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http://hdl.handle.net/1942/33081| Title: | Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET | Authors: | Pfaehler, Elisabeth MESOTTEN, Liesbet Kramer, Gem THOMEER, Michiel VANHOVE, Karolien de Jong, Johan ADRIAENSENS, Peter Hoekstra, Otto S Boellaard, Ronald |
Issue Date: | 2021 | Publisher: | Source: | EJNMMI Research, 11 (4) (Art N° 4) | 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. | Keywords: | Convolutional neural network;Repeatability;Textural segmentation;Tumor segmentation PET | Document URI: | http://hdl.handle.net/1942/33081 | ISSN: | 2191-219X | e-ISSN: | 2191-219X | DOI: | 10.1186/s13550-020-00744-9 | ISI #: | WOS:000610092100002 | 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/. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
| Appears in Collections: | Research publications |
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| s13550-020-00744-9.pdf | Published version | 1.88 MB | Adobe PDF | View/Open |
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