extracting-dicom-metadata
Reads DICOM file headers and DICOM-SR (Structured Report) content to pull study/series metadata and embedded report text, and flags PHI carried in header tags. Use before OpenMed processing when ingesting imaging data (CT/MR/CR/US, radiology SR) and you need the report narrative de-identified and analyzed, plus a list of header tags that must be scrubbed. Hand SR/report text to openmed.deidentify and openmed.analyze_text; use pydicom to read tags. Trigger keywords: DICOM, pydicom, DICOM-SR, structured report, PatientName, study metadata, PACS, radiology report, PS3.
npx skills add maziyarpanahi/openmed --skill extracting-dicom-metadata --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Extracting DICOM Metadata & Report Text for OpenMed DICOM (Digital Imaging and Communications in Medicine) files carry far more than pixels: a **header** of tagged attributes (patient, study, series, equipment) and, for **DICOM-SR (Structured Reports)**, a content tree holding the actual radiology/cardiology *report text*. Two jobs sit here: pull the report narrative for NLP, and **flag the PHI in the header** so it gets scrubbed. This skill does both, then hands narrative to OpenMed. Header tags are read with `pydicom` (external, MIT-licensed); de-identification of the extracted text is OpenMed's. ## When to use - You ingest DICOM from PACS/VNA or a research archive and want the SR report text mined with clinical NLP. - You must enumerate PHI-bearing header tags before sharing/exporting images. - You have DICOM-SR objects (e.g. radiology measurements + impression) whose content tree contains the dictated report. ## DICOM headers in one minute Every attribute has a **tag** `(gggg,eeee)` (group, element), a **VR** (value representation, e.g. `PN` person name, `DA` date, `UI` UID), and a value. PHI clusters in well-known tags: | Tag | Name | VR | Notes | | --- | --- | --- | --- | |
- When to use
- DICOM headers in one minute
- Quick start
- Workflow
- Hand-off to / from OpenMed
- Edge cases & gotchas
- Standards & references
What does the extracting-dicom-metadata skill do?
Reads DICOM file headers and DICOM-SR (Structured Report) content to pull study/series metadata and embedded report text, and flags PHI carried in header tags. Use before OpenMed processing when ingesting imaging data (CT/MR/CR/US, radiology SR) and you need the report narrative de-identified and analyzed, plus a list of header tags that must be scrubbed. Hand SR/report text to openmed.deidentify and openmed.analyze_text; use pydicom to read tags. Trigger keywords: DICOM, pydicom, DICOM-SR, structured report, PatientName, study metadata, PACS, radiology report, PS3.
How do I install it?
Run `npx skills add maziyarpanahi/openmed --skill extracting-dicom-metadata --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.