Data: The anonymized publication of data was approved by the local ethics committee and data protection officer. 501 consecutive whole body FDG-PET/CT data sets of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) as well as 513 data sets without PET-positive malignant lesions (negative controls) examined between 2014 and 2018 at the University Hospital Tübingen were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner (Siemens Biograph mCT). The imaging protocol consists of a diagnostic CT scan (mainly from skull base to mid-thigh level) with intravenous contrast enhancement in most cases, except for patients with contraindications. The following CT parameters were used: reference dose of 200 mAs, tube voltage of 120 kV, iterative reconstruction with a slice thickness of 2 - 3 mm. In addition, a whole-body FDG-PET scan was acquired 60 minutes after I.V. injection of 300-350 MBq 18F-FDG. PET data were reconstructed using an ordered-subset expectation maximization (OSEM) algorithm with 21 subsets and 2 iterations and a gaussian kernel of 2 mm and a matrix size of 400 x 400.
All data sets were analyzed in a clinical setting by a radiologist and nuclear medicine physician in consensus identifying primary tumors and metastases in each data set. All FDG-avid lesions identified as malignant based on patient history and prior examinations were manually segmented on PET images in a slice-per-slice manner by a single reader using dedicated software (NORA imaging platform, University of Freiburg, Germany).
We provide the anonymized original DICOM files of all studies as well as the DICOM segmentation masks. Primary diagnosis, age and sex are provided as non-imaging information (csv). In addition, we provide links to code for you to make a preprocessed version of the data with resampled and aligned PET, CT, and masks as a NIfTI file and in the hdf5 format ready to use in machine learning projects.
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- University Hospital Tübingen, Tübingen, Germany - Special thanks
- Christian La Fougère, MD from the Department of Nuclear Medicine
- Tobias Hepp, MD from the Department of Radiology
- Konstantin Nikolaou, MD from the Department of Radiology
- Christina Pfannenberg, MD from the Department of Radiology
- University Hospital of the LMU (Munich), Germany – Special thanks
- Clemens Cyran, MD from the Department of Radiology
- Michael Ingrisch from the Department of Radiology
Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to email@example.com before accessing the data.
|Data Type||Download all or Query/Filter||License|
|Images (DICOM, 418.9 GB)|
(Requires NBIA Data Retriever)
|Clinical data (CSV)|
Click the Versions tab for more info about data releases.
Additional Resources for this Dataset
- Scripts provided by the submitting group for file conversion, preprocessing alignment and resampling of PET, CT and mask data to NIfTI, MHA, and HDF5 formats: https://github.com/lab-midas/TCIA_processing
|Radiology Image Statistics|
PT, CT, SEG
Number of Patients
Number of Studies
Number of Series
Number of Images
|Images Size (GB)||418.9|
Here are conversion scripts for these data https://github.com/lab-midas/TCIA_processing
- Converts DICOM to NIfTI , and also create resampled/resliced CT and an SUV file using tcia_dicom_to_nifti.py (requires install of dicom2nifti and matplotlib)
- It is straight forward to generate HDF5 files from the NIfTI files using tcia_nifti_to_hdf5.py.
- Organizes NIfTI into HDF5 structure; note this output is a single large package.
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Gatidis S, Kuestner T. A whole-body FDG-PET/CT dataset with manually annotated tumor lesions (FDG-PET-CT-Lesions) [Dataset]. The Cancer Imaging Archive, 2022. DOI: 10.7937/gkr0-xv29
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7