Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography

Objectives Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical imag...

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Veröffentlicht in:	European radiology 2022-09, Vol.32 (9), p.6037-6045
Hauptverfasser:	Li, Yiming, Wu, Yu, He, Jingjing, Jiang, Weili, Wang, Jianyong, Peng, Yong, Jia, Yuheng, Xiong, Tianyuan, Jia, Kaiyu, Yi, Zhang, Chen, Mao
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Angiography Arteries Artificial intelligence Artificial neural networks Automation Cardiac Cardiovascular disease Classification Computed tomography Coronary artery Coronary artery disease Coronary vessels Deep learning Diagnosis Diagnostic Radiology Heart diseases Image analysis Image classification Image processing Image segmentation Imaging Internal Medicine Interventional Radiology Labeling Machine learning Medical diagnosis Medical imaging Medicine Medicine & Public Health Neural networks Neuroradiology Radiology Reconstruction Stenosis Stents Tomography Ultrasound
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