| 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. |
| tags | ["dicom","medical-imaging","anonymization","pacs","dicomweb","pipeline","zorai"] |
Overview
End-to-end DICOM workflow: parsing, anonymization, conversion, structured reporting, PACS query/retrieve, and DICOMweb integration.
Installation
uv pip install pydicom
Read and Inspect
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
Anonymization
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
import requests
resp = requests.get(
"http://pacs:8080/dicom-web/studies",
params={"PatientName": "Doe*"},
headers={"Accept": "application/dicom+json"},
)
Workflow
- Parse DICOM with
pydicom.dcmread()
- Extract metadata: modality, anatomy, patient info
- Anonymize per DICOM PS3.15 (clear PHI tags)
- Convert to NIfTI via dcm2niix or manual pixel_array
- Query PACS with DICOMweb QIDO-RS
- Generate DICOM SR (Structured Reports) for AI findings
