Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis

Table 2 Characteristics of deep learning networks for segmentation of hepatocellular carcinoma from computed tomography images, mean ± SD

Ref.	Method	Combine	Type of CT	Train set	Test set	Validation set	VOE (%)	RVD (%)	ASD (mm)	RMSD (mm)	DICE (%)
Nakai et al[32]	CNN		CECT	493	62	62
Shah et al[33]	MDL-CNN	Cascade	CECT	2948 slice	1264 slice		9.8	8.2			95.7
Ouhmich et al[34]	UNet	Cascade	CECT	Cross-validation (1:6)							68.1 ± 23.2
Khan et al[35]	RMS-UNet	Residual multi-scale	CT	101	21	21	14.95 ± 9.40	-0.7 ± 1.3	3.06 ± 3.13	1.60 ± 0.72	91.92 ± 0.05
Gong et al[36]	UNet-DRLSEIC	DRLSEIC	CT	110	40						95.2 ± 1.7
Chen et al[37]	RDA-UNet	ResNet DenseNet and UNet	CT	15611 slice		3903 slice					87.03
Li et al[38]	H-DenseUnNet	DenseNet and UNet	CECT	131	70	20	11.68 ± 4.33	-0.01 ± 0.05	0.58 ± 0.46	1.87 ± 2.33	93.7 ± 2
Wang et al[39]	MAD-Unet	Multi-scale attention and deep supervision	CECT	116	15		6.83 ± 2.31	0.34 ± 0.19	1.03 ± 0.37	3.74 ± 3.58	97.27 ± 1.22
Lee et al[40]	HFS-Net	DenseUNet and UNet	DNCT	298	179	118					82.8
Ou et al[41]	ResTransUNet	Transformer and UNet	CECT				8.04 ± 6.8	-0.07 ± 9.5			95.35 ± 4.5
Jiang et al[42]	Swin-UNet	SFTB and LCAB	CECT	104	26		37.38	-0.1577	5.1433		76.14
Clinton Atabansi et al[43]	ICT-Net	Transformer and convolution	CECT	1789	225	223					90.91
d’Albenzi et al[44]	DEDC-Net	ResNet and VGG-19	CECT	101	15	15			12.17 ± 12.67		46.1 ± 27.4
Singh et al[45]	FasNet	ResNet-50 and VGG-16	CECT								87.66
Guo et al[46]	FCN and ACM	FCN and ACM	CT	42	16	19			1.6 ± 0.5	3.5 ± 1.2	95.8 ± 1.4
Zhang et al[47]	DeepRecS	RMP-Net and CGBS-Net	CECT	139	46	46	15.88 ± 3.79	0.32 ± 6.45	0.47 ± 0.45	1.57 ± 1.46	91.32 ± 2.30
Balasubramanian et al[48]	APESTNet and Mask R-CNN	APESTNet and Mask R-CNN	CECT	121	10	20	5.37 ± 3.27	-1.08 ± 2.06	1.85 ± 0.30		97.31 ± 1.49
Liu et al[49]	S2DANet	FSMF and MAHA and GMCA	CECT	92	26	13	43.95	0.3861	10.80		69.51

VOE: Volumetric overlap error; CT: Computed tomography; RVD: Relative volume difference; ASD: Average symmetric surface distance; RMSD: Root mean square symmetric surface distance; DICE: Dice coefficient; DNCT: Dynamic computed tomography; CECT: Contrast-enhanced computed tomography; MDL: Multi-channel deep learning; CNN: Convolutional neural network; UNet: U shaped network; RMS: Residual multi scale; DRLSEIC: Distance regularized level set evolution D constrained; RDA-Unet: Residual-dense-attention-U shaped network; H-DenseUnNet: Hybrid densely connected U shaped network; MAD: Multi-scale attention and deep supervision; HFS: Hierarchical fusion strategy; ResTransUnet: An effective network combined with transformer and U shaped network for liver segmentation in computed tomography scans; ICT-Net: Integrated convolution and transformer-based network; DEDC-Net: The dual-encoder double concatenation network; FCN: Fully convolutional network; ACM: Active contour model; DeepRecS: A novel deep learning based interactive method for liver tumor segmentation, which incorporated RECIST-marks as guiding signals; APESTNet: Enhanced Swin transformer network with adversarial propagation; Mask R-CNN: Mask region-based convolutional neural network; S2DANet: Spatial and spectral-learning double-branched aggregation network; ResNet: Residual network; DenseNet: Densely connected convolutional network; SFTB: Swin-neighborhood fusion transformer block; LCAB: Large-kernel convolutional attention block; VGG: Visual geometry group; RMP-Net: RECIST mark propagation network; CGBS-Net: Context-guided boundary-sensitive network; FSMF: Fourier spectral-learning multi-scale fusion; MAHA: Multi-axis aggregation Hadamard attention; GMCA: Group multi-head cross-attention aggregation.