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Pyramid attention recurrent networks for real-time guidewiresegmentation and tracking in intraoperative X-ray fluoroscopy
Zhou, Yan-Jie1,3; Xie, Xiao-Liang1,3; Zhou, Xiao-Hu1; Liu, Shi-Qi1; Bian, Gui-Bin1; Hou, Zeng-Guang1,2,3
发表期刊Computerized Medical Imaging and Graphics
2020-05
卷号83页码:101734
文章类型期刊
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.

关键词Guidewire Catheter Segmentation Deep learning X-ray fluoroscopy
DOIhttps://doi.org/10.1016/j.compmedimag.2020.101734
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[U1613210] ; 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] ; National Natural Science Foundation of China[U1613210]
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/48543
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,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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