Neural Implicit Representation for Three-dimensional Ultrasound Carotid Surface Reconstruction using Unsigned Distance Function

doi:10.1109/IUS51837.2023.10307668

	Neural Implicit Representation for Three-dimensional Ultrasound Carotid Surface Reconstruction using Unsigned Distance Function
	Chen, Hongbo1,2 ; Kumaralingam, Logiraj 1; Li, Jiawen2 ; Punithakumar, Kumaradevan 1; Le, Lawrence H.1,3; Zheng, Rui2,4
	2023-11-07
会议录名称	2023 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IUS)
ISSN	1948-5719
发表状态	已发表
DOI	10.1109/IUS51837.2023.10307668
摘要	Accurate 3D geometric shapes of carotid arteries are imperative for the three-dimensional (3D) ultrasound (US) imaging to clinically assess the carotid atherosclerosis (CA). However, the traditional surface reconstruction method, such as iso-surface (ISO-SURF), suffers from the image noise, voxel resolution and additional processing. In this paper, we introduce the neural implicit representation based on the deep learning network for the 3D surface reconstruction of media-adventitia boundary (MAB), plaque, and lumen-intima boundary (LIB) together. The unsigned distance functions are learned by the network to generate the mesh. For the validation, six volumes were simulated in carotid shape with MAB and LIB, and random noise was added around the boundaries. The results showed that experiments on six simulated volumes illustrated better performance than ISO-SURF, with 47%, 36%, and 55% decrease in Chamfer distance, average absolute distance, and Hausdorff distance, respectively. The visualization results from the CA clinical data revealed a smoother and more continuous geometric surface than ISO-SURF. The comparison result has shown the potential of the proposed method to examine vascular pathologies in the future. © 2023 IEEE.
会议录编者/会议主办者	IEEE ; IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (UFFC)
关键词	Carotid atherosclerosis 3D ultrasound surface reconstruction Unsigned distance functions Neural network
会议名称	2023 IEEE International Ultrasonics Symposium, IUS 2023
会议地点	Montreal, QC, Canada
会议日期	3-8 Sept. 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	IEEE Computer Society
EI入藏号	20234915174930
EI主题词	Diseases
EISSN	1948-5727
EI分类号	461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 723.2 Data Processing and Image Processing ; 723.5 Computer Applications ; 746 Imaging Techniques ; 753.1 Ultrasonic Waves ; 921 Mathematics
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348748
专题	信息科学与技术学院信息科学与技术学院_PI研究组_郑锐组信息科学与技术学院_硕士生信息科学与技术学院_博士生
通讯作者	Le, Lawrence H.; Zheng, Rui
作者单位	1.University of Alberta, Department of Radiology and Diagnostic Imaging, Edmonton; AB, Canada 2.ShanghaiTech University, School of Information Science and Technology, Shanghai, China 3.University of Alberta, Department of Biomedical Engineering, Edmonton; AB, Canada 4.Shanghai Engineering Research Center of Energy Efficient and Custom Ai Ic, Shanghai, China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Chen, Hongbo,Kumaralingam, Logiraj,Li, Jiawen,et al. Neural Implicit Representation for Three-dimensional Ultrasound Carotid Surface Reconstruction using Unsigned Distance Function[C]//IEEE, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (UFFC):IEEE Computer Society,2023.