Emerging artificial intelligence applications in liver magnetic resonance imaging

Table 1 Applications of artificial intelligence segmentation methods to liver magnetic resonance imaging

Ref.	Task	Method	MR image	DICE
Mole et al[37], 2020	Segment liver from T1 mapping technique to aid surgical planning	3D U-Net	T1 map	0.97
Winther et al[27], 2020	Segment liver from Gd-EOB-DTPA-enhanced MRI for volume calculations	3D U-Net	Gadoxetic acid-enhanced MRI	0.96 ± 1.9
Liu et al[30], 2020	Segment liver for automated liver iron quantification	2D U-Net	T2* map	0.86 ± 0.01
Wang et al[43], 2019	Segment Liver across multiple imaging modalities and techniques	2D U-Net	T1- and T2*- weighted	T1-w: 0.95 ± 0.03
				T2-w: 0.92 ± 0.05
Cunha et al[46], 2020	Segment liver to classify if adequate contrast uptake has occurred in contrast enhanced scans	2D U-Net	Pre- and post-contrast T1- weighted, and T2- weighted	Not reported
Chen et al[31], 2020	Segment multiple organs in abdominal scans, to aid radiotherapy planning	2D Dense U-Net	T1-weighted	Liver: 0.96 ± 0.009
Bousabarah et al[36], 2020	Segment and delineate HCCs	2D U-Net	Gadoxetic acid-enhanced MRI	Liver: 0.91 ± 0.01
				Tumour: 0.68 ± 0.03
Ivashchenko et al[41], 2019	Segment liver, vasculature and biliary tree	4D k-mean clustering	Gadoxetic acid-enhanced MRI	Liver: 0.95 ± 0.01
Irving et al[44], 2017	Segment liver with vessel exclusion to assist in liver assessment	2D U-Net	T1 map	0.95
Yang et al[45], 2019	Segment liver across multiple domains via domain transfer	Cycle GAN and 2D U-Net	Gadoxetic acid-enhanced MRI	0.891 ± 0.040
Christ et al[39], 2017	Segment liver and tumours within, in CT and MRI	Two sequential 2D U-Nets	Diffusion-weighted	Liver: 0.87
				Tumour: 0.697
Fu et al[35], 2018	Segment multiple organs in abdominal scans, to aid radiotherapy planning	Three Dense CNNs	T2/T1-weighted	Liver: 0.953 ± 0.007
Valindria et al[33], 2018	Segment multiple organs in multi-modal (MR,CT) scans	ResNet Encoder Decoder	T2-weighted	Liver: 0.914
Masoumi et al[42], 2012	Segment the liver	Watershed (non-AI) + ANN	Abdominal MRI	0.94 (IoU not DICE)
Jansen et al[40], 2019	Segment liver and metastases	CNN	DCE-MR and diffusion-weighted	Liver: 0.95

MRI: Magnetic resonance imaging; CT: Computed tomography; DCE-MR: Dynamic contrast enhanced magnetic resonance; HCC: Hepatocellular carcinoma; GAN: Generative adversarial network; CNN: Convolutional neural network; ANN: Artificial neural network; IoU: Intersect over union.