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, 155, pp. 165-170. (doi: 10.1016/j.patrec.2021.10.025)
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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.
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was supported in part by the Dazhi Scholarship of the Guangdong Polytechnic Normal University, National Natural Science Foundation of China (62072122), Education Department of Guangdong Province (2019KSYS009), China, and a Feasibility Study supported by EPSRC (Engineering and Physics Sciences Research Council) Centre for Multiscale Soft Tissue Mechanics - with application to heart & cancer. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Gao, Dr Hao and Luo, Professor Xiaoyu |
| Authors: | Ren, J., Sun, H., Zhao, H., Gao, H., Maclellan, C., Zhao, S., and Luo, X. |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics > Mathematics |
| Journal Name: | Pattern Recognition Letters |
| Publisher: | Elsevier |
| ISSN: | 0167-8655 |
| ISSN (Online): | 1872-7344 |
| Published Online: | 26 October 2021 |
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