Table 4 Our domain adaptation strategy improves results in a wide range of external test sets.

From: State-of-the-art retinal vessel segmentation with minimalistic models

Training set

DRIVE

CHASE-DB

HRF

STARE

IOSTAR

AUC

DICE

MCC

AUC

DICE

MCC

AUC

DICE

MCC

AUC

DICE

MCC

AUC

DICE

MCC

DRIVE

98.09

82.82

80.27

97.22

75.13

72.44

95.90

70.39

68.05

98.11

79.48

77.30

97.97

78.77

76.47

PSEUDO-L

98.09

82.82

80.27

97.56

76.49

74.02

96.12

71.12

68.86

98.28

79.76

77.65

98.06

78.95

76.73

Training Set

DRiDB

LES-AV

DR HAGIS

AV-WIDE

UoA-DR

AUC

DICE

MCC

AUC

DICE

MCC

AUC

DICE

MCC

AUC

DICE

MCC

AUC

DICE

MCC

DRIVE

96.17

68.45

66.62

95.45

76.60

74.32

97.17

67.92

66.79

86.54

61.51

59.02

82.32

38.29

35.51

PSEUDO-L

96.52

68.25

66.59

97.34

77.93

75.92

97.34

68.67

67.49

87.64

62.46

59.97

82.71

37.68

34.97

  1. First row: W-Net trained on DRIVE, second row (Pseudo-Labels): same model fine-tuned using the strategy illustrated in Fig. 3. Best metric marked in bold. Please note that Dice/MCC are computed in all cases from segmentations binarized using a threshold that is optimal for maximizing the Dice score in the training dataset (DRIVE).