Automatic left ventricle segmentation from cardiac magnetic resonance images using a capsule network-Reference-Cited by-同舟云学术

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Automatic left ventricle segmentation from cardiac magnetic resonance images using a capsule network

Published:2020-06-09 Issue:3 Volume:28 Page:541-553
ISSN:0895-3996
Container-title:Journal of X-Ray Science and Technology
language:
Short-container-title:XST

Author:

He Yangsu¹²,Qin Wenjian¹,Wu Yin¹,Zhang Mengxi³,Yang Yongfeng¹,Liu Xin¹,Zheng Hairong¹,Liang Dong¹,Hu Zhanli¹

Affiliation:

1. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

2. School of Electrical and Information Engineering, Hunan University, Changsha, China

3. Department of Biomedical Engineering, University of California, Davis, CA, USA

Publisher

IOS Press

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiology Nuclear Medicine and imaging,Instrumentation,Radiation

Reference17 articles.

1. Word Health Organization. Cardiovascular diseases (CVDs). (17 May 2017).

2. Artifact correction in low-dose dental CT imaging using wasserstein generative adversarial networks;Hu;Medical Physics,2019

3. Comparison of CT and CMR for detection and quantification of carotid artery calcification: the Rotterdam Study;Mujaj;Journal of Cardiovascular Magnetic Resonance,2017

4. Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture;Qin;Biomedical Physics & Engineering Express,2020

5. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?;Bernard;IEEE Transactions on Medical Imaging,2018

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2. Improved efficient capsule network for Kuzushiji-MNIST benchmark dataset classification;Bulletin of the Polish Academy of Sciences Technical Sciences;2023-09-05

3. A Review of Capsule Networks in Medical Image Analysis;Artificial Neural Networks in Pattern Recognition;2022-11-11

4. Artificial intelligence study on left ventricular function among normal individuals, hypertrophic cardiomyopathy and dilated cardiomyopathy patients using 1.5T cardiac cine MR images obtained by SSFP sequence;The British Journal of Radiology;2022-05-01

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