Abstract
Coronary artery disease is a major cause of mortality and morbidity. Automatic segmentation of the coronary artery is a key step in the diagnosis of coronary artery disease. This study aims to propose an attention guided multi-scale fusion network with channel-enhanced Transformer for automatic segmentation of coronary arteries on coronary computed tomography angiography (CCTA). To improve the segmentation performance, an attention guided multi-scale fusion (AGMF) module and channel-enhanced transformer (C-Trans) module are proposed. The AGMF can effectively extract and fuse multi-layer features to locate and segment multiple small coronary arteries accurately. Specifically, the AGMF module not only can extract representative coronary artery features in the encoding process but also can accurately locate and segment coronary arteries in the decoding process. To select the effective channel features, the C-Trans is introduced to obtain the channel features with token. This module can effectively screen out the channel features in each layer of U-net to improve the accuracy of segmentation. Experimental results show that our proposed method obtains the IoU of 0.6996 and the Dice of 0.8224, which are more than 1% higher than the existing model. From the results, it is found that our proposed network outperforms the state-of-the-art 2D coronary artery segmentation method.
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Acknowledgments
This work was supported by the Liaoning Provincial ‘Selecting the Best Candidates by Opening Competition Mechanism’ Science and Technology Program (No.2022JH/10400004). This study was also supported by the National Natural Science Foundation of China (No. 62273082 and No. 61773110), the Natural Science Foundation of Liaoning Province (No. 20170540312 and No. 2021-YGJC-14), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education (LJKZ00042021), the Fundamental Research Funds for the Central Universities (No. N2119008), and the Shenyang Science and Technology Plan Fund (No. 21–104-1–24, No. 20–201-4–10, and No. 201375). We gratefully acknowledge the kind assistance of Yanan Wu and Chengbao Peng in article structure and writing logic.
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Yang, J. et al. (2024). An Attention Guided Multi-scale Network with Channel-Enhanced Transformer for Coronary Arteries Segmentation. In: Wang, G., Yao, D., Gu, Z., Peng, Y., Tong, S., Liu, C. (eds) 12th Asian-Pacific Conference on Medical and Biological Engineering. APCMBE 2023. IFMBE Proceedings, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-031-51455-5_19
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