Liang Zhao, Yu-Hsiang Shao, Chaoran Jia, Jiajun Ma
{"title":"时间序列肺癌CT数据集","authors":"Liang Zhao, Yu-Hsiang Shao, Chaoran Jia, Jiajun Ma","doi":"10.1109/BIBM55620.2022.9995198","DOIUrl":null,"url":null,"abstract":"In order to better explore the evolution process of lung nodules in lung cancer patients, we collect lung CT data at multiple time points of lung cancer patients, track and mark the CT positions of the same lung nodules in lung cancer patients at different time points, and make time-series CT data sets of lung cancer patients. After that, 3D-UNet model is used to detect lung nodules on our data set. Experiment proves the effectiveness and availability of the data set, and also proved that the image data at multiple time points could improve the accuracy of the model’s identification of lung nodules.","PeriodicalId":210337,"journal":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time-series lung cancer CT dataset\",\"authors\":\"Liang Zhao, Yu-Hsiang Shao, Chaoran Jia, Jiajun Ma\",\"doi\":\"10.1109/BIBM55620.2022.9995198\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to better explore the evolution process of lung nodules in lung cancer patients, we collect lung CT data at multiple time points of lung cancer patients, track and mark the CT positions of the same lung nodules in lung cancer patients at different time points, and make time-series CT data sets of lung cancer patients. After that, 3D-UNet model is used to detect lung nodules on our data set. Experiment proves the effectiveness and availability of the data set, and also proved that the image data at multiple time points could improve the accuracy of the model’s identification of lung nodules.\",\"PeriodicalId\":210337,\"journal\":{\"name\":\"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBM55620.2022.9995198\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM55620.2022.9995198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In order to better explore the evolution process of lung nodules in lung cancer patients, we collect lung CT data at multiple time points of lung cancer patients, track and mark the CT positions of the same lung nodules in lung cancer patients at different time points, and make time-series CT data sets of lung cancer patients. After that, 3D-UNet model is used to detect lung nodules on our data set. Experiment proves the effectiveness and availability of the data set, and also proved that the image data at multiple time points could improve the accuracy of the model’s identification of lung nodules.