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Segmentation of cardiac structures is one of the fundamental steps toestimate volumetric indices of the heart. This step is still performedsemi-automatically in clinical routine, and is thus prone to inter- andintra-observer variability. Recent studies have shown that deep learning hasthe potential to perform fully automatic segmentation. However, the currentbest solutions still suffer from a lack of robustness. In this work, weintroduce an end-to-end multi-task network designed to improve the overallaccuracy of cardiac segmentation while enhancing the estimation of clinicalindices and reducing the number of outliers. Results obtained on a large openaccess dataset show that our method outperforms the current best performingdeep learning solution and achieved an overall segmentation accuracy lower thanthe intra-observer variability for the epicardial border (i.e. on average amean absolute error of 1.5mm and a Hausdorff distance of 5.1mm) with 11% ofoutliers. Moreover, we demonstrate that our method can closely reproduce theexpert analysis for the end-diastolic and end-systolic left ventricularvolumes, with a mean correlation of 0.96 and a mean absolute error of 7.6ml.Concerning the ejection fraction of the left ventricle, results are morecontrasted with a mean correlation coefficient of 0.83 and an absolute meanerror of 5.0%, producing scores that are slightly below the intra-observermargin. Based on this observation, areas for improvement are suggested.

LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography