Extracting Dicom Metadata

What it does

When your agent uses it

• Keywords: DICOM
• Structured report
• Radiology report

Example prompts

• “Use the extracting-dicom-metadata skill to read DICOM file headers and DICOM-SR (Structured Report) content to pull study/series metadata and…”
• “/extracting-dicom-metadata”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Read the dataset with pydicom.dcmread (use stop_before_pixels=True
2. 2Walk the SR content tree. ContentSequence nests CONTAINER, TEXT,
3. 3Inventory PHI tags. Flag the standard identifier tags and free-text
4. 4De-identify → analyze the report narrative with OpenMed.
5. 5Scrub the header before any image export using a DICOM de-identification

What it can do on your machine

Read from SKILL.md and the folder at commit 34d7b8c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • dicom.nema.org
  • dicomstandard.org
  • pydicom.github.io

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Extracting Dicom Metadata loads about 1.8k tokens when it runs. Until then it costs about 150 tokens; SKILL.md has 622 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~150

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from maziyarpanahi/openmed at commit 34d7b8c, republished under its Apache-2.0 licence (© maziyarpanahi). 622 words, ~1,797 tokens.

name

extracting-dicom-metadata

description

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.

license

Apache-2.0

metadata.project

OpenMed

metadata.category

data-ingestion

metadata.pairs

before

metadata.version

1.0

import pydicom
import openmed

ds = pydicom.dcmread("study.dcm")

# 1) Enumerate PHI-bearing header tags (report, do not log values).
PHI_TAGS = [
    (0x0010, 0x0010), (0x0010, 0x0020), (0x0010, 0x0030), (0x0010, 0x1040),
    (0x0008, 0x0090), (0x0008, 0x0050), (0x0008, 0x0020), (0x0008, 0x0030),
]
present_phi = [hex_pair for hex_pair in PHI_TAGS if hex_pair in ds]

# 2) Extract report text from a DICOM-SR content tree (recursively).
def sr_text(dataset):
    chunks = []
    for item in dataset.get("ContentSequence", []):
        vt = item.get("ValueType")
        if vt == "TEXT" and "TextValue" in item:
            chunks.append(item.TextValue)
        if "ContentSequence" in item:          # nested CONTAINER
            chunks.append(sr_text(item))
    return "\n".join(c for c in chunks if c)

report = sr_text(ds)
# Some modalities stash narrative in free-text header tags too:
for tag in ("ImageComments", "SeriesDescription", "StudyDescription"):
    if tag in ds and isinstance(ds.get(tag), str):
        report += "\n" + ds.get(tag)

# 3) De-identify the narrative, then run NER.
if report.strip():
    deid = openmed.deidentify(report, method="replace", policy="hipaa_safe_harbor")
    result = openmed.analyze_text(deid.text, output_format="dict")

© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/extracting-dicom-metadata of maziyarpanahi/openmed.

Open the folder on GitHubat commit 34d7b8c