Table 3 Registration performance comparison with SOTAs using a one-step learning strategy (without one-cycle transfer training applied) and using a two-step learning strategy (with one-cycle transfer learning applied).
From: A two-step deep learning method for 3DCT-2DUS kidney registration during breathing
Metric (mm) | One-step learning | Two-step learning | |||
|---|---|---|---|---|---|
CT–CT | CT–US | CT–CT | CT–US | ||
Ours | HD | 11.59 ± 1.57 | 6.71 ± 1.06 | 3.97 ± 1.37 | 3.80 ± 0.87 |
MCD | 4.09 ± 0.65 | 2.03 ± 0.29 | 1.15 ± 0.57 | 0.94 ± 0.18 | |
VoxelMorph | HD | 12.21 ± 1.87 | 5.99 ± 0.93 | 8.15 ± 3.94 | 4.16 ± 0.97 |
MCD | 4.38 ± 0.72 | 1.82 ± 0.28 | 2.71 ± 1.48 | 1.05 ± 0.21 | |
C2FViT | HD | 10.36 ± 1.77 | 6.42 ± 0.84 | 9.93 ± 1.72 | 5.64 ± 0.90 |
MCD | 3.74 ± 0.91 | 1.93 ± 0.50 | 3.48 ± 0.78 | 1.45 ± 0.42 | |
VTN-Affine | HD | 12.18 ± 1.86 | 5.58 ± 1.01 | 7.66 ± 2.95 | 4.14 ± 1.15 |
MCD | 4.21 ± 0.71 | 1.45 ± 0.35 | 2.45 ± 1.10 | 1.02 ± 0.20 | |
ConvNet-Affine | HD | 11.58 ± 2.43 | 6.72 ± 1.34 | 4.56 ± 1.55 | 3.89 ± 0.91 |
MCD | 4.36 ± 1.11 | 2.07 ± 0.51 | 1.46 ± 0.62 | 1.00 ± 0.23 | |