Guidewire Tracking Based on Regional Proposal Network and Residual Structure
LIU Shiqi1,2, SUN Xiaobo1, XIE Xiaoliang2, HOU Zengguang2
1.School of Automation, Harbin University of Science and Tech-nology, Harbin 150080 2. State Key Laboratory of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract X-ray image navigation is a breakthrough of improving the accuracy and safety of robotic interventional surgery. A method based on region proposal network, residual structure and canny edge detection is proposed in this paper. It is specifically designed for guidewire segmentation framework. In the image calibration, multi-scale marking strategies are adopted to enable detection networks to learn accurate features. In the image augmentation, a multi-filter fusion strategy is employed to increase the recognizability of the guidewire and improve the tracking accuracy and the system robustness. The experiment is conducted on 22 sets of X-ray video sequences. Experimental results demonstrate the superiority of the proposed algorithm in terms of speed, accuracy and robustness.
Fund:Supported by National Natural Science Foundation of China(No.61533016,U1613210)
Corresponding Authors:
HOU Zengguang, Ph.D., professor. His research interests include rehabilitation robot and minimal invasive interventional robot.
About author:: LIU Shiqi, master student. His research interests include minimal invasive interventional robot.SUN Xiaobo, master, professor. His research interests include control system and control engineering.XIE Xiaoliang, Ph.D., associate profe-ssor. His research interests include surgical robot modeling and control, medical image processing.
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2019, Vol. 32 
