Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/33081
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dc.contributor.authorPfaehler, Elisabeth-
dc.contributor.authorMESOTTEN, Liesbet-
dc.contributor.authorKramer, Gem-
dc.contributor.authorTHOMEER, Michiel-
dc.contributor.authorVANHOVE, Karolien-
dc.contributor.authorde Jong, Johan-
dc.contributor.authorADRIAENSENS, Peter-
dc.contributor.authorHoekstra, Otto S-
dc.contributor.authorBoellaard, Ronald-
dc.date.accessioned2021-01-13T12:27:56Z-
dc.date.available2021-01-13T12:27:56Z-
dc.date.issued2021-
dc.date.submitted2021-01-12T13:45:53Z-
dc.identifier.citationEJNMMI Research, 11 (4) (Art N° 4)-
dc.identifier.issn2191-219X-
dc.identifier.urihttp://hdl.handle.net/1942/33081-
dc.description.abstractPositron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV-including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge.-
dc.description.sponsorshipWe would like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster-
dc.language.isoen-
dc.publisher-
dc.rightsThe Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.-
dc.subject.otherConvolutional neural network-
dc.subject.otherRepeatability-
dc.subject.otherTextural segmentation-
dc.subject.otherTumor segmentation PET-
dc.titleRepeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET-
dc.typeJournal Contribution-
dc.identifier.issue4-
dc.identifier.volume11-
local.format.pages11-
local.bibliographicCitation.jcatA1-
local.publisher.placeONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.artnr4-
dc.identifier.doi10.1186/s13550-020-00744-9-
dc.identifier.pmid33409747-
dc.identifier.isiWOS:000610092100002-
local.provider.typePubMed-
local.uhasselt.uhpubyes-
local.uhasselt.internationalyes-
item.validationecoom 2022-
item.contributorPfaehler, Elisabeth-
item.contributorMESOTTEN, Liesbet-
item.contributorKramer, Gem-
item.contributorTHOMEER, Michiel-
item.contributorVANHOVE, Karolien-
item.contributorde Jong, Johan-
item.contributorADRIAENSENS, Peter-
item.contributorHoekstra, Otto S-
item.contributorBoellaard, Ronald-
item.accessRightsOpen Access-
item.fullcitationPfaehler, Elisabeth; MESOTTEN, Liesbet; Kramer, Gem; THOMEER, Michiel; VANHOVE, Karolien; de Jong, Johan; ADRIAENSENS, Peter; Hoekstra, Otto S & Boellaard, Ronald (2021) Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET. In: EJNMMI Research, 11 (4) (Art N° 4).-
item.fulltextWith Fulltext-
crisitem.journal.issn2191-219X-
crisitem.journal.eissn2191-219X-
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