Cardiac MR segmentation based on sequence propagation by deep learning-Reference-Cited by-同舟云学术

Cardiac MR segmentation based on sequence propagation by deep learning

Published:2020-04-09 Issue:4 Volume:15 Page:e0230415
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Luo Chao,Shi Canghong,Li Xiaoji,Gao Dongrui^ORCID

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference19 articles.

1. Explaining the decline in coronary heart disease mortality in the Czech Republic between 1985 and 2007;Jan Bruthans;European Journal of Preventive Cardiology,2012

2. Late-life coronary heart disease mortality of Finnish war veterans in the TAMRISK study, a 28-year follow-up;Tarja Kunnas;Bmc Public Health,2011

3. Standardized cardiovascular magnetic resonance imaging (CMR) protocols, society for cardiovascular magnetic resonance: board of trustees task force on standardized protocols;Christopher M Kramer;Journal of Cardiovascular Magnetic Resonance Official Journal of the Society for Cardiovascular Magnetic Resonance,2008

4. Motion compensated cine CMR of the fetal heart using radial undersampling and compressed sensing;Christopher W. Roy;Journal of Cardiovascular Magnetic Resonance,2017

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