Azarmehr, N. orcid.org/0000-0002-6367-207X, Ye, X., Janan, F. et al. (3 more authors) (2019) Automated segmentation of left ventricle in 2D echocardiography using deep learning. In: MIDL 2019 : Medical Imaging with Deep Learning, extended abstracts. MIDL 2019 : Medical Imaging with Deep Learning, 08-10 Jul 2019, London, UK.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. |
Keywords: | Echocardiography; Segmentation; Deep Learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Aug 2021 08:35 |
Last Modified: | 23 Aug 2021 08:35 |
Published Version: | https://openreview.net/forum?id=Sye8klvmcN |
Status: | Published |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177145 |