Multi-center dataset of 1,597 FDG-PET/CT images and segmentations aids research on various pathologies, suggesting improved clinical applications.
Abstract We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Computed Tomography (PET/CT) studies, consisting of 1,597 PET/CT images and the corresponding CT-derived segmentations of over 100 target regions. This multi-center dataset includes images from individuals without overt disease and patients with different pathologies (lung cancer, lymphoma, and melanoma). Target regions were first automatically segmented from CT images using an in-house software, and subsequently verified and corrected by physicians. In total, the segmented regions encompass 130 volumes, including abdominal organs, muscles, bones, cardiac subregions, vessels, adipose tissue, and skeletal muscle around vertebra L3. 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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Ferrara et al. (2025) studied this question.
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