Evaluation of fully automated myocardial segmentation techniques in native and contrast‐enhanced T1‐mapping cardiovascular magnetic resonance images using fully convolutional neural networks-Reference-Cited by-同舟云学术

Evaluation of fully automated myocardial segmentation techniques in native and contrast‐enhanced T1‐mapping cardiovascular magnetic resonance images using fully convolutional neural networks

Published:2020-12 Issue:1 Volume:48 Page:215-226
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Farrag Nadia A.¹,Lochbihler Aidan²,White James A.³,Ukwatta Eranga⁴

Affiliation:

1. Department of Systems and Computer Engineering Carleton University 1125 Colonel By Drive, Mackenzie 4456 Ottawa ONK1S5B6Canada

2. Department of Systems and Computer Engineering Carleton University Ottawa ONK1S 5B6Canada

3. Department of Cardiac Sciences University of Calgary Calgary ABT2N 1N4Canada

4. School of Engineering University of Guelph Guelph ONN1G 2W1Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Carleton University

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14574

Reference39 articles.

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3. Cardiac fibrosis as a determinant of ventricular tachyarrhythmias

4. Association of Fibrosis With Mortality and Sudden Cardiac Death in Patients With Nonischemic Dilated Cardiomyopathy

5. Assessment of Myocardial Fibrosis With Cardiovascular Magnetic Resonance

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