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2 不同配准模型在LPBA40数据集的定量结果

Table 2 Quantitative results of different registration models on the LPBA40 dataset

Model DSC HD95/mm %|Jϕ|≤0/% Params/k Flops/G Memory/MB Time/s SyN 0.670 ± 0.032 1.105 ± 0.180 <0.001 N/A N/A 204 108.688 NiftyReg 0.660 ± 0.018 1.312 ± 0.401 <0.001 N/A N/A 204 32.996 VoxelMorph-1 0.650 ± 0.027 1.310 ± 0.399 0.717 ± 0.302 274.39 218.09 8080 0.207 VoxelMorph-2 0.651 ± 0.029 1.143 ± 0.261 0.665 ± 0.285 301.41 285.94 9804 0.242 CycleMorph 0.670 ± 0.026 1.143 ± 0.261 0.625 ± 0.236 361.30 90.73 17192 0.221 Vit-V-Net 0.636 ± 0.029 1.258 ± 0.409 0.586 ± 0.268 31560.08 279.31 8044 0.225 TransMorph 0.668 ± 0.027 1.177 ± 0.339 0.630 ± 0.240 46771.25 511.97 11978 0.257 MDDA-Net （本文模型） 0.673 ± 0.023 1.104 ± 0.179 0.507 ± 0.139 439.18 415.68 11954 0.237