Pyramid attention recurrent networks for real-time guidewiresegmentation and tracking in intraoperative X-ray fluoroscopy | |
Zhou, Yan-Jie1,2; Xie, Xiao-Liang1,2; Zhou, Xiao-Hu1; Liu, Shi-Qi1; Bian, Gui-Bin1; Hou, Zeng-Guang1,2,3 | |
刊名 | Computerized Medical Imaging and Graphics
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2020-05 | |
卷号 | 83页码:101734 |
关键词 | Guidewire Catheter Segmentation Deep learning X-ray fluoroscopy |
DOI | https://doi.org/10.1016/j.compmedimag.2020.101734 |
文献子类 | 期刊 |
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 state-of-the-art performance. |
资助项目 | Foundation for Innovative Research Groups of the National Natural Science Foundation of China[61421004] ; National Natural Science Foundation of China[61533016] ; National Natural Science Foundation of China[U1613210] |
语种 | 英语 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48543] ![]() |
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.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 |
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 guidewiresegmentation and tracking in intraoperative X-ray fluoroscopy[J]. Computerized Medical Imaging and Graphics,2020,83:101734. |
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 guidewiresegmentation and tracking in intraoperative X-ray fluoroscopy.Computerized Medical Imaging and Graphics,83,101734. |
MLA | Zhou, Yan-Jie,et al."Pyramid attention recurrent networks for real-time guidewiresegmentation and tracking in intraoperative X-ray fluoroscopy".Computerized Medical Imaging and Graphics 83(2020):101734. |
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