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. 2022 May 28;12:30. doi: 10.1186/s13550-022-00901-2

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© The Author(s) 2022

Open AccessThis 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://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — Representative examples of the segmentations from the AI model for two patients. Maximum intensity projection [⁶⁴Cu]Cu-DOTATATE PET without tumor segmentation (A, D). Ground truth segmentation of tumor (B, E). AI predicted segmentations—no manual adjustments performed (C, F). In the AI output, all separate segmentations are given a unique color, e.g., red, blue, green, making manual adjustment with deletion of a segmentation easy and fast (e.g., part of the bladder was erroneously segmented in C)