Sharing a whole-/total-body [<sup>18</sup>F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.
Authors
Affiliations (13)
Affiliations (13)
- QIMP Team, Medical University of Vienna, Vienna, Austria. [email protected].
- QIMP Team, Medical University of Vienna, Vienna, Austria.
- Division of Nuclear Medicine, Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Vienna, Austria.
- Department of Radiology, University of California Davis, Sacramento, California, USA.
- Division of Nuclear Medicine, Azienda Ospedaliero Universitaria Careggi, Florence, Italy.
- Comprehensive Cancer Center, University of California Davis, Sacramento, California, USA.
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Division of Respiratory Medicine, Department of Medicine II, Leipzig University Medical Center, Leipzig, Germany.
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany.
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany.
- Department of Medicine and Dermatology, UC Davis School of Medicine, Sacramento, California, USA.
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany.
- DIGIT-X Lab, Department of Radiology, LMU Munich, Munich, Germany.
Abstract
We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[<sup>18</sup>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.