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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

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