RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments | |
Zhen-Liang Ni1,2; Gui-Bin Bian1,2; Xiao-Hu Zhou1; Zeng-Guang Hou1,2,3; Xiao-Liang Xie1; Chen Wang1,2; Yan-Jie Zhou1,2; Rui-Qi Li1,2; Zhen Li1 | |
2019-12-09 | |
会议日期 | 2019.12.12-2019.12.15 |
会议地点 | Sydney, Australia |
关键词 | surgical instrument segmentation Robotics and Vision |
DOI | https://doi.org/10.1007/978-3-030-36711-4_13 |
英文摘要 | Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module has very few parameters, which helps to save memory. Thus, it can be flexibly plugged into other networks. Besides, a hybrid loss is introduced to train our network for addressing the class imbalance issue, which merges cross entropy and logarithms of Dice loss. A new dataset named Cata7 is constructed to evaluate our network. To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation. Based on this dataset, RAUNet achieves state-of-the-art performance 97.71%% mean Dice and 95.62%% mean IOU. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48700] |
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 |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.The 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 | Zhen-Liang Ni,Gui-Bin Bian,Xiao-Hu Zhou,et al. RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments[C]. 见:. Sydney, Australia. 2019.12.12-2019.12.15. |
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