Automated segmentation of left ventricle in 2D echocardiography using deep learning

Neda, Azarmehr, Xujiong, Ye and Zolgharni, Massoud ORCID: (2019) Automated segmentation of left ventricle in 2D echocardiography using deep learning. In: nternational Conference on Medical Imaging with Deep Learning, 8‑10 July 2019, London. (In Press)

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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.

Item Type: Conference or Workshop Item (Paper)
Keywords: Echocardiography, Segmentation, Deep Learning
Subjects: Computing > Intelligent systems
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Depositing User: Massoud Zolgharni
Date Deposited: 23 Sep 2019 07:07
Last Modified: 28 Aug 2021 07:11


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