The Comparison of Automatic Segmentation Effects for Organs at Risk in Cervical Cancer Radiotherapy Based on UNet and ResUNet++Models
Objective To compare the automatic segmentation effects for organs at risk in cervical cancer radiotherapy based on UNet and ResUNet++models.Methods UNet and ResUNet++models are built on the PyTorch platform.With the treatment plan of 232 cervical cancer patients underwent radiation therapy in hospitals from June 2023 to February 2024,among them,194 cases were planned for model training and validation,and 38 cases were planned for testing.Organs at risk include the liver,bladder,rectum,spinal cord,kidney,femur,and femoral head.The 3 Dimensions dice similarity coefficient(3D-DSC)and hausdorff distance 95th percentile(HD95%)were used to evaluate the segmentation results of the two models.Results According to the segmentation results of UNet model,the 3D-DSC of rectum was relatively low,which was 0.847(0.809,0.868).The 3D-DSC values of other organs at risk were higher,ranging from 0.938(0.929,0.945)to 0.978(0.975,0.979).The HD95%of liver and bladder were higher,which were 11.449(8.822,13.740)and 13.038(11.365,15.699),respectively.The HD95%of other organs at risk were within the range of 2.638(2.341,2.812)to 6.424(5.502,8.071).The segmentation results of the ResUNet++model showed that the 3D-DSC of the rectum was relatively low,which was 0.792(0.707,0.855).The 3D-DSC of other organs at risk is relatively high,ranging from 0.938(0.929,0.945)to 0.978(0.975,0.979);The HD95%of the liver and bladder were relatively high,which were 10.954(8.552,13.460)and 13.114(11.066,16.664),respectively.The HD95%of other organs at risk ranged from 2.640(2.161,3.029)to 6.824(6.050,8.066).There were no statistically significant differences in the 3D-DSC of the liver and right kidney segmented by the two models(P>0.05);The differences in 3D-DSC of other organs segmented by the two models were statistically significant(P<0.05).The HD95%of the left femoral head segmented by the UNet model was lower than that of the ResUNet++model,and the difference was statistically significant(P<0.05);There was no significant difference in HD95%of other organs(P>0.05).Conclusion Both UNet and ResUNet++models can automatically segment organs at risk in cervical cancer radiotherapy,and the overall segmentation effect of the UNet model is better than that of the ResUNet++model from the data evaluation.
UNet modelResUNet++modelCervical cancerOrgans at riskRadiation therapy
NETL
NSTL
万方数据
