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Real-Time 2D/3D Registration via CNN Regression and Centroid Alignment

文献类型：期刊论文

作者Huang, De-Xing1,2; Zhou, Xiao-Hu1,2; Xie, Xiao-Liang1,2; Liu, Shi-Qi1,2; Feng, Zhen-Qiu1,2; Hou, Zeng-Guang1,2,3; Ma, Ning4; Yan, Long4
刊名IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
出版日期2024-01-04
页码14
关键词Chronic carotid artery occlusion real-time 2D/3D registration regression centroid alignment
ISSN号1545-5955
DOI10.1109/TASE.2023.3345927
通讯作者Zhou, Xiao-Hu(xiaohu.zhou@ia.ac.cn) ; Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
英文摘要Registration of pre-operative 3D volumes and intra-operative 2D images is critical for neurological interventions. In various 2D/3D registration tasks, deep learning-based approaches have become popular and achieved tremendous success. However, due to vast space of transformation parameters, estimation errors are significant in these approaches. To tackle above issues, a novel learning-based framework for 2D/3D registration is proposed, consisting of CNN regression and centroid alignment. The former introduces a residual regression network (Res-RegNet) to preliminarily estimate transformation parameters. To further reduce estimation errors, the latter utilizes target vessel centroids to refine projected images. The proposed framework is individually trained and evaluated on three patients, reaching mean Dice of 76.69%, 78.51%, and 85.39%, respectively, all outperforming baseline methods. Extensive ablation studies demonstrate centroid alignment can significantly improve registration performance. As a normalization layer in Res-RegNet, SPADE can modulate activations using binarized inputs through a spatially-adaptive, learned transformation. Semantic information of inputs is preserved to learn better representations for parameter estimation. Moreover, the inference rate of our framework is about 21 FPS combined with the state-of-the-art segmentation model, significantly surpassing real-time requirements (6 similar to 12 FPS) in clinical practice. These promising results indicate the potential of the framework to facilitate various 2D/3D registration tasks.
WOS关键词IMAGE REGISTRATION ; RADIOGRAPHS ; RAY
资助项目Beijing Natural Science Foundation
WOS研究方向Automation & Control Systems
语种英语
WOS记录号WOS:001167028400002
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/57929]  
专题多模态人工智能系统全国重点实验室_医疗机器人
通讯作者Zhou, Xiao-Hu; Hou, Zeng-Guang
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Macau Univ Sci & Technol, Inst Syst Engn, Joint Lab Intelligence Sci & Technol, Macau, Peoples R China
4.Capital Med Univ, Beijing Tiantan Hosp, Dept Intervent Neuroradiol, Beijing 100070, Peoples R China
推荐引用方式
GB/T 7714		 Huang, De-Xing,Zhou, Xiao-Hu,Xie, Xiao-Liang,et al. Real-Time 2D/3D Registration via CNN Regression and Centroid Alignment[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2024:14.
APA Huang, De-Xing.,Zhou, Xiao-Hu.,Xie, Xiao-Liang.,Liu, Shi-Qi.,Feng, Zhen-Qiu.,...&Yan, Long.(2024).Real-Time 2D/3D Registration via CNN Regression and Centroid Alignment.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,14.
MLA Huang, De-Xing,et al."Real-Time 2D/3D Registration via CNN Regression and Centroid Alignment".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2024):14.

入库方式： OAI收割

来源：自动化研究所

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