Junfang Yan, Xue Qin, C. Qiao, Jiawei Zhu, Lina Song, Mi Yang, Shaobin Wang, Lu Bai, Zhikai Liu, J. Qiu
{"title":"在接受阴道术后近距离放疗的妇科癌症患者中,使用域对抗神经网络进行临床靶体积的自动分割","authors":"Junfang Yan, Xue Qin, C. Qiao, Jiawei Zhu, Lina Song, Mi Yang, Shaobin Wang, Lu Bai, Zhikai Liu, J. Qiu","doi":"10.1002/pro6.1206","DOIUrl":null,"url":null,"abstract":"For postoperative vaginal brachytherapy (POVBT), the diversity of applicators complicates the creation of a generalized auto‐segmentation model, and creating models for each applicator seems difficult due to the large amount of data required. We construct an auto‐segmentation model of POVBT using small data via domain‐adversarial neural networks (DANNs).","PeriodicalId":32406,"journal":{"name":"Precision Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Auto‐segmentation of the clinical target volume using a domain‐adversarial neural network in patients with gynaecological cancer undergoing postoperative vaginal brachytherapy\",\"authors\":\"Junfang Yan, Xue Qin, C. Qiao, Jiawei Zhu, Lina Song, Mi Yang, Shaobin Wang, Lu Bai, Zhikai Liu, J. Qiu\",\"doi\":\"10.1002/pro6.1206\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For postoperative vaginal brachytherapy (POVBT), the diversity of applicators complicates the creation of a generalized auto‐segmentation model, and creating models for each applicator seems difficult due to the large amount of data required. We construct an auto‐segmentation model of POVBT using small data via domain‐adversarial neural networks (DANNs).\",\"PeriodicalId\":32406,\"journal\":{\"name\":\"Precision Radiation Oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Precision Radiation Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/pro6.1206\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Radiation Oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/pro6.1206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Auto‐segmentation of the clinical target volume using a domain‐adversarial neural network in patients with gynaecological cancer undergoing postoperative vaginal brachytherapy
For postoperative vaginal brachytherapy (POVBT), the diversity of applicators complicates the creation of a generalized auto‐segmentation model, and creating models for each applicator seems difficult due to the large amount of data required. We construct an auto‐segmentation model of POVBT using small data via domain‐adversarial neural networks (DANNs).