基于3D scSE-UNet的肝脏CT图像半监督学习分割方法

刘清清,周志勇,范国华,钱旭升,胡冀苏,陈光强,戴亚康

Semi-supervised learning segmentation method of liver CT images based on 3D scSE-UNet

Qing-qing LIU,Zhi-yong ZHOU,Guo-hua FAN,Xu-sheng QIAN,Ji-su HU,Guang-qiang CHEN,Ya-kang DAI

表 2 不同标签数量占比下的分割性能比较

Tab.2 Comparison of segmentation performance under different proportion of labels

方法 L U LP/% DSC SEN PPV VOE/% RVD/% ASD/mm RMSD/mm MSD/mm 全监督3D UNet 100 − − 0.937±0.034 0.958±0.021 0.920±0.059 5.613±6.744 6.034±7.788 4.168±5.688 10.133±13.870 88.078±61.122 全监督3D scSE-UNet 100 − − 0.950±0.032 0.964±0.047 0.938±0.041 4.532±6.572 4.689±6.443 1.974±2.032 4.257±4.660 38.767±22.557 半监督3D scSE-UNet 50 50 50 0.948±0.023 0.960±0.024 0.937±0.041 4.162±4.696 4.262±5.078 2.037±1.123 4.227±2.571 37.608±16.748 40 60 40 0.946±0.025 0.959±0.026 0.934±0.048 5.079±5.358 4.970±6.004 2.143±1.213 4.295±2.801 36.659±16.682 30 70 30 0.941±0.032 0.959±0.047 0.925±0.045 5.130±6.832 5.176±6.853 2.431±1.702 5.014±3.798 40.554±19.559 20 80 20 0.930±0.040 0.945±0.048 0.919±0.060 6.326±8.065 6.598±8.684 2.972±2.176 5.758±4.541 43.552±21.964 10 90 10 0.902±0.049 0.943±0.052 0.874±0.078 11.337±10.201 12.419±12.079 4.403±2.736 8.000±4.958 49.131±21.866