Remove Burnt-in Annotation

Some images contain burnt-in text — patient names, dates, hospital names, or measurements drawn directly onto the pixels (common on ultrasound, CR/DR, and some CT/MR screenshots). Standard DICOM de-identification removes tags in the file header but leaves this visible text in the picture.

Remove Burnt-in Annotation uses optical character recognition (OCR) to find text in the image and replace it with a solid black region or a blended patch, depending on your chosen mode.

What you need before using it

On the Welcome screen, click AI Features.
Check Remove Burnt-in Annotation.
Click Download if shown, and wait until the status is ready.
Save your settings.

How it works (in plain terms)

The tool reads each image frame the same way Series View displays it (including window/level for grayscale images).
It searches for text-like regions and compares them against rules and optional whitelists (allowed words such as “L” or “R” for left/right markers).
Matched text is covered using either black out (solid rectangle) or blend into background (softer fill).
Changes are written to new DICOM files in your anonymized storage; originals in the PHI area are not modified by batch runs until you export the updated series.

Using Series View (one series at a time)

Open a project and go to the Dataset view, then open Series View for a series.
Click Detect Text to highlight regions the tool thinks are text. Detected regions appear as green rectangles on the image.
To keep text inside a green rectangle (for example “L”, “R”, or a slice number you want to preserve), click the rectangle. That text is added to the whitelist on the left and will not be removed when you run Remove Text.
Use Remove Text to apply removal on the current frame or series according to the active mode.
You can draw blackout rectangles manually for areas OCR missed (toolbar in Series View).
Modality-specific whitelists (under project locale assets) define text that should be kept, such as orientation markers.

Removal modes

Black out text — fills detected text with black (or the appropriate background for inverted grayscale). Best when you want obvious, uniform removal.
Blend into background — tries to match surrounding pixels. Useful when a hard black box would look unnatural.

When running AI Batch Process, you choose the mode in the batch options dialog before processing starts.

Batch processing many studies

See AI Batch Process. Select Remove Burnt-in Annotation in the batch dialog. The tool runs on each slice of each selected series where OCR finds text.

Limitations and tips

Very small or low-contrast text may be missed; use manual blackout rectangles if needed.
Short numeric strings (slice numbers, etc.) may be ignored by design to avoid false positives.
Always spot-check a sample of images before releasing a dataset.
OCR models require a one-time download; they do not send your images to the internet during processing.