Knowledge Commons of Institute of Automation,CAS
Online Instance Segmentation and Reconstruction of Ultrasound Vascular Videos | |
Chen JA(陈玖安)1,2![]() ![]() ![]() | |
2024-11 | |
会议名称 | IEEE Medical Measurements & Applications 2024 |
会议日期 | 2024-6 |
会议地点 | 荷兰 埃因霍温 |
摘要 | The application of ultrasound in interventional surgery faces many challenges due to its lack of clarity. While existing algorithms can process single-frame ultrasound images efficiently, they still suffer from poor accuracy and discontinuous detection. In this paper, we propose an online instance segmentation network for ultrasound-guided interventional surgery Videos. When detecting the current frame, the fusion of previous frames enhances the accuracy and continuity of the segmentation. Meanwhile, real-time 3D reconstruction of vessels and interventional instruments is also achieved through the collaboration of robotic arms. Furthermore, we construct a novel dataset for vascular interventions. It accurately labels vessels and interventional instruments in 112 ultrasound videos, making it suitable for tasks related to the detection and segmentation of vascular ultrasound images. Experiments demonstrate that the proposed network improves detection accuracy by 11.0% mAP75 compared to the state-of-the-art method. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57244 |
专题 | 多模态人工智能系统全国重点实验室_智能微创医疗技术团队 |
通讯作者 | Liu HB(刘宏斌) |
作者单位 | 1.中国科学院大学人工智能学院 2.中国科学院自动化研究所多模态人工智能系统全国重点实验室 3.香港城市大学生物医学工程学系 4.海军军医大学第二附属医院血管外科 5.中国科学院香港创新研究院人工智能与机器人创新中心 6.School of Biomedical Engineering and Imaging Sciences, King's College London |
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 | Chen JA,Chen MC,Zou SL,et al. Online Instance Segmentation and Reconstruction of Ultrasound Vascular Videos[C],2024. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
1571005830 final.pdf(1860KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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