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 3 不同标签数量占比下使用3D scSE-UNet进行全监督和半监督的分割性能比较

Tab.3 Comparison of full supervised and semi supervised segmentation performance using 3D scSE-UNet under different proportions of labels

监督方式 L U LP/% DSC SEN PPV VOE/% RVD/% ASD/mm RMSD/mm MSD/mm 全监督 10 − − 0.896±0.053 0.934±0.054 0.867±0.082 11.283±10.339 12.333±12.343 9.203±7.892 21.924±17.558 140.408±63.219 半监督 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 全监督 20 − − 0.914±0.065 0.941±0.060 0.893±0.086 7.860±10.219 8.721±12.834 5.882±8.069 13.767±15.446 115.778±69.480 半监督 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 全监督 30 − − 0.925±0.045 0.953±0.057 0.903±0.067 7.786±9.618 8.356±10.889 4.301±2.755 10.548±5.728 81.865±25.524 半监督 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 全监督 40 − − 0.937±0.039 0.950±0.036 0.928±0.064 5.549±7.023 5.847±7.830 3.821±3.949 6.258±10.666 59.050±67.107 半监督 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 全监督 50 − − 0.943±0.026 0.960±0.028 0.929±0.044 4.717±5.280 4.912±5.702 2.230±1.212 4.452±2.651 36.582±14.887 半监督 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