Table 2 Performance comparisons of different methods on the Synapse dataset.

From: MedFuseNet: fusing local and global deep feature representations with hybrid attention mechanisms for medical image segmentation

Methods

Average DSC

HD

Aorta

Gallbladder

Kidney(L)

Kidney(R)

Liver

Pancreas

Spleen

Stomach

DARR22

69.77

-

74.74

53.77

72.31

73.24

94.08

54.18

89.90

45.96

R50 U-Net3

74.68

36.87

87.74

63.66

80.6

78.19

93.74

56.9

85.87

74.16

U-Net3

76.85

39.70

89.07

69.72

77.77

68.6

93.43

53.98

86.67

75.58

R50 Att-UNet45

75.57

36.97

55.92

63.91

79.2

72.71

93.56

49.37

87.19

74.95

Att-UNet45

77.77

36.02

89.55

68.88

68.88

71.11

93.57

58.04

58.04

75.75

R50 ViT15

71.29

32.87

73.73

55.13

75.8

72.2

91.51

45.99

81.99

73.95

TransUnet42

77.48

31.69

87.23

63.13

81.87

77.02

94.08

55.86

85.08

75.62

DeepLabv3+32

77.63

39.95

88.04

66.51

82.76

74.21

91.23

58.32

87.43

73.53

DuAT46

76.43

28.69

88.96

66.42

77.43

74.31

92.46

55.47

82.45

73.97

DAE-Former47

77.33

27.38

84.39

67.39

78.29

75.82

93.71

56.85

86.73

75.47

TransAttUnet48

78.38

29.32

86.12

68.74

83.27

76.31

92.08

57.21

86.45

76.89

TransClaw U-Net43

78.09

26.38

87.87

61.38

84.83

77.36

93.28

57.65

87.74

74.55

Ours

78.40

18.44

85.71

62.58

85.10

77.76

93.98

58.16

75.33

78.74