Automated cardiovascular magnetic resonance image analysis with fully convolutional networks-Reference-Cited by-同舟云学术

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Published:2018-09-14 Issue:1 Volume:20 Page:
ISSN:1532-429X
Container-title:Journal of Cardiovascular Magnetic Resonance
language:en
Short-container-title:J Cardiovasc Magn Reson

Author:

Bai Wenjia^ORCID,Sinclair Matthew,Tarroni Giacomo,Oktay Ozan,Rajchl Martin,Vaillant Ghislain,Lee Aaron M.,Aung Nay,Lukaschuk Elena,Sanghvi Mihir M.,Zemrak Filip,Fung Kenneth,Paiva Jose Miguel,Carapella Valentina,Kim Young Jin,Suzuki Hideaki,Kainz Bernhard,Matthews Paul M.,Petersen Steffen E.,Piechnik Stefan K.,Neubauer Stefan,Glocker Ben,Rueckert Daniel

Funder

Engineering and Physical Sciences Research Council

Publisher

Springer Science and Business Media LLC

Subject

Cardiology and Cardiovascular Medicine,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://link.springer.com/content/pdf/10.1186/s12968-018-0471-x.pdf

Reference34 articles.

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3. Fihn SD, et al. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease. Circulation. 2012; 60(24):44–164.

4. McMurray JJV, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012. Eur J Heart Fail. 2012; 14(8):803–69.

5. UK Biobank Imaging Study. http://imaging.ukbiobank.ac.uk/. Accessed 11 July 2017.

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