Table 4. Performance of the deep learning methods: summary table.

Method	DSC [rank]
	Full-trained	Fine-tuned
U-Net-ResNet34	0.853±0.082 [1]	0.762±0.207 [11]
U-Net-MobileNet	0.830±0.194 [2]	0.755±0.230 [11]
LinkNet-ResNet34	0.828±0.131 [3]	0.797±0.178 [8]
LinkNet-MobileNet	0.827±0.116 [5]	0.789±0.201 [7]
PSPNet-ResNet34	0.813±0.155 [6]	0.769±0.172 [12]
U-Net-InceptionV3	0.811±0.128 [8]	0.804±0.190 [6]
PSPNet-InceptionV3	0.805±0.168 [6]	0.784±0.159 [11]
FPN-ResNet34	0.803±0.191 [3]	0.764±0.240 [9]
PSPNet-MobileNet	0.794±0.160 [10]	0.792±0.174 [10]
FPN-InceptionV3	0.792±0.189 [8]	0.814±0.173 [4]
LinkNet-InceptionV3	0.780±0.214 [8]	0.758±0.217 [12]
FPN-MobileNet	0.762±0.198 [12]	0.779±0.219 [7]

Data format is mean ± standard deviation [rank]; methods are listed in descending order of mean DSC for full-trained networks. A lower rank indicates a higher position in the standings. DSC, Sørensen-Dice coefficient; FPN, Feature Pyramid Networks.