医疗卫生装备2024，Vol.45Issue(6)：59-64,6.DOI:10.19745/j.1003-8868.2024113
磁共振图像引导下前列腺癌在线自适应放疗自动勾画研究
Auto-segmentation during online adaptive MRI-guided radiotherapy for prostate cancer
摘要
Abstract
Objective To explore the effect of auto-segmentation based on deep learning(DL)and Atlas during online adaptive MRI-guided radiotherapy.Methods Totally 15 prostate cancer patients undergoing MRI-guided online adaptive radiotherapy at some hospital from January 2020 to September 2021 were selected and divided into a training set(12 cases)and a test set(3 cases)by random sampling method.With the training set data the models of clinical target volume(CTV)and organs at risk(OAR)by DL and Atlas segmentation were established,and with the test set data the two segmentation models were modified and the modification lengths were recorded.DL and Atlas segmentation methods were compared on segmentation efficiency and accuracy in terms of Dice similarity coefficient(DSC),Hausdorff distance(HD)and mean distance to agreement(MDA).A joint auto-segmentation scheme based on combined DL and Atlas was constructed with considerations on the advantages and characteristics of the two methods,which was compared with the schemes respectively based on DL or Atlas from the aspect of the time consumed for segmentation.Results Accuracy comparison showed Atlas segmentation model behaved better significantly than DL model for CTV(P＜0.05),while obviously worse than the latter for DSC and MDA in bladder and rectum(P＜0.05).The doctor took 9.4 min in average for CTV and OAR modification based on DL model and 12 min in average for Atlas-model-based modification.The joint auto-segmentation scheme only needed 8 min in average for CTV and OAR modification,which gained advantages over the schemes based on DL or Atlas.Conclusion The auto-segmentation based on combined DL and Atlas during online adaptive MRI-guided radiotherapy behaves well in low time consumption,high accuracy and efficiency.[Chinese Medical Equipment Journal,2024,45(6):59-64]关键词
磁共振图像引导放疗/DL勾画/Atlas勾画/自动勾画/前列腺癌Key words
MRI-guided radiotherapy/deep learning-based segmentation/Atlas-based segmentation/auto segmentation/prostate cancer分类
医药卫生引用本文复制引用
闫雪娜,马翔宇,曾强,门阔,陈辛元..磁共振图像引导下前列腺癌在线自适应放疗自动勾画研究[J].医疗卫生装备,2024,45(6):59-64,6.基金项目
国家自然科学基金项目(12275357) （12275357）
北京市自然科学基金项目(7222149) （7222149）
中国癌症基金会北京希望马拉松专项基金项目(LC2021A15) （LC2021A15）
中国医学科学院肿瘤医院住培教学研究课题(E2024002) （E2024002）
