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Purpose <p>Facial recognition of reconstructed computed tomography (CT) scans poses patient privacy risks, necessitating reliable facial de-identification methods. Current methods obscure sinuses, turbinates, and other anatomy relevant for otolaryngology. We present a facial de-identification method that preserves these structures, along with two automated workflows for large-volume datasets.</p> Methods <p>A total of 20 adult head CTs from the New Mexico Decedent Image Database were included. Using 3D Slicer, a seed-growing technique was performed to label the skin around the face. This label was dilated bidirectionally to form a 6-mm mask that obscures facial features. This technique was then automated using: (1) segmentation propagation that deforms an atlas head CT and corresponding mask to match other scans and (2) a deep learning model (nnU-Net). Accuracy of these methods against manually generated masks was evaluated with Dice scores and modified Hausdorff distances (mHDs).</p> Results <p>Manual de-identification resulted in facial match rates of 45.0% (zero-fill), 37.5% (deletion), and 32.5% (re-face). Dice scores for automated face masks using segmentation propagation and nnU-Net were 0.667 ± 0.109 and 0.860 ± 0.029, respectively, with mHDs of 4.31 ± 3.04&#xa0;mm and 1.55 ± 0.71&#xa0;mm. Match rates after de-identification using segmentation propagation (zero-fill: 42.5%; deletion: 40.0%; re-face: 35.0%) and nnU-Net (zero-fill: 42.5%; deletion: 35.0%; re-face: 30.0%) were comparable to manual masks.</p> Conclusion <p>We present a simple facial de-identification approach for head CTs, as well as automated methods for large-scale implementation. These techniques show promise for preventing patient identification while preserving underlying sinonasal anatomy, but further studies using live patient photographs are necessary to fully validate its effectiveness.</p>

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Manual and automated facial de-identification techniques for patient imaging with preservation of sinonasal anatomy

  • 期刊论文

  • 发表日期：

    2025年5月29日

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  • Andy S. Ding,
  • Nimesh V. Nagururu,
  • Stefanie Seo,
  • George S. Liu,
  • Manish Sahu,
  • Russell H. Taylor,
  • Francis X. Creighton

摘要

Purpose

Facial recognition of reconstructed computed tomography (CT) scans poses patient privacy risks, necessitating reliable facial de-identification methods. Current methods obscure sinuses, turbinates, and other anatomy relevant for otolaryngology. We present a facial de-identification method that preserves these structures, along with two automated workflows for large-volume datasets.

Methods

A total of 20 adult head CTs from the New Mexico Decedent Image Database were included. Using 3D Slicer, a seed-growing technique was performed to label the skin around the face. This label was dilated bidirectionally to form a 6-mm mask that obscures facial features. This technique was then automated using: (1) segmentation propagation that deforms an atlas head CT and corresponding mask to match other scans and (2) a deep learning model (nnU-Net). Accuracy of these methods against manually generated masks was evaluated with Dice scores and modified Hausdorff distances (mHDs).

Results

Manual de-identification resulted in facial match rates of 45.0% (zero-fill), 37.5% (deletion), and 32.5% (re-face). Dice scores for automated face masks using segmentation propagation and nnU-Net were 0.667 ± 0.109 and 0.860 ± 0.029, respectively, with mHDs of 4.31 ± 3.04 mm and 1.55 ± 0.71 mm. Match rates after de-identification using segmentation propagation (zero-fill: 42.5%; deletion: 40.0%; re-face: 35.0%) and nnU-Net (zero-fill: 42.5%; deletion: 35.0%; re-face: 30.0%) were comparable to manual masks.

Conclusion

We present a simple facial de-identification approach for head CTs, as well as automated methods for large-scale implementation. These techniques show promise for preventing patient identification while preserving underlying sinonasal anatomy, but further studies using live patient photographs are necessary to fully validate its effectiveness.