-loss function is designed by jointly learning Absolute loss and Euclidean loss. Finally, the experimental results under the typical surgical scene demonstrate that iris tracker achieves an accuracy of 89.16 & x0025; and a real-time speed of 134fps with GPU, which is suitable for the ophthalmic robotic system to perform real-time robotic manipulation.
关键词	Robots Target tracking Iris recognition Surgery Iris Cataracts Robotic surgery deep learning cataract surgery iris tracking real-time tracking
DOI	10.1109/ACCESS.2020.2980005
关键词[WOS]	CATARACT ; PREVALENCE
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1302704] ; National Natural Science Foundation of China[U1713220]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000524898800002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	机器人感知与决策
引用统计	被引频次：4[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/173211/38816
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Yang, Yu
作者单位	1.Capital Med Univ, Beijing Chaoyang Hosp, Dept Ophthalmol, Beijing 100020, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Beijing Inst Technol, Sch Mechatron Engn, Beijing 100081, Peoples R China 4.Beijing Tongren Hosp, Beijing Tongren Eye Ctr, Beijing 100730, Peoples R China 5.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China
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	Qiu, Huaiyu,Li, Zhen,Yang, Yu,et al. Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery[J]. IEEE ACCESS,2020,8:50648-50658.
APA	Qiu, Huaiyu,Li, Zhen,Yang, Yu,Xin, Chen,&Bian, Gui-Bin.(2020).Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery.IEEE ACCESS,8,50648-50658.
MLA	Qiu, Huaiyu,et al."Real-Time Iris Tracking Using Deep Regression Networks for Robotic Ophthalmic Surgery".IEEE ACCESS 8(2020):50648-50658.