Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation-Reference-Cited by-同舟云学术

Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation

Published:2023-12 Issue:12 Volume:42 Page:3474-3486
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Ding Wangbin¹^ORCID,Li Lei²^ORCID,Qiu Junyi³,Wang Sihan³^ORCID,Huang Liqin¹^ORCID,Chen Yinyin⁴,Yang Shan⁴,Zhuang Xiahai³^ORCID

Affiliation:

1. College of Physics and Information Engineering, Fuzhou University, Fuzhou, China

2. Department of Engineering Science, University of Oxford, OX3 7DQ Oxford, U.K

3. School of Data Science, Fudan University, Shanghai, China

4. Department of Radiology, Zhongshan Hospital, and the Department of Medical Imaging, Shanghai Medical School, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China

Funder

National Natural Science Foundation of China

Fujian Provincial Natural Science Foundation Project

Shanghai Jiao Tong University 2021 Outstanding Doctoral Graduate Development Scholarship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10336247/10159447.pdf?arnumber=10159447

Reference56 articles.

1. Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images

2. Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

3. Relation of Gender to Infarct Size in Patients With ST-Segment Elevation Myocardial Infarction Undergoing Primary Angioplasty

4. Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi‐planar U‐Net (CMPU‐Net)

5. Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors

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