Knowledge Commons of Institute of Automation,CAS
FRR-NET: Fast Recurrent Residual Networks for Real-Time Catheter Segmentation and Tracking in Endovascular Aneurysm Repair | |
Zhou, Yan-Jie1,3![]() ![]() ![]() ![]() ![]() ![]() | |
2020-04 | |
会议名称 | International Symposium on Biomedical Imaging (ISBI) |
会议日期 | 2020.04.03-07 |
会议地点 | Iowa city, USA |
出版者 | IEEE |
摘要 | For endovascular aneurysm repair (EVAR), real-time and accurate segmentation and tracking of interventional instruments can aid in reducing radiation exposure, contrast agents, and procedure time. Nevertheless, this task often comes with the challenges of the slender deformable structures with low contrast in noisy X-ray fluoroscopy. In this paper, a novel efficient network architecture, termed FRR-Net, is proposed for real-time catheter segmentation and tracking. The novelties of FRR-Net lie in the manner in which recurrent convolutional layers ensure better feature representation and the pre-trained lightweight components can improve model processing speed while ensuring performance. Quantitative and qualitative evaluation of images from 175 X-ray sequences of 30 patients 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 |
关键词 | Catheter Segmentation Tracking Deep learning X-ray fluoroscopy |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61533016] ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China[61421004] ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China[61421004] ; National Natural Science Foundation of China[61533016] |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48549 |
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.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.CAS Center for Excellence in Brain Science and Intelligence Technology 3.School of Artificial Intelligence, University of Chinese Academy of Sciences |
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,Hou, Zeng-Guang,et al. FRR-NET: Fast Recurrent Residual Networks for Real-Time Catheter Segmentation and Tracking in Endovascular Aneurysm Repair[C]:IEEE,2020. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
8-FRR-Net Fast Recur(2567KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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