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

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Authors: Daria Ferrara, Manuel Pires, Sebastian Gutschmayer, Josef Yu, Yasser G. Abdelhafez, Elisabetta Abenavoli, Ramsey D. Badawi, Abhijit J. Chaudhari, M Chen, Simon R. Cherry, Armin Frille, Barbara Katharina Geist
+18 more Stefan Gruenert, M H Hacker, Swen Hesse, Teresa Kerkhoff, Pia H. Linder, Johanna Pappisch, Smilla Pusitz, Osama A. A. Raslan, Ivo Rausch, Siba P. Raychaudhuri, O Sabri, Fabian P. Schmidt, Roberto Sciagra, Benjamin A. Spencer, Guobao Wang, H Wirtz, Thomas Beyer, Lalith Kumar Shiyam Sundar
Cited by: 2
References: 52

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.

Fields of Study & Subjects

Medical Imaging Techniques and Applications Radiomics and Machine Learning in Medical Imaging Cell Image Analysis Techniques

Publication details

Journal / source
PubMed
Publisher
Springer Science and Business Media LLC
Volume
13
Issue
1
Publication date
14 April 2026
Language
EN