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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Vydané v:	IEEE transactions on medical imaging Ročník 38; číslo 9; s. 2198 - 2210
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	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
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Popis
Shrnutí:	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 convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0278-0062 1558-254X 1558-254X
DOI:	10.1109/TMI.2019.2900516