How Shaip’s Privacy-First MRI De-Identification Workflow Powers Research at Scale
A multi-institutional research program chose Shaip to design and validate an MRI de-identification workflow that secures ~100,000 scans for compliant data sharing.
Project Overview
A multi-institutional research program enabling secure, privacy-compliant medical imaging for AI innovation and clinical studies. To support secure data sharing and multi-site collaboration, the client needed a robust pipeline to de-identify ~100,000 MRI scans, removing reconstructible facial/anatomical features and embedded PHI while preserving research utility. Shaip was engaged to implement and validate a full de-identification workflow.
Key Stats
Modality
Brain & musculoskeletal MRI across research cohorts
Volume
~100,000 scans processed
end-to-end
Semi-automated defacing + skull-stripping + metadata scrubbing
Human-in-the-loop verification for PHI removal & diagnostic integrity
HIPAA & GDPR-aligned protocols; guideline documentation
Challenges
- Generalization across vendors/studies with semi-automated pipelines.
- Identity protection without degrading scientific signal (defacing & skull-stripping).
- Human-in-the-loop QC to catch residual PHI in pixels and DICOM headers.
- Regulatory alignment with HIPAA/GDPR and auditable workflows.
Solution
Mapped the path from inbound DICOM to de-identified outputs (DICOM/NIfTI), identifying PHI risk points in pixel data and headers.
Applied calibrated defacing and skull‑stripping methods; automated header scrubbing and checksum audits; retained non‑identifying acquisition parameters for analysis.
Two‑tier review—algorithmic checks plus trained reviewers validating identity cue removal and research utility; exception handling with re‑processing loops.
HIPAA/GDPR‑aligned SOPs, access controls, transformation logs, and a standard de‑identification guideline for future studies.
Project Scope
| Stream | Scope | Technologies / Controls | Outcomes |
|---|---|---|---|
| Pixel De-ID | Defacing & skull-stripping | Semi-automated tools + visual QC | Identity protection with signal preserved |
| Metadata De-ID | DICOM tag scrubbing | Rule-based removal + whitelist | No PHI leakage in headers |
| Verification | Reviewer audits | Checklists; sampling plans | Measurable PHI risk reduction |
| Governance | SOPs & training | Audit trails; access controls | Reproducibility & compliance |
The Outcome
- Secure sharing of ~100,000 MRI scans with human‑verified PHI removal for research collaborations.
- Internal de‑ID guidelines standardized future studies and reduced rework.
- Ecosystem impact: Protocol positions millions of scans to become research‑ready over time.
Strategic Impact: The program established a repeatable, auditable factory from raw MRI to privacy‑preserved datasets—accelerating innovation while protecting identity.
Shaip’s privacy pipeline enabled us to share large MRI cohorts without compromising diagnostic value—setting a new benchmark for research governance.
— Technical Lead, Imaging Privacy & Security
