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@ARTICLE{Rempe:301908,
      author       = {M. Rempe and L. Heine and C. Seibold and F. Hörst and J.
                      Kleesiek$^*$},
      title        = {{D}e-identification of medical imaging data: a
                      comprehensive tool for ensuring patient privacy.},
      journal      = {European radiology},
      volume       = {nn},
      issn         = {0938-7994},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2025-01178},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {Medical imaging data employed in research frequently
                      comprises sensitive Protected Health Information (PHI) and
                      Personal Identifiable Information (PII), which is subject to
                      rigorous legal frameworks such as the General Data
                      Protection Regulation (GDPR) or the Health Insurance
                      Portability and Accountability Act (HIPAA). Consequently,
                      these types of data must be de-identified prior to
                      utilization, which presents a significant challenge for many
                      researchers. Given the vast array of medical imaging data,
                      it is necessary to employ a variety of de-identification
                      techniques.To facilitate the de-identification process for
                      medical imaging data, we have developed an open-source tool
                      that can be used to de-identify Digital Imaging and
                      Communications in Medicine (DICOM) magnetic resonance
                      images, computer tomography images, whole slide images and
                      magnetic resonance twix raw data. Furthermore, the
                      implementation of a neural network enables the removal of
                      text within the images.The proposed tool reaches comparable
                      results to current state-of-the-art algorithms at reduced
                      computational time (up to × 265). The tool also manages to
                      fully de-identify image data of various types, such as
                      Neuroimaging Informatics Technology Initiative (NIfTI) or
                      Whole Slide Image (WSI-)DICOMS.The proposed tool automates
                      an elaborate de-identification pipeline for multiple types
                      of inputs, reducing the need for additional tools used for
                      de-identification of imaging data.Question How can
                      researchers effectively de-identify sensitive medical
                      imaging data while complying with legal frameworks to
                      protect patient health information? Findings We developed an
                      open-source tool that automates the de-identification of
                      various medical imaging formats, enhancing the efficiency of
                      de-identification processes. Clinical relevance This tool
                      addresses the critical need for robust and user-friendly
                      de-identification solutions in medical imaging, facilitating
                      data exchange in research while safeguarding patient
                      privacy.},
      keywords     = {Data privacy (Other) / Medical imaging (Other) / Medical
                      machine learning (Other)},
      cin          = {ED01},
      ddc          = {610},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40481871},
      doi          = {10.1007/s00330-025-11695-x},
      url          = {https://inrepo02.dkfz.de/record/301908},
}