宫颈癌后装治疗中基于U-net的自动施源器分割

目的 在CT引导的宫颈癌三维后装治疗计划制定中,应用U-net模型完成施源器的自动分割.方法 基于U-net网络创建深度学习模型,将2019年12月—2020年10月的27例宫颈癌患者数据经过预处理后写入数据集,按照15:2:10的比例将数据集划分为训练集、验证集和测试集.将训练集和验证集数据放入模型中训练并验证,并将...

Automatic applicator segmentation based on U-net model in the brachytherapy of cervical cancer

Objective In the preparation of CT-guided three-dimensional brachytherapy treatment plan for cervical cancer,the U-net model was used to complete the automatic segmentation of the applicator.Methods A deep learning model was created based on the U-net network.The data of 27 cervical cancer patients from December 2019 to October 2020 were preprocessed and written into the data set,which was divided into ratios of 15∶2∶10 for a training set,a verification set and a test set,respectively.The training set and verification set were put into the model for training and verification.The test set was applied to the trained neural network to segment the applicators.Dice similarity coefficient(DSC),95th percentile house dove distance(HD95),relative volume diffidence(RVD),precision and recall were used to evaluate this model.Results The average DSC of 10 patients in the test set was 0.90,HD95 was 1.26mm,RVD was-0.06,the accuracy rate was 0.94,the recall rate was 0.88,and the segmentation time was 5s.Conclusion In this study,the U-net network was used to realize the automatic segmentation of the applicator in the three-dimensional brachytherapy treatment plan for cervical cancer.It can be applied to the reconstruction of the applicator,which has great significance in realizing the automation of clinical planning.