D. Parpulov, A. Samorodov, D. Makhov, E. Slavnova, N. Volchenko, V. Iglovikov
{"title":"卷积神经网络在免疫细胞化学研究中的应用","authors":"D. Parpulov, A. Samorodov, D. Makhov, E. Slavnova, N. Volchenko, V. Iglovikov","doi":"10.1109/USBEREIT.2018.8384557","DOIUrl":null,"url":null,"abstract":"HER2/neu status of breast cancer is important for treatment strategy choice, but nowadays it is evaluated manually by pathologist. The automation of this procedure is an urgent task, because it will allow to free a pathologist from routine work. But the problem of cells segmentation is difficult for classical methods of computer vision due to the frequent presence of erythrocytic background and non-cellular elements at specimen' image. In this work we propose segmentation algorithm, based on deep convolutional neural networks. Is it has been shown that using this approach, it's possible to get better results, than using classical computer vision algorithms.","PeriodicalId":176222,"journal":{"name":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Convolutional neural network application for cells segmentation in immunocytochemical study\",\"authors\":\"D. Parpulov, A. Samorodov, D. Makhov, E. Slavnova, N. Volchenko, V. Iglovikov\",\"doi\":\"10.1109/USBEREIT.2018.8384557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"HER2/neu status of breast cancer is important for treatment strategy choice, but nowadays it is evaluated manually by pathologist. The automation of this procedure is an urgent task, because it will allow to free a pathologist from routine work. But the problem of cells segmentation is difficult for classical methods of computer vision due to the frequent presence of erythrocytic background and non-cellular elements at specimen' image. In this work we propose segmentation algorithm, based on deep convolutional neural networks. Is it has been shown that using this approach, it's possible to get better results, than using classical computer vision algorithms.\",\"PeriodicalId\":176222,\"journal\":{\"name\":\"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/USBEREIT.2018.8384557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/USBEREIT.2018.8384557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Convolutional neural network application for cells segmentation in immunocytochemical study
HER2/neu status of breast cancer is important for treatment strategy choice, but nowadays it is evaluated manually by pathologist. The automation of this procedure is an urgent task, because it will allow to free a pathologist from routine work. But the problem of cells segmentation is difficult for classical methods of computer vision due to the frequent presence of erythrocytic background and non-cellular elements at specimen' image. In this work we propose segmentation algorithm, based on deep convolutional neural networks. Is it has been shown that using this approach, it's possible to get better results, than using classical computer vision algorithms.