Table 1 Evaluation indicators ± standard deviation of all competing methods in the Synapse multi-organ segmentation dataset.

From: Dual-branch dynamic hierarchical U-Net with multi-layer space fusion attention for medical image segmentation

Methods

DICE (%)

IOU (%)

RAVD (%)

ASSD

MSSD

FCN

62.61 ± 0.56

49.67 ± 0.46

− 17.67 ± 0.48

7.98 ± 0.48

43.11 ± 4.2

U-Net

75.73 ± 0.45

64.52 ± 0.52

− 8.07 ± 0.39

5.83 ± 0.39

32.50 ± 3.8

ResU-Net

77.31 ± 0.33

66.40 ± 0.33

− 10.85 ± 0.22

5.49 ± 0.52

31.00 ± 2.5

Attention U-Net

77.57 ± 0.42

66.44 ± 0.28

− 9.91 ± 0.45

5.29 ± 0.37

31.35 ± 2.9

U-Net++

79.33 ± 0.33

67.53 ± 0.36

− 8.68 ± 0.12

4.74 ± 0.28

26.56 ± 5.1

ResU-Net++

76.61 ± 0.24

65.00 ± 0.28

3.21 ± 0.36

4.76 ± 0.42

25.52 ± 2.7

TransformU-Net

79.52 ± 0.14

66.48 ± 0.19

1.63 ± 0.25

4.33 ± 0.37

23.51 ± 3.2

SwimU-Net

76.61 ± 0.31

65.00 ± 0.20

3.21 ± 0.21

4.76 ± 0.22

25.52 ± 2.9

HiFormer

76.92 ± 0.21

65.4 ± 0.36

− 5.56 ± 0.15

4.53 ± 0.38

26.81 ± 2.8

D2HU-Net

80.39 ± 0.19

68.32 ± 0.13

1.54 ± 0.09

4.03 ± 0.36

21.49 ± 2.4