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Affine Medical Image Registration With Coarse-To-Fine Vision Transformer

CVPR 2022 · Architectures and efficiency › CNN design · approach: Compact CNN architectures · 97 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use C2FViT instead of the strongest listed CNN baselineAffine registration · Template-Matching Normalization (MNI152), without CoM initializationDSC4: 0.67 ± 0.06 -> 0.71 ± 0.06Helped
Use C2FViT instead of the strongest listed CNN baselineAffine registration · Atlas-Based Registration (OASIS), without CoM initializationDSC23: 0.57 ± 0.07 -> 0.64 ± 0.06Helped
Use C2FViT instead of ConvNet-AffineAffine registration · Atlas-Based Registration (OASIS train → LPBA test), without CoM initializationDSC3: 0.36 ± 0.07 -> 0.47 ± 0.04Helped
Add center-of-mass initialization (ablation)C2FViT · Template-Matching Normalization (MNI152)Initial DSC4: 0.14 ± 0.12 -> 0.49 ± 0.11Helped
Add progressive spatial transformation (ablation)C2FViT · Atlas-Based Registration (OASIS)DSC23: 0.62 -> 0.64Helped
Add center-of-mass initialization (ablation)C2FViT · Atlas-Based Registration (OASIS)DSC23: 0.64 -> 0.66Helped
Add semi-supervised learning (ablation)C2FViT · Atlas-Based Registration (OASIS)DSC23: 0.66 -> 0.69Helped
Use semi-supervised training instead of unsupervised training (ablation)C2FViT · Atlas-Based Registration (OASIS train → LPBA test), with CoM initializationDSC3: 0.54 ± 0.03 -> 0.51 ± 0.03Hurt
Predict decoupled geometric parameters rather than directly estimating the affine matrix (ablation)C2FViT · Atlas-Based Registration (OASIS)DSC23: 0.63 ± 0.06 -> 0.64 ± 0.06Helped

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