Professor Jinchang Ren j.ren@rgu.ac.uk
Professor of Computing Science
Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net.
Ren, Jinchang; Sun, He; Zhao, Huimin; Gao, Hao; Maclellan, Calum; Zhao, Sophia; Luo, Xiaoyu
Authors
He Sun
Huimin Zhao
Hao Gao
Calum Maclellan
Sophia Zhao
Xiaoyu Luo
Abstract
Accurate extraction of semantic objects such as ventricles and myocardium from magnetic resonance (MR) images is one essential but very challenging task for the diagnosis of the cardiac diseases. To tackle this problem, in this paper, an automatic end-to-end supervised deep learning framework is proposed, using a multi-task learning based U-Net (MTL-UNet). Specifically, an edge extraction module and a fusion-based module are introduced for effectively capturing the contextual information such as continuous edges and consistent spatial patterns in terms of intensity and texture features. With a weighted triple loss including the dice loss, the cross-entropy loss and the edge loss, the accuracy of object segmentation and extraction has been effectively improved. Extensive experiments on the publicly available ACDC 2017 dataset have validated the efficacy and efficiency of the proposed MTL-UNet model.
Citation
REN, J., SUN, H., ZHAO, H., GAO, H., MACLELLAN, C., ZHAO, S. and LUO, X. 2022. Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net. Pattern recognition letters [online], 155, pages 165-170. Available from: https://doi.org/10.1016/j.patrec.2021.10.025
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 22, 2021 |
Online Publication Date | Oct 26, 2021 |
Publication Date | Mar 31, 2022 |
Deposit Date | Jan 10, 2022 |
Publicly Available Date | Mar 29, 2024 |
Journal | Pattern Recognition Letters |
Print ISSN | 0167-8655 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 155 |
Pages | 165-170 |
DOI | https://doi.org/10.1016/j.patrec.2021.10.025 |
Keywords | U-net; Multi-task learning; Magnetic resonance images (MRI); Ventricles and myocardium extraction; Fusion-based decoder |
Public URL | https://rgu-repository.worktribe.com/output/1512750 |
Files
REN 2021 Effective extraction of ventricles (AAM)
(1.3 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Feature aggregation and region-aware learning for detection of splicing forgery.
(2024)
Journal Article
PWDformer: deformable transformer for long-term series forecasting.
(2023)
Journal Article
Siamese residual neural network for musical shape evaluation in piano performance assessment.
(2023)
Conference Proceeding
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search