Hamon M, Biondi-Zoccai GGL, Malagutti P, Agostoni P, Morello R, Valgimigli M, Hamon M. Diagnostic performance of multislice spiral computed tomography of coronary arteries as compared with conventional invasive coronary angiography: a meta-analysis. J Am Coll Cardiol. 2006;48:1896–910.

Article  PubMed  Google Scholar 

Kulathilake KASH, Ranathunga L, Constantine GR, Abdullah NA. (2015) Region growing segmentation method for extracting vessel structures from coronary cine-angiograms. In: 2015 Moratuwa Eng. Res. Conf. MERCon. pp 142–147.

Kerkeni A, Benabdallah A, Manzanera A, Bedoui MH. A coronary artery segmentation method based on multiscale analysis and region growing. Comput Med Imaging Graph. 2016;48:49–61.

Article  PubMed  Google Scholar 

Ma G, Yang J, Zhao H. A coronary artery segmentation method based on region growing with variable sector search area. Technol Health Care. 2020;28:463–72.

Article  PubMed  PubMed Central  Google Scholar 

Cruz-Aceves I, Oloumi F, Rangayyan RM, Aviña-Cervantes JG, Hernandez-Aguirre A. Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization. Biomed Signal Process Control. 2016;25:76–85.

Article  Google Scholar 

Zai S, Abbas A. (2018) An Effective Enhancement and Segmentation of Coronary Arteries in 2D Angiograms. In: 2018 Int. Conf. Smart Comput. Electron. Enterp. ICSCEE. pp 1–4.

Yi F, Moon I. Image segmentation: a survey of graph-cut methods. 2012 Int Conf Syst Inf ICSAI 2012. 2012. https://doi.org/10.1109/ICSAI.2012.6223428.

Article  Google Scholar 

M’hiri F, Duong L, Desrosiers C, Leye M, Miró J, Cheriet M. A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences. Comput Biol Med. 2016;79:45–58.

Article  PubMed  Google Scholar 

Mabrouk S, Oueslati C, Ghorbel F. Multiscale Graph cuts based method for coronary artery segmentation in Angiograms. IRBM. 2017;38:167–75.

Article  Google Scholar 

Carballal A, Novoa FJ, Fernandez-Lozano C, García-Guimaraes M, Aldama-López G, Calviño-Santos R, Vazquez-Rodriguez JM, Pazos A. Automatic multiscale vascular image segmentation algorithm for coronary angiography. Biomed Signal Process Control. 2018;46:1–9.

Article  Google Scholar 

Cervantes-Sanchez F, Cruz-Aceves I, Hernandez-Aguirre A, Hernandez-Gonzalez MA, Solorio-Meza SE. Automatic segmentation of coronary arteries in X-ray angiograms using Multiscale Analysis and Artificial neural networks. Appl Sci. 2019;9:5507.

Article  Google Scholar 

Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SMR, Ward K, Jafari MH, Felfeliyan B, Nallamothu B, Najarian K. Vessel extraction in X-ray angiograms using deep learning. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Int Conf. 2016;2016:643–6.

CAS  Google Scholar 

Fan J, Yang J, Wang Y, Yang S, Ai D, Huang Y, Song H, Hao A, Wang Y. Multichannel fully Convolutional Network for coronary artery segmentation in X-Ray angiograms. IEEE Access. 2018;6:44635–43.

Article  Google Scholar 

Yang S, Kweon J, Roh J-H, et al. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep. 2019;9:16897.

Article  PubMed  PubMed Central  Google Scholar 

Shin SY, Lee S, Yun ID, Lee KM. Deep vessel segmentation by learning graphical connectivity. Med Image Anal. 2019;58:101556.

Article  PubMed  Google Scholar 

Wang L, Liang D, Yin X, Qiu J, Yang Z, Xing J, Dong J, Ma Z. Coronary artery segmentation in angiographic videos utilizing spatial-temporal information. BMC Med Imaging. 2020;20:110.

Article  PubMed  PubMed Central  Google Scholar 

Wan T, Chen J, Zhang Z, Li D, Qin Z. Automatic vessel segmentation in X-ray angiogram using spatio-temporal fully-convolutional neural network. Biomed Signal Process Control. 2021;68:102646.

Article  Google Scholar 

Zhu X, Cheng Z, Wang S, Chen X, Lu G. Coronary angiography image segmentation based on PSPNet. Comput Methods Programs Biomed. 2021;200:105897.

Article  PubMed  Google Scholar 

Gao Z, Wang L, Soroushmehr R, Wood A, Gryak J, Nallamothu B, Najarian K. Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features. BMC Med Imaging. 2022;22:10.

Article  PubMed  PubMed Central  Google Scholar 

Tao X, Dang H, Zhou X, Xu X, Xiong D. A Lightweight Network for Accurate Coronary Artery Segmentation using X-Ray angiograms. Front Public Health. 2022;10:892418.

Article  PubMed  PubMed Central  Google Scholar 

Lourenço-Silva J, Menezes MN, Rodrigues T, Silva B, Pinto FJ, Oliveira AL. Encoder-decoder architectures for clinically relevant Coronary artery segmentation. In: Bansal MS, Măndoiu I, Moussa M, Patterson M, Rajasekaran S, Skums P, Zelikovsky A, editors. Comput. Adv. Bio Med. Sci. Cham: Springer International Publishing; 2022. pp. 63–78.

Chapter  Google Scholar 

Meng Y, Du Z, Zhao C, Dong M, Pienta D, Tang J, Zhou W. Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms. Technol Health Care off J Eur Soc Eng Med. 2023;31:2303–17.

