1. Faraji M, Cheng I, Naudin I, Basu A. Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection. Ultrasonics. 2018; 84:356–365. PMID:
29241056.
2. Jeong G, Lee G, Lee J, Kang SJ. Deep learning-based lumen and vessel segmentation of intravascular ultrasound images in coronary artery disease. Korean Circ J. 2024; 54:30–39. PMID:
38111183.
3. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. Red Hook (NY): Curran Associates, Inc.;2012.
4. Yang J, Tong L, Faraji M, Basu A. In : Basu A, Berretti S, editors. IVUS-Net: an intravascular ultrasound segmentation network. Proceedings of the International Conference on Smart Multimedia; 2018 August 24–26; Toulon, France. Cham: Springer;2018. p. 367–377.
5. Nishi T, Yamashita R, Imura S, et al. Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease. Int J Cardiol. 2021; 333:55–59. PMID:
33741429.
6. Baheti B, Innani S, Gajre S, Talbar S. Eff-UNet: a novel architecture for semantic segmentation in unstructured environment. In : Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops; 2020 June 14–19; Seattle (WA), USA. New York (NY): IEEE;2020.
7. Coenen A, Kim YH, Kruk M, et al. Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium. Circ Cardiovasc Imaging. 2018; 11:e007217. PMID:
29914866.
8. Ziedses des Plantes AC, Scoccia A, Gijsen F, van Soest G, Daemen J. Intravascular imaging-derived physiology-basic principles and clinical application. Interv Cardiol Clin. 2023; 12:83–94. PMID:
36372464.
9. Yang S, Koo BK. Coronary physiology-based approaches for plaque vulnerability: implications for risk prediction and treatment strategies. Korean Circ J. 2023; 53:581–593. PMID:
37653694.
10. Ihdayhid AR, Sakaguchi T, Kerrisk B, et al. Influence of operator expertise and coronary luminal segmentation technique on diagnostic performance, precision and reproducibility of reduced-order CT-derived fractional flow reserve technique. J Cardiovasc Comput Tomogr. 2020; 14:356–362. PMID:
31787591.