{"title":"CT三维总体病灶分割","authors":"M. Jolly, L. Grady","doi":"10.1109/ISBI.2008.4541116","DOIUrl":null,"url":null,"abstract":"This paper describes a general purpose algorithm to segment any kind of lesions in CT images. The algorithm expects a click or a stroke inside the lesion from the user and learns gray level properties on the fly. It then uses the random walker algorithm and combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion. Quantitative evaluation on 293 lesions demonstrates that the method is ready for clinical use.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"48","resultStr":"{\"title\":\"3D general lesion segmentation in CT\",\"authors\":\"M. Jolly, L. Grady\",\"doi\":\"10.1109/ISBI.2008.4541116\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a general purpose algorithm to segment any kind of lesions in CT images. The algorithm expects a click or a stroke inside the lesion from the user and learns gray level properties on the fly. It then uses the random walker algorithm and combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion. Quantitative evaluation on 293 lesions demonstrates that the method is ready for clinical use.\",\"PeriodicalId\":184204,\"journal\":{\"name\":\"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"48\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBI.2008.4541116\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2008.4541116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper describes a general purpose algorithm to segment any kind of lesions in CT images. The algorithm expects a click or a stroke inside the lesion from the user and learns gray level properties on the fly. It then uses the random walker algorithm and combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion. Quantitative evaluation on 293 lesions demonstrates that the method is ready for clinical use.
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