{"title":"使用GM-PHD跟踪细胞运动","authors":"R. Juang, A. Levchenko, P. Burlina","doi":"10.1109/ISBI.2009.5193262","DOIUrl":null,"url":null,"abstract":"We present a method for tracking the movement of multiple cells and their lineage. We use the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, a multi-target tracking algorithm, to track the motion of multiple cells over time and to keep track of the lineage of cells as they spawn. We describe a spawning model for the GM-PHD filter as well as modifications to the original GM-PHD algorithm to track lineage. Experimental results are provided illustrating the approach for dense cell colonies.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"264 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"44","resultStr":"{\"title\":\"Tracking cell motion using GM-PHD\",\"authors\":\"R. Juang, A. Levchenko, P. Burlina\",\"doi\":\"10.1109/ISBI.2009.5193262\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method for tracking the movement of multiple cells and their lineage. We use the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, a multi-target tracking algorithm, to track the motion of multiple cells over time and to keep track of the lineage of cells as they spawn. We describe a spawning model for the GM-PHD filter as well as modifications to the original GM-PHD algorithm to track lineage. Experimental results are provided illustrating the approach for dense cell colonies.\",\"PeriodicalId\":272938,\"journal\":{\"name\":\"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro\",\"volume\":\"264 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"44\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBI.2009.5193262\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2009.5193262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present a method for tracking the movement of multiple cells and their lineage. We use the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, a multi-target tracking algorithm, to track the motion of multiple cells over time and to keep track of the lineage of cells as they spawn. We describe a spawning model for the GM-PHD filter as well as modifications to the original GM-PHD algorithm to track lineage. Experimental results are provided illustrating the approach for dense cell colonies.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
