Optimal Coronary Artery Segmentation Based on Transfer Learning and UNet Architecture
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46914-5_5
Reference10 articles.
1. Cheung, W.K., et al.: A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning. IEEE Access 9, 108873–108888 (2021)
2. Gu, L., Cai, X.C.: Fusing 2D and 3D convolutional neural networks for the segmentation of aorta and coronary arteries from CT images. Artif. Intell. Med. 121, 102189 (2021). https://doi.org/10.1016/j.artmed.2021.102189
3. Hajhosseiny, R., et al.: Clinical comparison of sub-mm high-resolution non-contrast coronary CMR angiography against coronary CT angiography in patients with low-intermediate risk of coronary artery disease: a single center trial. J. Cardiovasc. Magn. Reson. 23, 1–14 (2021)
4. Kurata, A., et al.: On-site computed tomography-derived fractional flow reserve using a machine-learning algorithm–clinical effectiveness in a retrospective multicenter cohort. Circ. J. 83(7), 1563–1571 (2019)
5. Otero-Cacho, A., et al.: Validation of a new model of non-invasive functional assessment of coronary lesions by computer tomography fractional flow reserve. REC: CardioClinics (2023)
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