{"title":"Amira™软件中大型多通道时间序列数据的深度学习细胞分割","authors":"J. Giesebrecht","doi":"10.22443/rms.elmi2021.53","DOIUrl":null,"url":null,"abstract":"Quantitative live cell imaging has been widely used to study various dynamical processes in cell biology. Fluorescence microscopy, a popular imaging modality for live cell imaging, has been used to monitor the dynamics of specific molecules in live cells. However, the fluorescence live cell images are highly prone to noise, low contrast, and uneven illumination. These often lead to erroneous cell segmentation, hindering quantitative analyses of dynamical cellular processes.","PeriodicalId":334941,"journal":{"name":"Proceedings of the European Light Microscopy Initiative 2021","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Learning for Cell Segmentation of Large Multi-Channels Time-Series Data in the Amira™ Software\",\"authors\":\"J. Giesebrecht\",\"doi\":\"10.22443/rms.elmi2021.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantitative live cell imaging has been widely used to study various dynamical processes in cell biology. Fluorescence microscopy, a popular imaging modality for live cell imaging, has been used to monitor the dynamics of specific molecules in live cells. However, the fluorescence live cell images are highly prone to noise, low contrast, and uneven illumination. These often lead to erroneous cell segmentation, hindering quantitative analyses of dynamical cellular processes.\",\"PeriodicalId\":334941,\"journal\":{\"name\":\"Proceedings of the European Light Microscopy Initiative 2021\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the European Light Microscopy Initiative 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22443/rms.elmi2021.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the European Light Microscopy Initiative 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22443/rms.elmi2021.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learning for Cell Segmentation of Large Multi-Channels Time-Series Data in the Amira™ Software
Quantitative live cell imaging has been widely used to study various dynamical processes in cell biology. Fluorescence microscopy, a popular imaging modality for live cell imaging, has been used to monitor the dynamics of specific molecules in live cells. However, the fluorescence live cell images are highly prone to noise, low contrast, and uneven illumination. These often lead to erroneous cell segmentation, hindering quantitative analyses of dynamical cellular processes.
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