Face De-identify

On some head CT scans, facial features in the reconstructed images could allow someone to recognize a patient even after DICOM tags are removed. Face De-identify finds the face region in the scan, blurs those voxels, and saves updated images suitable for research sharing.

What you need before using it

On the Welcome screen, click AI Features.
Check Face De-identify.
Click Download for the face segmentation model if prompted.
Accept the academic license when prompted (required by the segmentation software vendor). This is a one-time acknowledgement on your machine.
Save your settings.

Which studies qualify

Modality: CT only.
Anatomy: Head or head-and-neck coverage where a face segmentation is feasible.
Very small fields of view, corrupted stacks, or non-diagnostic scouts may be skipped with a message explaining why.

How it works (in plain terms)

The tool converts the CT series into a 3D volume.
It runs a face segmentation model to locate voxels belonging to the face.
It blurs those voxels using the blur mode you selected (see below).
Blurred slices are written as a new DICOM series (or update path used by your workflow) while preserving geometry and unrelated anatomy.
Segmentation masks are cached so re-running on the same series is faster.

Blur modes

Gaussian blur — smooth blur inside the face mask (common default).
Mean / median blur — alternative smoothing styles; choose in Series View or batch options if available.

After batch or automatic blur, open the Face blur review dialog when offered to confirm coverage on sample slices before export.

Using Series View (one series)

Open Series View for a qualifying head CT series.
Run face blur from the series actions (when enabled and ready).
Review axial slices in the review dialog: check that the face is covered and that critical anatomy outside the mask is unchanged.
Accept or re-run with force if you changed inputs and need a fresh segmentation.

Batch processing many studies

See AI Batch Process. Select Face De-identify in the batch dialog. Ineligible series are skipped and counted in the batch summary.

Limitations and tips

Designed for CT head data — not for plain radiographs or MRI facial sequences.
Always review a subset of blurred series; segmentation can miss unusual positioning or extreme anatomy.
Blurring reduces recognizability but is not a guarantee against all re-identification risk.
Face model download and license acceptance require internet once; processing itself is local.