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DFR-Net: A Novel Multi-Task Learning Network for Real-Time Multi-Instrument Segmentation 会议论文
, 中国成都, 2021.10.20-24
作者:  Zhou, Yan-Jie;  Liu, Shi-Qi;  Xie, Xiao-Liang;  Hou, Zeng-Guang
Adobe PDF(2606Kb)  |  收藏  |  浏览/下载：195/33  |  提交时间：2022/06/14
neural networks  instrument segmentation  multi-task learning  
A Real-Time Multifunctional Framework for Guidewire Morphological and Positional Analysis in Interventional X-Ray Fluoroscopy 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2021, 卷号: 13, 期号: 3, 页码: 657-667
作者:  Zhou, Yan-Jie;  Xie, Xiao-Liang;  Zhou, Xiao-Hu;  Liu, Shi-Qi;  Bian, Gui-Bin;  Hou, Zeng-Guang
收藏  |  浏览/下载：255/0  |  提交时间：2021/11/04
Deep learning  guidewire  robot-assisted intervention  segmentation and localization  X-ray fluoroscopy  
An Interventionalist-Behavior-Based Data Fusion Framework for Guidewire Tracking in Percutaneous Coronary Intervention 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2020, 卷号: 50, 期号: 11, 页码: 4836-4849
作者:  Zhou, Xiao-Hu;  Bian, Gui-Bin;  Xie, Xiao-Liang;  Hou, Zeng-Guang
收藏  |  浏览/下载：254/0  |  提交时间：2021/01/07
Tracking  Sensors  Tools  Catheters  Electromyography  Data integration  Frequency modulation  Data fusion framework  guidewire tracking  interventionalist behavior  percutaneous coronary intervention (PCI)  
Lightweight Double Attention-Fused Networks for Intraoperative Stent Segmentation 会议论文
, Lima, Peru, 2020.10.04-08
作者:  Zhou, Yan-Jie;  Xie, Xiao-Liang;  Hou, Zeng-Guang;  Zhou, Xiao-Hu;  Bian, Gui-Bin;  Liu, Shi-Qi
Adobe PDF(1648Kb)  |  收藏  |  浏览/下载：170/39  |  提交时间：2022/06/14
Stent segmentation  Intraoperative X-ray fluoroscopy  Convolution neural networks  
A Real-time Multi-functional Framework for Guidewire Morphological and Positional Analysis in Interventional X-ray Fluoroscopy 期刊论文
IEEE Transactions on Cognitive and Developmental Systems, 2020, 卷号: 13, 期号: 3, 页码: 657-667
作者:  Zhou, Yan-Jie;  Xie, Xiao-Liang;  Zhou, Xiao-Hu;  Liu, Shi-Qi;  Bian, Gui-Bin;  Hou, Zeng-Guang
Adobe PDF(2854Kb)  |  收藏  |  浏览/下载：288/78  |  提交时间：2020/11/05
Deep learning  Guidewire  X-ray fluoroscopy  Segmentation and localization  Robot-assisted intervention  
A Lightweight Recurrent Attention Network for Real-Time Guidewire Segmentation and Tracking in Interventional X-Ray Fluoroscopy 会议论文
, Santiago de Compostela, Spain, 2020.08.31-09.04
作者:  Zhou, Yan-Jie;  Xie, Xiao-Liang;  Bian, Gui-Bin;  Hou, Zeng-Guang
Adobe PDF(2761Kb)  |  收藏  |  浏览/下载：164/40  |  提交时间：2022/06/14
Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy 期刊论文
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2020, 卷号: 83, 页码: 9
作者:  Zhou, Yan-Jie;  Xie, Xiao-Liang;  Zhou, Xiao-Hu;  Liu, Shi-Qi;  Bian, Gui-Bin;  Hou, Zeng-Guang
收藏  |  浏览/下载：204/0  |  提交时间：2020/08/24
Guidewire  Catheter  Segmentation  Deep learning  X-ray fluoroscopy  
Pyramid attention recurrent networks for real-time guidewiresegmentation and tracking in intraoperative X-ray fluoroscopy 期刊论文
Computerized Medical Imaging and Graphics, 2020, 卷号: 83, 页码: 101734
作者:  Zhou, Yan-Jie;  Xie, Xiao-Liang;  Zhou, Xiao-Hu;  Liu, Shi-Qi;  Bian, Gui-Bin;  Hou, Zeng-Guang
Adobe PDF(2211Kb)  |  收藏  |  浏览/下载：186/46  |  提交时间：2022/06/14
Guidewire  Catheter  Segmentation  Deep learning  X-ray fluoroscopy  
FRR-NET: Fast Recurrent Residual Networks for Real-Time Catheter Segmentation and Tracking in Endovascular Aneurysm Repair 会议论文
, Iowa city, USA, 2020.04.03-07
作者:  Zhou, Yan-Jie;  Xie, Xiao-Liang;  Hou, Zeng-Guang;  Bian, Gui-Bin;  Liu, Shi-Qi;  Zhou, Xiao-Hu
Adobe PDF(2567Kb)  |  收藏  |  浏览/下载：166/30  |  提交时间：2022/06/14
Catheter  Segmentation  Tracking  Deep learning  X-ray fluoroscopy  
Qualitative and Quantitative Assessment of Technical Skills in Percutaneous Coronary Intervention: In Vivo Porcine Studies 期刊论文
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2020, 卷号: 67, 期号: 2, 页码: 353-364
作者:  Zhou, Xiao-Hu;  Bian, Gui-Bin;  Xie, Xiao-Liang;  Hou, Zeng-Guang;  Li, Rui-Qi;  Zhou, Yan-Jie
收藏  |  浏览/下载：284/0  |  提交时间：2020/03/30
Natural behaviors  percutaneous coronary intervention  assessment framework  qualitative and quantitative assessment  in vivo porcine studies