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Table 1 Table containing metrics for volume measurements (mL) obtained from label maps

From: Combining generative modelling and semi-supervised domain adaptation for whole heart cardiovascular magnetic resonance angiography segmentation

Method

SUP

LV

LVM

RV

LA

RA

AO

GT

 

99.64 ± 33.14

115.51 ± 26.63

117.32 ± 31.92

57.45 ± 20.31

70.18 ± 21.97

35.90 ± 9.23

VAE

0%

35.6 ± 17.7 (40.5)

37.9 ± 28.6 (47.3)

63.3 ± 25.9 (68.2)

32.2 ± 15.5 (35.7)

26.9 ± 12.4 (29.6)

13.7 ± 9.1 (16.4)

10%

12.6 ± 10.6 (16.4)

31.4 ± 16.1 (35.2)

39.7 ± 19.9 (44.3)

20.2 ± 16.4 (25.9)

19.4 ± 10.7 (22.1)

8.6 ± 9.8 (13.0)

20%

6.3 ± 9.2 (11.1)

28.2 ± 13.1 (31.1)

33.8 ± 20.0 (39.2)

13.9 ± 14.5 (20.0)

18.3 ± 9.5 (20.6)

9.3 ± 5.4 (10.7)

30%

3.4 ± 8.6 (9.2)

26.5 ± 11.5 (28.8)

24.5 ± 15.7 (29.0)

12.2 ± 12.9 (17.7)

16.3 ± 11.3 (19.8)

7.5 ± 5.4 (9.2)

GAN

0%

12.7 ± 12.4 (17.6)

3.9 ± 14.1 (14.5)

20.8 ± 13.1 (24.5)

2.4 ± 16.3 (16.2)

− 0.8 ± 9.2 (9.2)

1.8 ± 4.5 (4.8)

10%

2.3 ± 7.2 (7.5)

2.1 ± 8.8 (8.9)

7.4 ± 11.2 (13.4)

4.7 ± 13.7 (14.3)

1.2 ± 10.4 (10.3)

1.0 ± 5.2 (5.2)

20%

0.0 ± 7.6 (7.5)

− 1.4 ± 12.4 (12.3)

3.6 ± 11.2 (11.6)

2.0 ± 13.8 (13.8)

− 0.4 ± 9.7 (9.6)

0.6 ± 5.4 (5.4)

30%

− 1.8 ± 8.1 (8.3)

0.6 ± 9.5 (9.4)

2.2 ± 10.3 (10.5)

1.9 ± 10.9 (11.0)

0.6 ± 9.6 (9.6)

0.0 ± 5.4 (5.4)

UNet

10%

47.2 ± 75.1 (87.9)

− 5.1 ± 27.4 (27.6)

− 9.8 ± 25.6 (27.1)

7.6 ± 42.5 (42.7)

7.4 ± 32.9 (33.3)

− 5.9 ± 9.3 (10.9)

20%

97.2 ± 73.2 (121.2)

0.3 ± 14.9 (14.7)

− 9.4 ± 18.1 (20.3)

− 1.7 ± 19.7 (19.5)

0.4 ± 17.3 (17.1)

1.1 ± 11.1 (11.1)

30%

15.6 ± 46.6 (48.7)

6.7 ± 19.9 (20.8)

− 2.0 ± 18.5 (18.4)

3.1 ± 27.2 (27.0)

− 2.6 ± 15.5 (15.5)

− 0.3 ± 6.4 (6.4)

  1. The results are reported using avg ± std signed differences (RMSE) between ground truth volumes (top row) and predicted volumes. The second column (SUP) refers to the supervision level adopted in the experiment. The best result per each method is highlighted in bold, and the best result overall is bolditalics. HRMRA Dataset