{"title":"基于 PET-CT 图像上肿瘤的 3D Alpha Matting 协同分割。","authors":"Zisha Zhong, Yusung Kim, John Buatti, Xiaodong Wu","doi":"10.1007/978-3-319-67564-0_4","DOIUrl":null,"url":null,"abstract":"<p><p>Positron emission tomography - computed tomography (PET-CT) has been widely used in modern cancer imaging. Accurate tumor delineation from PET and CT plays an important role in radiation therapy. The PET-CT co-segmentation technique, which makes use of advantages of both modalities, has achieved impressive performance for tumor delineation. In this work, we propose a novel 3D image matting based semi-automated co-segmentation method for tumor delineation on dual PET-CT scans. The \"matte\" values generated by 3D image matting are employed to compute the region costs for the graph based co-segmentation. Compared to previous PET-CT co-segmentation methods, our method is completely data-driven in the design of cost functions, thus using much less hyper-parameters in our segmentation model. Comparative experiments on 54 PET-CT scans of lung cancer patients demonstrated the effectiveness of our method.</p>","PeriodicalId":15615,"journal":{"name":"Journal of Dairy Research","volume":"58 1","pages":"31-42"},"PeriodicalIF":1.6000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886662/pdf/","citationCount":"0","resultStr":"{\"title\":\"3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images.\",\"authors\":\"Zisha Zhong, Yusung Kim, John Buatti, Xiaodong Wu\",\"doi\":\"10.1007/978-3-319-67564-0_4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Positron emission tomography - computed tomography (PET-CT) has been widely used in modern cancer imaging. Accurate tumor delineation from PET and CT plays an important role in radiation therapy. The PET-CT co-segmentation technique, which makes use of advantages of both modalities, has achieved impressive performance for tumor delineation. In this work, we propose a novel 3D image matting based semi-automated co-segmentation method for tumor delineation on dual PET-CT scans. The \\\"matte\\\" values generated by 3D image matting are employed to compute the region costs for the graph based co-segmentation. Compared to previous PET-CT co-segmentation methods, our method is completely data-driven in the design of cost functions, thus using much less hyper-parameters in our segmentation model. Comparative experiments on 54 PET-CT scans of lung cancer patients demonstrated the effectiveness of our method.</p>\",\"PeriodicalId\":15615,\"journal\":{\"name\":\"Journal of Dairy Research\",\"volume\":\"58 1\",\"pages\":\"31-42\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886662/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Dairy Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-3-319-67564-0_4\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/9/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Dairy Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-319-67564-0_4","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/9/9 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images.
Positron emission tomography - computed tomography (PET-CT) has been widely used in modern cancer imaging. Accurate tumor delineation from PET and CT plays an important role in radiation therapy. The PET-CT co-segmentation technique, which makes use of advantages of both modalities, has achieved impressive performance for tumor delineation. In this work, we propose a novel 3D image matting based semi-automated co-segmentation method for tumor delineation on dual PET-CT scans. The "matte" values generated by 3D image matting are employed to compute the region costs for the graph based co-segmentation. Compared to previous PET-CT co-segmentation methods, our method is completely data-driven in the design of cost functions, thus using much less hyper-parameters in our segmentation model. Comparative experiments on 54 PET-CT scans of lung cancer patients demonstrated the effectiveness of our method.
期刊介绍:
The Journal of Dairy Research is an international Journal of high-standing that publishes original scientific research on all aspects of the biology, wellbeing and technology of lactating animals and the foods they produce. The Journal’s ability to cover the entire dairy foods chain is a major strength. Cross-disciplinary research is particularly welcomed, as is comparative lactation research in different dairy and non-dairy species and research dealing with consumer health aspects of dairy products. Journal of Dairy Research: an international Journal of the lactation sciences.