Dandan Yuan, Weiwei Du, Xiaojie Duan, Jianming Wang, Yanhe Ma, Hong Zhang
{"title":"用半监督学习方法检测CT体积数据中包含毛玻璃不透明结节的切片","authors":"Dandan Yuan, Weiwei Du, Xiaojie Duan, Jianming Wang, Yanhe Ma, Hong Zhang","doi":"10.1109/SNPD.2017.8022778","DOIUrl":null,"url":null,"abstract":"The features of GGO nodules need to be obtained such as volume, mean, variance of Ground-Glass Opacity Nodules by boundaries of GGO nodules to judge malignant or benign of lung tumors. However, radiologists need to look for the slices including the GGO nodule in CT volume data. It is time-consuming. This paper proposes a semi-supervised learning method based on the label propagation. First, a GGO nodule was labeled in one slice. Secondly, similarities were found by comparing with the labeled GGO nodule using the values of pixels. Finally, the GGO nodule of the other slices was labeled by iteration. Experimental results showed that the approach of this paper can find slices including the GGO nodule. The approach is better than the nearest neighbor algorithm in performance.","PeriodicalId":186094,"journal":{"name":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Detection of slices including a ground-glass opacity nodule in CT volume data with semi-supervised learning\",\"authors\":\"Dandan Yuan, Weiwei Du, Xiaojie Duan, Jianming Wang, Yanhe Ma, Hong Zhang\",\"doi\":\"10.1109/SNPD.2017.8022778\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The features of GGO nodules need to be obtained such as volume, mean, variance of Ground-Glass Opacity Nodules by boundaries of GGO nodules to judge malignant or benign of lung tumors. However, radiologists need to look for the slices including the GGO nodule in CT volume data. It is time-consuming. This paper proposes a semi-supervised learning method based on the label propagation. First, a GGO nodule was labeled in one slice. Secondly, similarities were found by comparing with the labeled GGO nodule using the values of pixels. Finally, the GGO nodule of the other slices was labeled by iteration. Experimental results showed that the approach of this paper can find slices including the GGO nodule. The approach is better than the nearest neighbor algorithm in performance.\",\"PeriodicalId\":186094,\"journal\":{\"name\":\"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SNPD.2017.8022778\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD.2017.8022778","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of slices including a ground-glass opacity nodule in CT volume data with semi-supervised learning
The features of GGO nodules need to be obtained such as volume, mean, variance of Ground-Glass Opacity Nodules by boundaries of GGO nodules to judge malignant or benign of lung tumors. However, radiologists need to look for the slices including the GGO nodule in CT volume data. It is time-consuming. This paper proposes a semi-supervised learning method based on the label propagation. First, a GGO nodule was labeled in one slice. Secondly, similarities were found by comparing with the labeled GGO nodule using the values of pixels. Finally, the GGO nodule of the other slices was labeled by iteration. Experimental results showed that the approach of this paper can find slices including the GGO nodule. The approach is better than the nearest neighbor algorithm in performance.