Knowledge Commons of Institute of Automation,CAS
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network | |
Zhen-Liang Ni1,3; Gui-Bin Bian1,3; Xiao-Liang Xie1; Zeng-Guang Hou1,2,3; Xiao-Hu Zhou1,3; Yan-Jie Zhou1,3 | |
2019-07 | |
会议名称 | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
会议日期 | 2019.7.23-2019.7.27 |
会议地点 | Berlin, Germany |
摘要 | Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU. |
DOI | 10.1109/EMBC.2019.8856495 |
收录类别 | EI |
语种 | 英语 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48699 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
作者单位 | 1.the 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.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhen-Liang Ni,Gui-Bin Bian,Xiao-Liang Xie,et al. RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network[C],2019. |
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