基于级联网络的膝关节图像分割与模型构建

马岩,邢藏菊,肖亮

Knee Joint Image Segmentation and Model Construction Based on Cascaded Network

Yan MA,Cang-ju XING,Liang XIAO

表1 对本文所提方法与其它4种对比方法分割结果的评价指标的分析

Table 1 Analysis for the segmentation evaluation indicators of the proposed method and 4 comparison methods

评价指标 分割方法 统计参数 半月板 软骨 皮质骨 松质骨 脂肪 肌肉 FPR U-Net Mean 0.0003 0.0007 0.0054 0.0024 0.0091 0.0135 Std 0.0001 0.0002 0.0013 0.0007 0.0024 0.0033 U-Net (focal loss) Mean 0.0002 0.0011 0.0093 0.0024 0.0074 0.0133 Std 0.0001 0.0004 0.0019 0.0007 0.0019 0.0033 U-Net (attention) Mean 0.0003 0.0010 0.0054 0.0027 0.0105 0.0122 Std 0.0001 0.0003 0.0010 0.0007 0.0026 0.0035 U-Net++ Mean 0.0004 0.0005 0.0054 0.0022 0.0089 0.0169 Std 0.0001 0.0002 0.0010 0.0008 0.0022 0.0038 本文所提方法 Mean 0.0002 0.0013 0.0050 0.0023 0.0064 0.0132 Std 0.0001 0.0004 0.0010 0.0008 0.0015 0.0035 TPR U-Net Mean 0.6347 0.5435 0.7550 0.9333 0.8799 0.9372 Std 0.1030 0.0912 0.0583 0.0297 0.0498 0.0264 U-Net (focal loss) Mean 0.7291 0.6025 0.8053 0.9251 0.8547 0.9333 Std 0.0824 0.1005 0.0466 0.0284 0.0638 0.0242 U-Net (attention) Mean 0.7076 0.5773 0.7298 0.9375 0.8792 0.9231 Std 0.0617 0.0794 0.0437 0.0336 0.0604 0.0244 U-Net++ Mean 0.6883 0.5144 0.6800 0.9396 0.8459 0.9349 Std 0.0926 0.0959 0.0494 0.0291 0.0600 0.0185 本文所提方法 Mean 0.7562 0.7291 0.7635 0.9410 0.8803 0.9422 Std 0.0671 0.0793 0.0479 0.0250 0.0630 0.0231 DCC U-Net Mean 0.6755 0.5502 0.6980 0.9292 0.8868 0.9252 Std 0.0718 0.0499 0.0378 0.0272 0.0387 0.0224 U-Net (focal loss) Mean 0.6884 0.5505 0.6608 0.9310 0.8860 0.9234 Std 0.0520 0.0562 0.0396 0.0244 0.0491 0.0207 U-Net (attention) Mean 0.6837 0.5551 0.6866 0.9289 0.8868 0.9232 Std 0.0416 0.0416 0.0347 0.0273 0.0511 0.0235 U-Net++ Mean 0.6389 0.5618 0.6502 0.9296 0.8730 0.9118 Std 0.0568 0.0610 0.0333 0.0310 0.0529 0.0230 本文所提方法 Mean 0.7071 0.6023 0.7083 0.9351 0.8907 0.9312 Std 0.0521 0.0396 0.0293 0.0234 0.0419 0.0176 JAC U-Net Mean 0.5394 0.4092 0.5391 0.8824 0.8102 0.8677 Std 0.0699 0.0491 0.0405 0.0319 0.0596 0.0289 U-Net (focal loss) Mean 0.5484 0.4107 0.5069 0.8844 0.8055 0.8632 Std 0.0528 0.0462 0.0421 0.0362 0.0698 0.0280 U-Net (attention) Mean 0.5418 0.4077 0.5341 0.8772 0.8073 0.8653 Std 0.0414 0.0379 0.0367 0.0337 0.0691 0.3033 U-Net++ Mean 0.4931 0.3296 0.4954 0.8716 0.7851 0.8468 Std 0.0511 0.0471 0.0415 0.0364 0.0699 0.0314 本文所提方法 Mean 0.5610 0.4572 0.5600 0.8896 0.8139 0.8761 Std 0.0525 0.0373 0.0331 0.0300 0.0600 0.0251 ASD/mm U-Net Mean 1.3462 2.8589 1.2567 1.1638 1.3399 2.2038 Std 0.7835 0.8687 0.5117 0.4399 0.1666 0.6693 U-Net (focal loss) Mean 1.3125 3.1638 1.2982 1.1325 1.3147 2.2202 Std 0.6548 1.1781 0.2242 0.3914 0.3295 0.7109 U-Net (attention) Mean 1.2539 2.7799 1.2440 1.5075 1.3017 2.4292 Std 0.6181 0.8609 0.4551 0.3993 0.2916 1.3714 U-Net++ Mean 1.6536 3.6479 1.6531 1.7551 1.4824 2.9900 Std 0.6723 1.5050 1.5734 1.0557 0.2625 1.3447 本文所提方法 Mean 1.2470 2.6555 1.0716 1.0651 1.2412 1.9097 Std 0.5749 0.8258 0.3542 0.3869 0.2607 0.5289