Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolu...

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Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 38; no. 9; pp. 2198 - 2210
Main Authors:	Leclerc, Sarah, Smistad, Erik, Pedrosa, Joao, Ostvik, Andreas, Cervenansky, Frederic, Espinosa, Florian, Espeland, Torvald, Berg, Erik Andreas Rye, Jodoin, Pierre-Marc, Grenier, Thomas, Lartizien, Carole, Dhooge, Jan, Lovstakken, Lasse, Bernard, Olivier
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Artificial neural networks Automation Cardiacsegmentation and diagnosis Coders Correlation coefficient Correlation coefficients Databases, Factual Datasets Deep Learning Echocardiography Echocardiography - methods Encoders-Decoders Engineering Sciences Heart Heart - diagnostic imaging Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation left atrium left ventricle Machine learning Medical diagnosis Medical imaging Myocardium Neural networks Signal and Image processing Three-dimensional displays Training Two dimensional analysis Two dimensional displays Ultrasonic imaging Ultrasound Ventricle cardiac strain Multimodal cardiac imaging deep learning electromechanical model left ventricle synthetic sequences motion estimation myocardium Cardiac segmentation and diagnosis left atrium ultrasound numerical simulation
ISSN:	0278-0062, 1558-254X, 1558-254X
Online Access:	Get full text
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