  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 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 (CAMUS) 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.
