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A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions.
Gatidis, Sergios; Hepp, Tobias; Früh, Marcel; La Fougère, Christian; Nikolaou, Konstantin; Pfannenberg, Christina; Schölkopf, Bernhard; Küstner, Thomas; Cyran, Clemens; Rubin, Daniel.
Afiliación
  • Gatidis S; Max-Planck-Institute for Intelligent Systems, Empirical Inference Department, Tuebingen, 72076, Germany. sergios.gatidis@tuebingen.mpg.de.
  • Hepp T; University Hospital Tübingen, Department of Radiology, Tübingen, 72076, Germany. sergios.gatidis@tuebingen.mpg.de.
  • Früh M; Max-Planck-Institute for Intelligent Systems, Empirical Inference Department, Tuebingen, 72076, Germany.
  • La Fougère C; University Hospital Tübingen, Department of Radiology, Tübingen, 72076, Germany.
  • Nikolaou K; University Hospital Tübingen, Department of Radiology, Tübingen, 72076, Germany.
  • Pfannenberg C; University Hospital Tübingen, Department of Nuclear Medicine and Clinical Molecular Imaging, Tübingen, 72076, Germany.
  • Schölkopf B; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, 72076, Germany.
  • Küstner T; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) Partner Site Tübingen, Tübingen, 72076, Germany.
  • Cyran C; University Hospital Tübingen, Department of Radiology, Tübingen, 72076, Germany.
  • Rubin D; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, 72076, Germany.
Sci Data ; 9(1): 601, 2022 10 04.
Article en En | MEDLINE | ID: mdl-36195599
ABSTRACT
We describe 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 original DICOM files of all studies as well as the corresponding DICOM segmentation masks. In addition, we provide scripts for image processing and conversion to different file formats (NIfTI, mha, hdf5). Primary diagnosis, age and sex are provided as non-imaging information. 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.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Guideline Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Guideline Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article