基于多尺度膨胀残差和双重注意力的可变形配准网络

羊晶晶, 王远军

Deformable Registration Network Based on Multi-scale Dilated Residual and Dual Attention

YANG Jingjing, WANG Yuanjun

表1 不同配准模型在IXI数据集的定量结果

Table 1 Quantitative results of different registration models on the IXI dataset

Model DSC HD95/mm %|Jϕ|≤0/% Params/k Flops/G Memory/MB Time/s SyN 0.641 ± 0.040 2.118 ± 0.754 <0.001 N/A N/A 254 189.172 NiftyReg 0.635 ± 0.066 2.166 ± 0.655 0.006 ± 0.016 N/A N/A 254 69.962 VoxelMorph-1 0.722 ± 0.029 2.287 ± 0.805 1.668 ± 0.345 274.39 305.32 9128 0.378 VoxelMorph-2 0.728 ± 0.030 2.278 ± 0.797 1.580 ± 0.344 301.41 400.31 10524 0.417 CycleMorph 0.738 ± 0.028 2.156 ± 0.760 1.732 ± 0.365 361.30 127.02 23724 0.483 Vit-V-Net 0.643 ± 0.041 2.399 ± 0.854 1.391 ± 0.284 31560.08 391.12 11370 0.317 TransMorph 0.744 ± 0.031 2.273 ± 0.786 1.540 ± 0.335 46771.25 714.16 16840 0.450 MDDA-Net （本文模型） 0.751 ± 0.027 2.107 ± 0.765 1.384 ± 0.355 439.18 581.96 16572 0.364