Table 3. Application of Deep Learning for Image Segmentation in Cardiovascular CT.

Study	Year	Development Dataset	CT Scans for Test (n)	Performance, Dice Coefficient	Network	Target of Segmentation/Modality for Prediction
Trullo et al. [34]	2017	Nonenhanced chest CT	30	0.91	FCN	Heart, thoracic aorta/nonenhanced chest CT
Commandeur et al. [16]	2018	Nonenhanced cardiac CT	250	0.82	Multi-task CNN (ConvNet)	Epicardial adipose tissue, paracardial adipose tissue, thoracic adipose tissue/ nonenhanced cardiac CT
Jin et al. [18]	2017	CCTA	150	0.94	FCN	Left atrial appendage/CCTA
López-Linares et al. [15]	2018	Aorta CT angiography	13	0.82	FCN	Postoperative thrombus after EVAR/aorta CT angiography
Cao et al. [38]	2019	Aorta CT angiography	30	0.93	FCN (3D U-Net)	Whole aorta, true lumen, false lumen/aorta CT angiography
Morris et al. [35]	2020	Paired CT/MRI data	11	0.88	FCN (3D U-Net)	Cardiac chambers, great vessels, coronary artery/nonenhanced CT for simulation
Baskaran et al. [4]	2020	CCTA	17	0.92	FCN (3D U-Net)	Cardiac chambers, LV myocardium/CCTA
Bruns et al. [36]	2020	CCTA (dual-energy set)	290	0.89	3D CNN	Cardiac chambers, LV myocardium/nonenhanced cardiac CT
Chen et al. [31]	2020	CCTA	518	-	CNN	Left atrium/CCTA
Koo et al. [17]	2020	CCTA	1000	0.88	FCN	LV myocardium/CCTA

CCTA = coronary computed tomography angiography, CNN = convolutional neural network, CT = computed tomography, D = dimensional, DC = dice coefficient, EVAR = endovascular aneurysm repair, FCN = fully convolutional neural network, LV = left ventricle