Table 3 Review of the papers that applied deep learning for coronary vessel segmentation.

References	Modality	Data source	No. of subj.	ML model	Input	DSC	Add’l perf. metrics
Dong et al.⁶⁷	CCTA	Private	338	Di-Vnet	3D patches	0.902	Prec = 0.921, Recal l= 0.97
Gao et al.⁶⁸	XCA	Private	130	GBDT	2D images	–	F1 = 0.874, Sen = 0.902, Spec = 0.992
Wolterink et al.⁶⁹	CCTA	Publicly available	18	GCN	2D images	0.74	MSD = 0.25 mm
Li et al.⁷⁰	CCTA	Private	243	2D U-Net with 3DNet	2D images	0.771 ± 0.021	AUC = 0.737
Song et al.⁷¹	CCTA	Private	68	3D FFR U-Net	3D patches	0.816	Prec = 0.77, Recall = 0.87
Zeng et al.⁷²	CCTA	Publicly available	1000	3D-UNet	3D volumes	0.82	–

DSC Dice similarity coefficient, Prec Precision, Sen sensitivity, Spec specificity, GBDT gradient boosting decision tree, CCTA coronary computed tomographic angiography, XCA X-ray coronary angiography, GCN graph convolutional networks, MSD mean surface distance, AUC area under the receiver operating characteristic curve, 3D FFR U-Net 3D feature fusion and rectification U-Net.

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