dicom-pipeline

End-to-end DICOM workflow: parsing, anonymization/de-identification, conversion, structured reporting, PACS query/retrieve, and DICOMweb.

Skill by mkurman

IndexedMIT★ 325

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

End-to-end DICOM workflow: parsing, anonymization/de-identification, conversion, structured reporting, PACS query/retrieve, and DICOMweb. Build automated medical imaging pipelines.

Use cases: data, research, productivity

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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What's inside

Skill name: dicom-pipeline
Description: End-to-end DICOM workflow: parsing, anonymization/de-identification, conversion, structured reporting, PACS query/retrieve, and DICOMweb. Build automated medical imaging pipelines.
License: MIT
Instructions: 55 lines in SKILL.md · view on GitHub

Before installing

Instructions call the network

Files (1)

SKILL.md

SKILL.md (first lines)

## Overview

End-to-end DICOM workflow: parsing, anonymization, conversion, structured reporting, PACS query/retrieve, and DICOMweb integration.

## Installation

```bash
uv pip install pydicom
```

## Read and Inspect

```python
import pydicom, numpy as np

ds = pydicom.dcmread("study.dcm")
print(f"Patient: {ds.PatientName}")
print(f"Modality: {ds.Modality}")
print(f"Study: {ds.StudyDescription}")
print(f"Size: {ds.Rows}x{ds.Columns}")

pixels = ds.pixel_array  # NumPy array
```

## Anonymization

```python
ds = pydicom.dcmread("input.dcm")
phi_tags = [(0x0010, 0x0010), (0x0010, 0x0030), (0x0008, 0x0080)]
for tag in phi_tags:
    if tag in ds:
        ds[tag].value = ""
ds.save_as("anon.dcm")
```

## DICOMweb

```python
import requests
resp = requests.get(
    "http://pacs:8080/dicom-web/studies",
    params={"PatientName": "Doe*"},
    headers={"Accept": "application/dicom+json"},
)
```

## Workflow

1. Parse DICOM with `pydicom.dcmread()`
2. Extract metadata: modality, anatomy, patient info
3. Anonymize per DICOM PS3.15 (clear PHI tags)
4. Convert to NIfTI via dcm2niix or manual pixel_array
5. Query PACS with DICOMweb QIDO-RS
6. Generate DICOM SR (Structured Reports) for AI findings

From mkurman/zorai, MIT.

Signals

Stars: 325
Forks: 24
Open issues: 0
Last commit (this folder): May 4, 2026 (5 months ago)
Last release: Oct 23, 2026 (5 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License: MIT
Repository: github.com/mkurman/zorai
Website: zorai.app/
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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dicom-pipeline skill. Agent Mart. https://agentmart.dev/skills/mkurman-dicom-pipeline (last updated 2026-10-24)