中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
Motion Decoupling Network for Intra-Operative Motion Estimation Under Occlusion

文献类型：期刊论文

作者Bian, Gui-Bin6; Zhang, Li5; Chen, He5; Li, Zhen4; Fu, Pan3; Yue, Wen-Qian2; Luo, Yu-Wen5; Ge, Pei-Cong1; Liu, Wei-Peng5
刊名IEEE TRANSACTIONS ON MEDICAL IMAGING
出版日期2023-10-01
卷号42期号:10页码:2924-2935
ISSN号0278-0062
关键词Optical flow Surgery Instruments Estimation Task analysis Videos Motion estimation Computer-assisted surgery motion estimation optical flow self-supervised learning surgical images
DOI10.1109/TMI.2023.3268774
通讯作者Liu, Wei-Peng(liuweipeng@hebut.edu.cn)
英文摘要In recent intelligent-robot-assisted surgery studies, an urgent issue is how to detect the motion of instruments and soft tissue accurately from intra-operative images. Although optical flow technology from computer vision is a powerful solution to the motion-tracking problem, it has difficulty obtaining the pixel-wise optical flow ground truth of real surgery videos for supervised learning. Thus, unsupervised learning methods are critical. However, current unsupervised methods face the challenge of heavy occlusion in the surgical scene. This paper proposes a novel unsupervised learning framework to estimate the motion from surgical images under occlusion. The framework consists of a Motion Decoupling Network to estimate the tissue and the instrument motion with different constraints. Notably, the network integrates a segmentation subnet that estimates the segmentation map of instruments in an unsupervised manner to obtain the occlusion region and improve the dual motion estimation. Additionally, a hybrid self-supervised strategy with occlusion completion is introduced to recover realistic vision clues. Extensive experiments on two surgical datasets show that the proposed method achieves accurate motion estimation for intra-operative scenes and outperforms other unsupervised methods, with a margin of 15% in accuracy. The average estimation error for tissue is less than 2.2 pixels on average for both surgical datasets.
WOS关键词OPTICAL-FLOW ; TRACKING
资助项目National Natural Science Foundation of China[62027813] ; National Natural Science Foundation of China[U20A20196] ; National Natural Science Foundation of China[62176266] ; Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team[JCTD-2019-07] ; Beijing Science Fund for Distinguished Young Scholars[JQ21016] ; Natural Science Foundation of Hebei Province[F2020202009]
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001081975500010
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team ; Beijing Science Fund for Distinguished Young Scholars ; Natural Science Foundation of Hebei Province
源URL[http://ir.ia.ac.cn/handle/173211/54343]  
专题多模态人工智能系统全国重点实验室
智能机器人系统研究
通讯作者Liu, Wei-Peng
作者单位1.Beijing Tiantan Hosp, Beijing 100070, Peoples R China
2.Hebei Univ Technol, Sch Hlth Sci & Biomed Engn, Tianjin 300131, Peoples R China
3.Beijing Informat Sci & Technol Univ, Sch Automat, Beijing 100192, Peoples R China
4.Tongji Univ, Sch Elect & Informat Engn, Shanghai 201804, Peoples R China
5.Hebei Univ Technol, Sch Artificial Intelligence & Data Sci, Tianjin 300131, Peoples R China
6.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Bian, Gui-Bin,Zhang, Li,Chen, He,et al. Motion Decoupling Network for Intra-Operative Motion Estimation Under Occlusion[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2023,42(10):2924-2935.
APA Bian, Gui-Bin.,Zhang, Li.,Chen, He.,Li, Zhen.,Fu, Pan.,...&Liu, Wei-Peng.(2023).Motion Decoupling Network for Intra-Operative Motion Estimation Under Occlusion.IEEE TRANSACTIONS ON MEDICAL IMAGING,42(10),2924-2935.
MLA Bian, Gui-Bin,et al."Motion Decoupling Network for Intra-Operative Motion Estimation Under Occlusion".IEEE TRANSACTIONS ON MEDICAL IMAGING 42.10(2023):2924-2935.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。