FDG-PET-CT-Lesions

Description

Introduction

A publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT datasets (501 studies of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) and 513 studies without PET-positive malignant lesions (negative controls)) acquired between 2014 and 2018 were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner. The imaging protocol consisted of a whole-body FDG-PET acquisition and a corresponding diagnostic CT scan. All FDG-avid lesions identified as malignant based on the clinical PET/CT report were manually segmented on PET images in a slice-per-slice (3D) manner. We provide the anonymized NIfTI files of all studies as well as the corresponding NIfTI segmentation masks. In addition, we provide scripts for image processing and conversion to different file formats (DICOM, mha, hdf5). Primary diagnosis, age and sex are provided as non-imaging information. 

Structure and usage

The data is organized in the following structure:

|--- Patient 1       
     |--- Study 1            
          |--- SUV.nii.gz       (PET image in SUV)            
          |--- CTres.nii.gz    (CT image resampled to PET)            
          |--- CT.nii.gz         (Original CT image)            
          |--- SEG.nii.gz     (Manual annotations of tumor lesions)            
          |--- PET.nii.gz      (Original PET image as activity counts)       
     |--- Study 2              (Potential 2nd visit of same patient)            
          |--- ...  
|--- Patient 2       
     |--- ...

|--- fdg_metadata.csv    (metadata csv for studies)

We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT data and provide the trained deep learning model: www.autopet.org

PET/CT acquisition protocol

Patients fasted at least 6 h prior to the injection of approximately 350 MBq 18F-FDG. Whole-body PET/CT images were acquired using a Biograph mCT PET/CT scanner (Siemens, Healthcare GmbH, Erlangen, Germany) and were initiated approximately 60 min after intravenous tracer administration. Diagnostic CT scans of the neck, thorax, abdomen and pelvis (200 reference mAs; 120 kV) were acquired 90 sec after intravenous injection of a contrast agent (90–120 ml Ultravist 370, Bayer AG). PET Images were reconstructed iteratively (three iterations, 21 subsets) with Gaussian post-reconstruction smoothing (2 mm full width at half-maximum). Slice thickness on contrast-enhanced CT was 2 or 3 mm.

Annotation

Two experts annotated training and test data: At the University Hospital Tübingen, a Radiologist with 10 years of experience in Hybrid Imaging and experience in machine learning research annotated all data. At the University Hospital of the LMU in Munich, a Radiologist with 5 years of of experience in Hybrid Imaging and experience in machine learning research annotated all data.

The following annotation protocol was defined:
Step 1: Identification of FDG-avid tumor lesions by visual assessment of PET and CT information together with the clinical examination reports.
Step 2: Manual free-hand segmentation of identified lesions in axial slices.

Files

Additional details