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<p>We describe a publicly available, large, annotated dataset of 597 whole-body Positron Emission Tomography/Computed Tomography (PET/CT) studies with Prostate-Specific Membrane Antigen (PSMA)-targeting radiotracers ([18 F]PSMA and [68Ga]Ga-PSMA-11) from 378 male patients with suspected or diagnosed prostate carcinoma. Scans were acquired between 2014 and 2022 on three clinical PET/CT scanners. The imaging protocol consisted of PET and diagnostic CT acquisitions extending from the skull base to the mid-thigh. All PSMA-expressing tumor lesions were manually segmented on the PET images in 3D space using dedicated software. The dataset includes anonymized DICOM files of all PET/CT studies, corresponding DICOM segmentation masks, and a TSV file with patient age, PET/CT manufacturer and model name, PET radionuclide, and information on whether CT contrast agent was used. We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT. Together with a previously published whole-body Fluorodeoxyglucose (FDG)-PET/CT dataset, this dataset was provided in the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) registered autoPET III and IV Grand Challenges to enable the development of multi-tracer machine learning models for automated lesion segmentation in whole-body PET/CT.</p>

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A Whole-Body PSMA-PET/CT dataset with manually annotated tumor lesions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Katharina Jeblick,
  • Balthasar Schachtner,
  • Andreas Mittermeier,
  • Jakob Dexl,
  • Philipp Wesp,
  • Thomas Küstner,
  • Sergios Gatidis,
  • Marcel Früh,
  • Matthias P. Fabritius,
  • Felix Herr,
  • Lena Unterrainer,
  • Konrad Klimek,
  • Gabriel Sheikh,
  • Guido Böning,
  • Matthias Brendel,
  • Jens Ricke,
  • Rudolf A. Werner,
  • Sijing Gu,
  • Lalith Kumar Shiyam Sundar,
  • Michael Ingrisch,
  • Thomas Geyer,
  • Clemens Cyran

摘要

We describe a publicly available, large, annotated dataset of 597 whole-body Positron Emission Tomography/Computed Tomography (PET/CT) studies with Prostate-Specific Membrane Antigen (PSMA)-targeting radiotracers ([18 F]PSMA and [68Ga]Ga-PSMA-11) from 378 male patients with suspected or diagnosed prostate carcinoma. Scans were acquired between 2014 and 2022 on three clinical PET/CT scanners. The imaging protocol consisted of PET and diagnostic CT acquisitions extending from the skull base to the mid-thigh. All PSMA-expressing tumor lesions were manually segmented on the PET images in 3D space using dedicated software. The dataset includes anonymized DICOM files of all PET/CT studies, corresponding DICOM segmentation masks, and a TSV file with patient age, PET/CT manufacturer and model name, PET radionuclide, and information on whether CT contrast agent was used. We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT. Together with a previously published whole-body Fluorodeoxyglucose (FDG)-PET/CT dataset, this dataset was provided in the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) registered autoPET III and IV Grand Challenges to enable the development of multi-tracer machine learning models for automated lesion segmentation in whole-body PET/CT.