Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916264562130944 |
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| author | Zhou, Yirong Wang, Chengyan Lu, Mengtian Guo, Kunyuan Wang, Zi Ruan, Dan Guo, Rui Zhao, Peijun Wang, Jianhua Wu, Naiming Lin, Jianzhong Chen, Yinyin Jin, Hang Xie, Lianxin Wu, Lilan Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Qu, Xiaobo |
| author_facet | Zhou, Yirong Wang, Chengyan Lu, Mengtian Guo, Kunyuan Wang, Zi Ruan, Dan Guo, Rui Zhao, Peijun Wang, Jianhua Wu, Naiming Lin, Jianzhong Chen, Yinyin Jin, Hang Xie, Lianxin Wu, Lilan Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Qu, Xiaobo |
| contents | In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods often address these tasks separately, limiting their synergistic potential. To address this, we propose SQNet, a dual-task network integrating Transformer and Convolutional Neural Network (CNN) components. SQNet features a T2-refine fusion decoder for quantitative analysis, leveraging global features from the Transformer, and a segmentation decoder with multiple local region supervision for enhanced accuracy. A tight coupling module aligns and fuses CNN and Transformer branch features, enabling SQNet to focus on myocardium regions. Evaluation on healthy controls (HC) and acute myocardial infarction patients (AMI) demonstrates superior segmentation dice scores (89.3/89.2) compared to state-of-the-art methods (87.7/87.9). T2 quantification yields strong linear correlations (Pearson coefficients: 0.84/0.93) with label values for HC/AMI, indicating accurate mapping. Radiologist evaluations confirm SQNet's superior image quality scores (4.60/4.58 for segmentation, 4.32/4.42 for T2 quantification) over state-of-the-art methods (4.50/4.44 for segmentation, 3.59/4.37 for T2 quantification). SQNet thus offers accurate simultaneous segmentation and quantification, enhancing cardiac disease diagnosis, such as AMI. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_10570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI Zhou, Yirong Wang, Chengyan Lu, Mengtian Guo, Kunyuan Wang, Zi Ruan, Dan Guo, Rui Zhao, Peijun Wang, Jianhua Wu, Naiming Lin, Jianzhong Chen, Yinyin Jin, Hang Xie, Lianxin Wu, Lilan Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Qu, Xiaobo Image and Video Processing Artificial Intelligence In cardiac Magnetic Resonance Imaging (MRI) analysis, simultaneous myocardial segmentation and T2 quantification are crucial for assessing myocardial pathologies. Existing methods often address these tasks separately, limiting their synergistic potential. To address this, we propose SQNet, a dual-task network integrating Transformer and Convolutional Neural Network (CNN) components. SQNet features a T2-refine fusion decoder for quantitative analysis, leveraging global features from the Transformer, and a segmentation decoder with multiple local region supervision for enhanced accuracy. A tight coupling module aligns and fuses CNN and Transformer branch features, enabling SQNet to focus on myocardium regions. Evaluation on healthy controls (HC) and acute myocardial infarction patients (AMI) demonstrates superior segmentation dice scores (89.3/89.2) compared to state-of-the-art methods (87.7/87.9). T2 quantification yields strong linear correlations (Pearson coefficients: 0.84/0.93) with label values for HC/AMI, indicating accurate mapping. Radiologist evaluations confirm SQNet's superior image quality scores (4.60/4.58 for segmentation, 4.32/4.42 for T2 quantification) over state-of-the-art methods (4.50/4.44 for segmentation, 3.59/4.37 for T2 quantification). SQNet thus offers accurate simultaneous segmentation and quantification, enhancing cardiac disease diagnosis, such as AMI. |
| title | Simultaneous Deep Learning of Myocardium Segmentation and T2 Quantification for Acute Myocardial Infarction MRI |
| topic | Image and Video Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2405.10570 |