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Facial De-identification of Head CT Scans

Abstract

Online supplemental material is available for this article.

The use of de-identification has become of paramount importance due to the increased availability of shared head and neck CT or MRI scans, given the concern that facial features reconstructed from these studies can be used to identify individuals (1). Per the Safe Harbor standard to achieve de-identification in accordance with the Health Insurance Portability and Accountability Act, full-face photographs and any comparable images must be removed. We propose a simple solution to de-identify three-dimensional surface-reconstructed facial images by using 3D Slicer software (version 4.10.2; https://www.slicer.org/) (2). After importing the Digital Imaging and Communications in Medicine (DICOM) volume into the 3D Slicer software, segmentation of the air on the CT scan around the patient’s face was created using a threshold range of −1024 to −150 HU. The segmentation was then dilated by a margin of 5 mm and smoothed using a Gaussian smoothing method with a standard deviation of 5 mm. Then, a surface model was generated from the dilated and smoothed segmentation to replace the original air and skin voxels in the original CT scan. This method created a new DICOM volume with the intracranial anatomy preserved (Figure; Movie [online]).

A, Volume rendering from the original nonedited CT scan. B, Volume rendering                 from the edited CT image does not display any unique distinguishing facial                 features.

A, Volume rendering from the original nonedited CT scan. B, Volume rendering from the edited CT image does not display any unique distinguishing facial features.

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Movie: Video clip of de-identified face.

Disclosures of Conflicts of Interest: S.A.C. disclosed no relevant relationships. J.W. disclosed no relevant relationships. H.X.B. disclosed no relevant relationships.

References

  • 1. Schwarz CG, Kremers WK, Therneau TM, et al. Identification of Anonymous MRI Research Participants with Face-Recognition Software. N Engl J Med 2019;381(17):1684–1686. Crossref, MedlineGoogle Scholar
  • 2. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012;30(9):1323–1341. Crossref, MedlineGoogle Scholar

Article History

Received: Nov 26 2019
Revision requested: Dec 16 2019
Revision received: Jan 28 2020
Accepted: Feb 4 2020
Published online: Apr 7 2020
Published in print: July 2020