Azarmehr, N. orcid.org/0000-0002-6367-207X, Ye, X., Sacchi, S. et al. (3 more authors) (2020) Segmentation of Left Ventricle in 2D Echocardiography Using Deep Learning. In: Zheng, Y., Williams, B.M. and Chen, K., (eds.) Medical Image Understanding and Analysis. MIUA 2019 : Medical Image Understanding and Analysis, 24-26 Jul 2019, Liverpool, UK. Communications in Computer and Information Science (1065). Springer , pp. 497-504. ISBN 9783030393427
Abstract
The segmentation of Left Ventricle (LV) is currently carried out manually by the experts, and the automation of this process has proved challenging due to the presence of speckle noise and the inherently poor quality of the ultrasound images. This study aims to evaluate the performance of different state-of-the-art Convolutional Neural Network (CNN) segmentation models to segment the LV endocardium in echocardiography images automatically. Those adopted methods include U-Net, SegNet, and fully convolutional DenseNets (FC-DenseNet). The prediction outputs of the models are used to assess the performance of the CNN models by comparing the automated results against the expert annotations (as the gold standard). Results reveal that the U-Net model outperforms other models by achieving an average Dice coefficient of 0.93 ± 0.04, and Hausdorff distance of 4.52 ± 0.90.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 Springer Nature Switzerland AG. |
Keywords: | Deep learning; Segmentation; Echocardiography |
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 08:54 |
Last Modified: | 23 Aug 2021 08:54 |
Status: | Published |
Publisher: | Springer |
Series Name: | Communications in Computer and Information Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-39343-4_43 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177146 |