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http://hdl.handle.net/1942/33081
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DC Field | Value | Language |
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dc.contributor.author | Pfaehler, Elisabeth | - |
dc.contributor.author | MESOTTEN, Liesbet | - |
dc.contributor.author | Kramer, Gem | - |
dc.contributor.author | THOMEER, Michiel | - |
dc.contributor.author | VANHOVE, Karolien | - |
dc.contributor.author | de Jong, Johan | - |
dc.contributor.author | ADRIAENSENS, Peter | - |
dc.contributor.author | Hoekstra, Otto S | - |
dc.contributor.author | Boellaard, Ronald | - |
dc.date.accessioned | 2021-01-13T12:27:56Z | - |
dc.date.available | 2021-01-13T12:27:56Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2021-01-12T13:45:53Z | - |
dc.identifier.citation | EJNMMI Research, 11 (4) (Art N° 4) | - |
dc.identifier.issn | 2191-219X | - |
dc.identifier.uri | http://hdl.handle.net/1942/33081 | - |
dc.description.abstract | Positron 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.sponsorship | We 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.iso | en | - |
dc.publisher | - | |
dc.rights | The 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.other | Convolutional neural network | - |
dc.subject.other | Repeatability | - |
dc.subject.other | Textural segmentation | - |
dc.subject.other | Tumor segmentation PET | - |
dc.title | Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET | - |
dc.type | Journal Contribution | - |
dc.identifier.issue | 4 | - |
dc.identifier.volume | 11 | - |
local.format.pages | 11 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 4 | - |
dc.identifier.doi | 10.1186/s13550-020-00744-9 | - |
dc.identifier.pmid | 33409747 | - |
dc.identifier.isi | WOS:000610092100002 | - |
local.provider.type | PubMed | - |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | yes | - |
item.validation | ecoom 2022 | - |
item.contributor | Pfaehler, Elisabeth | - |
item.contributor | MESOTTEN, Liesbet | - |
item.contributor | Kramer, Gem | - |
item.contributor | THOMEER, Michiel | - |
item.contributor | VANHOVE, Karolien | - |
item.contributor | de Jong, Johan | - |
item.contributor | ADRIAENSENS, Peter | - |
item.contributor | Hoekstra, Otto S | - |
item.contributor | Boellaard, Ronald | - |
item.accessRights | Open Access | - |
item.fullcitation | Pfaehler, 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.fulltext | With Fulltext | - |
crisitem.journal.issn | 2191-219X | - |
crisitem.journal.eissn | 2191-219X | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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s13550-020-00744-9.pdf | Published version | 1.88 MB | Adobe PDF | View/Open |
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