Knowledge Commons of Institute of Automation,CAS
Lightweight Double Attention-Fused Networks for Intraoperative Stent Segmentation | |
Zhou, Yan-Jie1,3; Xie, Xiao-Liang1,3; Hou, Zeng-Guang1,2,3; Zhou, Xiao-Hu1; Bian, Gui-Bin1; Liu, Shi-Qi1 | |
2020-10 | |
会议名称 | Medical Image Computing and Computer Assisted Intervention (MICCAI) |
会议日期 | 2020.10.04-08 |
会议地点 | Lima, Peru |
出版者 | Springer |
摘要 | In endovascular interventional therapy, the fusion of preoperative data with intraoperative X-ray fluoroscopy has demonstrated the potential to reduce radiation dose, contrast agent, and processing time. Real-time intraoperative stent segmentation is an important prerequisite for accurate fusion. Nevertheless, this task often comes with the challenge of the thin stent wires with low contrast in noisy X-ray fluoroscopy. In this paper, a novel and efficient network, termed Lightweight Double Attention-fused Network (LDA-Net), is proposed for end-to-end stent segmentation in intraoperative X-ray fluoroscopy. The proposed LDANet consists of three major components, namely feature attention module, relevance attention module, and pre-trained MobileNetV2 encoder. Besides, a hybrid loss function of both reinforced focal loss and dice loss is designed to better address the issues of class imbalance and misclassified examples. Quantitative and qualitative evaluations on 175 intraoperative X-ray sequences demonstrate that the proposed LDA-Net significantly outperforms simpler baselines as well as the best previously-published result for this task, achieving the state-of-the-art performance. |
关键词 | Stent segmentation Intraoperative X-ray fluoroscopy Convolution neural networks |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61533016] ; National Natural Science Foundation of China[61533016] |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48548 |
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 Science |
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,Hou, Zeng-Guang,et al. Lightweight Double Attention-Fused Networks for Intraoperative Stent Segmentation[C]:Springer,2020. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
7-Lightweight Double(1648KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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