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

By Sarah Leclerc, Erik Smistad, Joao Pedrosa, Andreas Ostvik, Fréderic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Berg, Pierre-Marc Jodoin, Thomas Grenier, Carole Lartizien, Jan Drhooge, Lasse Lovstakken and Olivier Bernard


International audienceDelineation 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 as well as estimating clinical indices, on a dataset especially designed to answer this objective. We therefore introduce the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CA-MUS) 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 endsystolic 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

Topics: Multimodal cardiac imaging, numerical simulation, cardiac strain, motion estimation, synthetic sequences, electromechanical model, Cardiac segmentation and diagnosis, deep learning, ultrasound, left ventricle, myocardium, left atrium, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Publisher: 'Institute of Electrical and Electronics Engineers (IEEE)'
Year: 2019
DOI identifier: 10.1109/TMI.2019.2900516
OAI identifier: oai:HAL:hal-02054458v1
Provided by: HAL-HCL
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