Table 4 Comparison with existing methods on dataset 3.

From: MAN-GAN: a mask-adaptive normalization based generative adversarial networks for liver multi-phase CT image generation

Methods

Mode conversion

P to AP

P to PVP

P to DP

PSNR

SSIM

PSNR

SSIM

PSNR

SSIM

pix2pix10

23.26 ± 2.90

0.74 ± 0.08

23.26 ± 2.93

0.74 ± 0.08

22.14 ± 3.50

0.70 ± 0.10

CycleGAN13

23.20 ± 3.11

0.74 ± 0.08

22.76 ± 2.95

0.73 ± 0.08

22.01 ± 3.70

0.70 ± 0.10

CUT12

22.65 ± 2.72

0.73 ± 0.08

21.65 ± 2.39

0.71 ± 0.08

21.48 ± 3.24

0.69 ± 0.10

F-LSeSim11

23.20 ± 2.81

0.73 ± 0.08

22.76 ± 2.76

0.72 ± 0.08

22.03 ± 3.50

0.70 ± 0.10

DINO9

23.36 ± 2.81

0.73 ± 0.07

22.47 ± 2.64

0.71 ± 0.07

21.09 ± 3.17

0.67 ± 0.09

CHAN8

23.14 ± 2.77

0.73 ± 0.08

22.74 ± 2.69

0.73 ± 0.08

21.72 ± 3.28

0.70 ± 0.10

Sun et al.21

23.23 ± 2.83

0.73 ± 0.08

22.77 ± 2.72

0.72 ± 0.08

21.73 ± 3.30

0.69 ± 0.10

Chen et al.22

23.73 ± 2.90

0.75 ± 0.07

23.18 ± 2.80

0.74 ± 0.07

22.38 ± 3.54

0.71 ± 0.10

MAN-GAN

23.92 ± 3.11

0.76 ± 0.08

23.31 ± 3.0

0.74 ± 0.08

22.50 ± 3.70

0.72 ± 0.10

  1. Significant values are in bold.
  2. Values were presented as mean ± SD.
Back to article page