Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning

Azarmehr, Neda, Ye, Xujiong, Janan, Faraz, Howard, James P, Francis, Darrel P and Zolgharni, Massoud (2019) Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning. In: Medical Imaging with Deep Learning 2019, 8-10th July 2019, London.

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Automated Segmentation of Left Ventricle in 2D echocardiography using deep learning
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Abstract

Following the successful application of the U-Net to medical images, there have been different encoder-decoder models proposed as an improvement to the original U-Net for segmenting echocardiographic images. This study aims to examine the performance of the state-of-the-art proposed models claimed to have better accuracies, as well as the original U-Net model by applying them to an independent dataset of patients to segment the endocardium of the Left Ventricle in 2D automatically. The prediction outputs of the models are used to evaluate the performance of the models by comparing the automated results against the expert annotations (gold standard). Our results reveal that the original U-Net model outperforms other models by achieving an average Dice coefficient of 0.92±0.05, and Hausdorff distance of 3.97±0.82.

Keywords:Echocardiography, Segmentation, Deep Learning
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G121 Mechanics (Mathematical)
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:38038
Deposited On:30 Oct 2019 13:59

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