Google Scholar 

Wang G, Zhou P, Gao H, Qin Z, Wang S, Sun J, Yu H. Coronary vessel segmentation in coronary angiography with a multi-scale U-shaped transformer incorporating boundary aggregation and topology preservation. Phys Med Biol. 2024. https://doi.org/10.1088/1361-6560/ad0b63.

Article  PubMed  PubMed Central  Google Scholar 

Zhao C, Bober R, Tang H et al. (2021) Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms. 2020.05.26.20103440.

Jun TJ, Kweon J, Kim Y-H, Kim D. T-Net: nested encoder–decoder architecture for the main vessel segmentation in coronary angiography. Neural Netw. 2020;128:216–33.

Article  PubMed  Google Scholar 

Xian Z, Wang X, Yan S, Yang D, Chen J, Peng C. Main coronary vessel segmentation using deep learning in Smart Medical. Math Probl Eng. 2020;2020:8858344.

Article  Google Scholar 

Zhang H, Zhang D, Gao Z, Zhang H. (2021) Joint Segmentation and Quantification of Main Coronary Vessels Using Dual-Branch Multi-scale Attention Network. In: Med. Image Comput. Comput. Assist. Interv. – MICCAI 2021 24th Int. Conf. Strasbg. Fr. Sept. 27–October 1 2021 Proc. Part I. Springer-Verlag, Berlin, Heidelberg, pp 369–378.

Park J, Kweon J, Kim YI, et al. Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography. Med Phys. 2023;50:7822–39.

Article  PubMed  Google Scholar 

Zhang H, Gao Z, Zhang D, Hau WK, Zhang H. Progressive perception learning for main coronary segmentation in X-Ray angiography. IEEE Trans Med Imaging. 2023;42:864–79.

Article  PubMed  Google Scholar 

Zhao C, Xu Z, Jiang J, Esposito M, Pienta D, Hung G-U, Zhou W. (2023) AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms. Pattern Recognit 143:109789. This paper introduces a novel graph matching network for semantic labelling of coronary arteries.

Zhao C, Xu Z, Hung G-U, Zhou W. EAGMN: coronary artery semantic labeling using edge attention graph matching network. Comput Biol Med. 2023;166:107469.

Article  PubMed  PubMed Central  Google Scholar 

Zhao C, Esposito M, Xu Z, Zhou W. (2023) Hyper Association Graph matching with uncertainty quantification for Coronary Artery Semantic Labeling. https://doi.org/10.48550/arXiv.2308.10320

Dwivedi VP, Bresson X. (2021) A Generalization of Transformer Networks to Graphs. https://doi.org/10.48550/arXiv.2012.09699

Corbière C, THOME N, Bar-Hen A, Cord M, Pérez P. (2019) Addressing failure prediction by learning Model confidence. Adv Neural Inf Process Syst 32.

Tang H, Bober RR, Zhao C, Zhang C, Zhu H, He Z, Xu Z, Zhou W. 3D fusion between fluoroscopy angiograms and SPECT myocardial perfusion images to guide percutaneous coronary intervention. J Nucl Cardiol. 2022;29:1870–84.

Article  PubMed  Google Scholar 

Xu Z, Malhotra S, Zhao C, Jiang J, Vij A, Ye Z, Hua R, Li C, Wang C, Zhou W. (2023) 3D fusion between SPECT myocardial perfusion imaging and invasive coronary angiography to guide the treatment for patients with stable CAD. 2023.09.18.23295731.

Xu Z, Tang H, Malhotra S, et al. Three-dimensional Fusion of Myocardial Perfusion SPECT and Invasive Coronary Angiography guides coronary revascularization. J Nucl Cardiol. 2022;29:3267–77.

Article  PubMed  Google Scholar 

Zhao C, Vij A, Malhotra S, Tang J, Tang H, Pienta D, Xu Z, Zhou W. (2021) Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms. Comput Biol Med 136:104667. This paper combines extraction of coronary and stenosis evaluation in a pipeline while enhancing dignostice accuracy amd efficiency in identifying coronary artery disease.

Freitas SA, Zeiser FA, Da Costa CA, De O, Ramos G. (2022) DeepCADD: A Deep Learning Architecture for Automatic Detection of Coronary Artery Disease. In: 2022 Int. Jt. Conf. Neural Netw. IJCNN. pp 1–8.

Han T, Ai D, Li X, Fan J, Song H, Wang Y, Yang J. Coronary artery stenosis detection via proposal-shifted spatial-temporal transformer in X-ray angiography. Comput Biol Med. 2023;153:106546.

Article  PubMed  Google Scholar 

Pang K, Ai D, Fang H, Fan J, Song H, Yang J. Stenosis-DetNet: sequence consistency-based stenosis detection for X-ray coronary angiography. Comput Med Imaging Graph. 2021;89:101900.

Article  PubMed  Google Scholar 

Moon JH, Lee DY, Cha WC, Chung MJ, Lee K-S, Cho BH, Choi JH. Automatic stenosis recognition from coronary angiography using convolutional neural networks. Comput Methods Programs Biomed. 2021;198:105819.

Article  PubMed  Google Scholar 

Avram R, Olgin JE, Ahmed Z, et al. CathAI: fully automated coronary angiography interpretation and stenosis estimation. Npj Digit Med. 2023;6:1–12.

Article  Google Scholar 

Avram R, Labrecque-Langlais E, Corbin D et al. (2023) Evaluation of Stenoses Using AI Video Models Applied to Coronary Angiographies. https://doi.org/10.21203/rs.3.rs-3610879/v1

Morris PD, Curzen N, Gunn JP. Angiography-derived fractional Flow Reserve: more or less physiology? J Am Heart Assoc. 2020;9:e015586.

Article  PubMed  PubMed Central