Joint Group-Wise Motion Estimation and Segmentation of Cardiac Cine MR Images Using Recurrent U-Net
2022
会议录名称LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
ISSN0302-9743
卷号13413 LNCS
页码65-74
发表状态已发表
DOI10.1007/978-3-031-12053-4_5
摘要

Cardiac segmentation and motion estimation are two important tasks for the assessment of cardiac structure and function. Studies have demonstrated deep learning segmentation methods considering the valuable dynamics of the heart have more robust and accurate segmentations than those treating each frame independently. The former methods require annotations of all frames for supervised training, while only end-systolic (ES) and end-diastolic (ED) frames are commonly labeled. The issue has been addressed by integrating motion estimation into the segmentation framework and generating annotations for unlabeled frames with the estimated motion. However, the current pair-wise registration method with the ED frame as the template image may result in inaccurate motion estimation for systolic frames. We therefore, propose to use a group-wise registration network where the template image is learned implicitly for optimal registration performance, with the assumption that more accurate motion estimation leads to improved segmentation performance. Specifically, a recurrent U-Net based network is employed for joint optimization of group-wise registration and segmentation of the left ventricle and myocardium, where the dynamic information is utilized for both tasks with the recurrent units. In addition, an enhancement mask covering the heart is generated with the segmentation masks, which is expected to improve the registration performance by focusing the motion estimation on the heart. Experimental results in a cardiac cine MRI dataset including normal subjects and patients show that the group-wise registration significantly outperforms the pair-wise registration which translates to more accurate segmentations. The effectiveness of the proposed enhancement mask is also demonstrated in an ablation study. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词Deep learning Heart Image enhancement Image segmentation Learning systems Magnetic resonance imaging Medical computing Medical imaging Cardiac motion Cardiac segmentation Cine-MRI Deep learning End-diastolic MR-images Nonrigid registration Registration performance Segmentation Template images
会议名称26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022
出版地GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
会议地点Cambridge, United kingdom
会议日期July 27, 2022 - July 29, 2022
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收录类别EI ; CPCI-S
语种英语
WOS研究方向Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000883331000005
出版者Springer Science and Business Media Deutschland GmbH
EI入藏号20223412590237
EI主题词Motion estimation
EISSN1611-3349
EI分类号461.1 Biomedical Engineering ; 461.2 Biological Materials and Tissue Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 701.2 Magnetism: Basic Concepts and Phenomena ; 723.5 Computer Applications ; 746 Imaging Techniques
原始文献类型Conference article (CA)
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文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/219706
专题生物医学工程学院_PI研究组_胡鹏组
信息科学与技术学院_硕士生
生物医学工程学院_PI研究组_齐海坤组
通讯作者Qi, Haikun
作者单位
1.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China
2.Univ Cambridge, Dept Comp Sci & Technol, Cambridge, England
第一作者单位生物医学工程学院
通讯作者单位生物医学工程学院
第一作者的第一单位生物医学工程学院
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Qian, Pengfang,Yang, Junwei,Lio, Pietro,et al. Joint Group-Wise Motion Estimation and Segmentation of Cardiac Cine MR Images Using Recurrent U-Net[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:Springer Science and Business Media Deutschland GmbH,2022:65-74.
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