Recognition of Endovascular Manipulations using Recurrent Neural Networks | |
Li, Rui-Qi1,2; Zhou, Xiao-Hu1,2; Bian, Gui-Bin1,2; Xie, Xiao-Liang1,2; Hou, Zeng-Guang1,2,3 | |
2019 | |
会议日期 | 7.23-7.27 |
会议地点 | 德国 |
英文摘要 | The ability to accurately recognize elementary surgical gestures is a stepping stone to automated surgical assessment and surgical training. In this paper, a long short-term memory (LSTM) recurrent neural network is applied to the task of recognizing six typical manipulations in percutaneous coronary intervention (PCI). The manipulation mentioned above is referring to the atomic surgical operation, also called surgeme in many research. Instead of using the video data or kinematic data of surgical instruments, we propose to use the kinematic data of the operator's hand acquired by our wearable data glove to recognize the manipulations. To establish a baseline for comparison, a method based on Hidden Markov Model (HMM) is applied because HMM is frequently used in the tasks of surgical sequence learning. Two cross-validation schemes are used in our experiments, they both illustrate that our LSTM-based |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/46619] |
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.University of Chinese Academy of Sciences, Beijing 100049, China. 2.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing 100190, China |
推荐引用方式 GB/T 7714 | Li, Rui-Qi,Zhou, Xiao-Hu,Bian, Gui-Bin,et al. Recognition of Endovascular Manipulations using Recurrent Neural Networks[C]. 见:. 德国. 7.23-7.27. |
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