CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
A Real-Time Multi-Task Framework for Guidewire Segmentation and Endpoint Localization in Endovascular Interventions
Zhou, Yan-Jie1,2; Liu, Shi-Qi1; Xie, Xiao-Liang1,2; Zhou, Xiao-Hu1; Wang, Guan-An1,2; Hou, Zeng-Guang1,2,3,4; Li, Rui-Qi1,2; Ni, Zhen-Liang1,2; Fan, Chen-Chen1,2
2021-06
Conference NameInternational Conference on Robotics and Automation (ICRA 2021)
Conference Date2021.05.31-06.04
Conference Place中国西安
PublisherIEEE
Abstract

Real-time guidewire segmentation and endpoint localization play a pivotal role in robot-assisted minimally invasive surgery, which is helpful to reduce radiation dose and procedure time. Nevertheless, the tasks often come with the challenge of limited computational resources. For this purpose, a real-time multi-task framework with two stages is developed. In the first stage, a Fast Attention-fused Network (FAD-Net) is proposed to obtain accurate guidewire segmentation masks. In the second stage, a lightweight localization network and a post-processing algorithm are designed to robustly predict the guidewire endpoint position. Quantitative and qualitative evaluations on intraoperative X-ray sequences from 30 patients demonstrate that the developed framework outperforms the previously-published results for the tasks, achieving state-of-the-art performance. Moreover, the inference rate of the developed framework is approximately 10.6 FPS, which meets the real-time requirement of X-ray fluoroscopy. These results indicate the proposed approach has the potential to be integrated into the robotic navigation framework for endovascular interventions, enabling robotic-assisted minimally invasive surgery.

Indexed ByEI
Funding ProjectStrategic Priority Research Program of Chinese Academy of Science[XDB32040000] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040000]
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48546
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorHou, Zeng-Guang
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
3.CAS Center for Excellence in Brain Science and Intelligence Technology
4.CASIA-MUST Joint Laboratory of Intelligence Science and Technology, Institute of Systems Engineering, Macau University of Science and Technology
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhou, Yan-Jie,Liu, Shi-Qi,Xie, Xiao-Liang,et al. A Real-Time Multi-Task Framework for Guidewire Segmentation and Endpoint Localization in Endovascular Interventions[C]:IEEE,2021.
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