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4 与同类型模块的对比

Table 4 Comparison with similar modules

Model DSC HD95/mm %|Jϕ|≤0/% Params/k Flops/G Memory/MB Time/s(GPU) +ASPP 0.751 ± 0.028 2.124 ± 0.782 1.506 ± 0.352 466.05 591.62 18978 0.374 +MDRC 0.751 ± 0.027 2.107 ± 0.765 1.384 ± 0.355 439.18 581.96 16572 0.364 +CBAM 0.745 ± 0.026 2.170 ± 0.826 1.448 ± 0.344 439.00 581.65 16310 0.377 +SE 0.747 ± 0.026 2.151 ± 0.834 1.394 ± 0.328 438.79 580.95 15630 0.344 +CSDA 0.751 ± 0.027 2.107 ± 0.765 1.384 ± 0.355 439.18 581.96 16572 0.364 +nearest 0.739 ± 0.029 2.162 ± 0.793 1.496 ± 0.344 414.22 576.33 14732 0.216 +trilinear 0.740 ± 0.023 2.126 ± 0.766 1.434 ± 0.379 414.22 576.33 14732 0.351 +Dysample 0.751 ± 0.027 2.107 ± 0.765 1.384 ± 0.355 439.18 581.96 16572 0.364