Sharing a whole-/total-body [<sup>18</sup>F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Computed Tomography (PET/CT) studies, consisting of 1,683 PET/CT images and the corresponding CT-derived segmentations of 130 target regions. This multi-center dataset includes images from individuals without overt disease and patients with a range of malignant and inflammatory pathologies, including arthritis, lymphoma, and melanoma, as well as cancers of the lung, head-neck, and genito-urinary tract. Target regions were first automatically segmented from CT images using an in-house software and subsequently verified and corrected by physicians-in-training. In total, the segmented regions encompass 130 volumes, including abdominal organs, muscles, bones, cardiac subregions, vessels, adipose tissue, and skeletal muscle around the third lumbar vertebra. PET/CT images and corresponding CT-derived segmentations are provided in anonymized NIfTI format. The dataset can be used for deep learning training, validation, or multi-modality image analysis and thus fills an important gap in available resources to advance the use of PET/CT data in clinical management.

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Publication Details

Journal
PubMed
Published
2026-04-14
DOI
https://doi.org/10.1038/s41597-026-07218-y
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

M Chen, Elisabetta Abenavoli, Josef Yu, Barbara Katharina Geist et al.
PubMed
Medical Imaging Techniques and Applications
article

Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

M Chen, Elisabetta Abenavoli, Josef Yu, Barbara Katharina Geist, H Wirtz, Osama Raslan, Lalith Kumar Shiyam Sundar, Sebastian Gutschmayer, Abhijit J. Chaudhari, Pia Linder, Ivo Rausch, Johanna Pappisch, Stefan Gruenert, Armin Frille, Daria Ferrara, Teresa Kerkhoff, Swen Hesse, Fabian Schmidt, Smilla Pusitz, Benjamin A. Spencer, Manuel Pires, M H Hacker, Guobao Wang, Thomas Beyer, O Sabri, Ramsey D. Badawi, Roberto Sciagra, Siba P Raychaudhuri, Simon R Cherry, Yasser G Abdelhafez
article en

Abstract

F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Computed Tomography (PET/CT) studies, consisting of 1,683 PET/CT images and the corresponding CT-derived segmentations of 130 target regions. This multi-center dataset includes images from individuals without overt disease and patients with a range of malignant and inflammatory pathologies, including arthritis, lymphoma, and melanoma, as well as cancers of the lung, head-neck, and genito-urinary tract. Target regions were first automatically segmented from CT images using an in-house software and subsequently verified and corrected by physicians-in-training. In total, the segmented regions encompass 130 volumes, including abdominal organs, muscles, bones, cardiac subregions, vessels, adipose tissue, and skeletal muscle around the third lumbar vertebra. PET/CT images and corresponding CT-derived segmentations are provided in anonymized NIfTI format. The dataset can be used for deep learning training, validation, or multi-modality image analysis and thus fills an important gap in available resources to advance the use of PET/CT data in clinical management.

PubMed
Siemens (Germany) (DE), King's College London (GB), LMU Klinikum (DE), Azienda Ospedaliero-Universitaria Careggi (IT), University Hospital Leipzig (DE), University of California Davis Medical Center (US), Medical University of Vienna (AT), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE), University of California, Davis (US), Leipzig University (DE)
Partnerships for the goals
Openalex Percentile: Top 18%
Medical Imaging Techniques and Applications
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