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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)
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ISSN | 0302-9743 |
卷号 | 13413 LNCS |
页码 | 65-74 |
发表状态 | 已发表 |
DOI | 10.1007/978-3-031-12053-4_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 |
URL | 查看原文 |
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 |
EISSN | 1611-3349 |
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) |
引用统计 | 正在获取...
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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 |
推荐引用方式 GB/T 7714 | 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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