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

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

URL: http://arxiv.org/abs/2004.02043v1
Date: Sat, 4 Apr 2020 23:07:53 GMT
Title: LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Authors: Sarah Leclerc, Erik Smistad, Andreas {\O}stvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, Erik Andreas Rye Berg, Thomas Grenier, Carole Lartizien, Pierre-Marc Jodoin, Lasse Lovstakken, Olivier Bernard
Abstract summary: We introduce an end-to-end multi-task network designed to improve the overall accuracy of cardiac segmentation. Results obtained on a large open access dataset show that our method outperforms the current best performing deep learning solution.
Score: 6.633387219468496
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine, and is thus prone to inter- and intra-observer variability. Recent studies have shown that deep learning has the potential to perform fully automatic segmentation. However, the current best solutions still suffer from a lack of robustness. In this work, we introduce an end-to-end multi-task network designed to improve the overall accuracy of cardiac segmentation while enhancing the estimation of clinical indices and reducing the number of outliers. Results obtained on a large open access dataset show that our method outperforms the current best performing deep learning solution and achieved an overall segmentation accuracy lower than the intra-observer variability for the epicardial border (i.e. on average a mean absolute error of 1.5mm and a Hausdorff distance of 5.1mm) with 11% of outliers. Moreover, we demonstrate that our method can closely reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, 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 more contrasted with a mean correlation coefficient of 0.83 and an absolute mean error of 5.0%, producing scores that are slightly below the intra-observer margin. Based on this observation, areas for improvement are suggested.

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