融合多重自注意力和可变形卷积的多模态脑胶质瘤分割

赵欣,张鑫,李鑫杰,王洪凯

Multimodal Glioma Segmentation with Fusion of Multiple Self-attention and Deformable Convolutions

ZHAO Xin,ZHANG Xin,LI Xinjie,WANG Hongkai

表3 添加不同模块的Unet网络在BraTs2019数据集上的分割结果

Table 3 Indexes of segmentation results using Unet models adding with different modules on BraTs2019 dataset

网络 Dice/% Hausdorff_95/mm PPV/% Sensitivity/% WT TC ET WT TC ET WT TC ET WT TC ET Unet 83.24 84.57 76.84 2.6167 1.6551 2.7735 85.57 85.34 78.52 85.94 90.58 80.55 Unet+Res 84.81 85.35 77.32 2.5977 1.6414 2.7408 86.06 85.95 78.64 86.47 90.89 81.23 Unet+Res+DCM 86.67 85.57 78.23 2.5677 1.6143 2.4234 87.07 86.50 78.79 86.55 91.24 81.92 Unet+Res+MATM 86.88 87.24 79.45 2.5681 1.5667 2.7588 87.70 86.30 79.75 87.13 92.01 82.16 Unet+Res+DCM+MATM （本文方法） 88.15 87.98 80.46 2.5637 1.5323 2.6623 87.75 88.98 79.89 88.22 92.16 83.66