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

## TL;DR

A new dataset of PET/CT scans with detailed segmentations is shared to support research and deep learning in medical imaging.

## Contribution

The novel contribution is a large, curated PET/CT dataset with CT-derived segmentations of 130 anatomical regions for research and AI training.

## Key findings

- The dataset includes 1,597 PET/CT images with segmentations of 130 anatomical regions.
- Segmentations cover diverse tissues like organs, muscles, and bones, verified by medical professionals.
- The dataset is anonymized and in NIfTI format, suitable for deep learning and multi-modality analysis.

## 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 130 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-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.