VIDS — Verified Imaging Dataset Standard

An open standard for organizing, validating, and delivering annotated medical imaging datasets for AI/ML development. Structure · Provenance · Quality.

Mandatory Provenance

Every annotation carries a structured record of who created it, when, with what tool, and under what quality controls. Provenance is a validation requirement, not a recommendation.

Automated Validation

21 machine-enforceable validation rules. One command. Binary PASS/FAIL. Conformance is determined by running the validator — not by reading a checklist. Anyone holding the dataset can run it and get the same result.

Framework Export

Curate once in VIDS, export to nnU-Net, MONAI, COCO JSON, or flat NIfTI. Provenance travels with the data through every format conversion.

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Quick Start

Install the validator and check any dataset in seconds:

pip install vids-validator

vids-validate /path/to/dataset

vids-validate /path/to/dataset --profile full --json

Example validator output

============================================================
VIDS Validation Report
Profile: POC
Dataset: /data/lung-nodule-ct-100
============================================================

  ✅ S001: .vids marker file present
  ✅ S002: dataset_description.json valid
  ✅ S003: Participants file present (json)
  ✅ S004: README.md present
  ✅ S005: 100 subject directories found
  ✅ S006: All subjects have session directories
  ✅ I001: All subjects have imaging files
  ✅ I002: All subjects have imaging sidecar JSONs
  ✅ I003: 100 imaging JSONs valid
  ✅ I004: All files follow VIDS naming convention
  ✅ A001: derivatives/annotations/ exists
  ✅ A002: Annotations found (100 seg)
  ✅ A003: All annotation files have required sidecars
  ✅ A004: 100 annotation JSONs valid
  ✅ A005: All annotations have complete provenance
  ⏭  Q001: POC profile — quality/ optional
  ⏭  Q002: POC profile — quality_summary optional
  ⏭  Q003: POC profile — annotation_agreement optional
  ⏭  M001: POC profile — ml/ optional
  ⏭  M002: POC profile — splits optional
  ⏭  D001: POC profile — CHANGES.md optional (recommended)

------------------------------------------------------------
  Passed:   15
  Failed:   0
  Warnings: 0
  Skipped:  6
------------------------------------------------------------

✅ VALIDATION PASSED (15/21 rules)

---

What a VIDS Dataset Looks Like

lung-nodule-ct-100/
├── .vids                              # Profile marker (poc or full)
├── dataset_description.json           # Dataset metadata
├── participants.json                  # Subject registry
├── README.md                          # Human-readable description
│
├── sub-001/
│   └── ses-baseline/
│       └── ct/
│           ├── sub-001_ses-baseline_ct_img.nii.gz
│           └── sub-001_ses-baseline_ct_img.json
│
├── derivatives/
│   └── annotations/
│       └── sub-001/
│           └── ses-baseline/
│               └── ct/
│                   ├── sub-001_ses-baseline_ct_seg.nii.gz
│                   └── sub-001_ses-baseline_ct_seg.json    ← provenance lives here
│
├── quality/                           # Full profile only
│   ├── quality_summary.json
│   └── annotation_agreement.json
│
└── ml/                                # Full profile only
    └── splits.json

Every annotation sidecar carries structured provenance:

{
  "VIDSVersion": "1.0",
  "AnnotationType": "segmentation",
  "SourceImage": "sub-001_ses-baseline_ct_img.nii.gz",
  "Provenance": {
    "Annotator": {
      "ID": "radiologist_001",
      "Name": "Dr. Jane Smith",
      "Credentials": "MD, Board-certified radiologist, 10 years experience",
      "Specialty": "Chest Radiology"
    },
    "AnnotationProcess": {
      "Tool": "3D Slicer",
      "ToolVersion": "5.6.2",
      "Date": "2026-02-12",
      "TimeSpent_minutes": 15,
      "Method": "Manual segmentation"
    },
    "QualityControl": {
      "ReviewedBy": "senior_radiologist_001",
      "ReviewDate": "2026-02-13",
      "ReviewOutcome": "approved",
      "Confidence": 0.93
    }
  }
}

---

Why VIDS?

Medical imaging datasets used to train AI models typically lack structured, machine-readable provenance. Annotation methodology exists in journal papers, README files, or institutional memory — invisible to any automated compliance or audit process.

Three converging pressures are making this untenable:

Pressure	Problem	What VIDS Provides
Dataset duplication	Datasets are copied across platforms without attribution or provenance	Every file carries its own structured metadata
Synthetic data	AI-generated images are indistinguishable from real scans	Origin documentation is a structural requirement
Regulatory direction	Emerging AI regulatory frameworks increasingly emphasize governance, documentation, and traceability of AI training data throughout its lifecycle	Machine-readable provenance chain a reviewer can follow

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How VIDS Compares

VIDS is complementary to existing standards — it fills the gap none of them cover.

	DICOM	BIDS	NIfTI	nnU-Net / MONAI	VIDS
Image storage
Neuro folder structure
Multi-modality datasets
Annotation provenance
Quality documentation
Automated validation
ML framework export
Compliance profiles

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For Buyers of Medical Imaging Datasets

Acquiring annotated imaging datasets from external vendors? VIDS provides the procurement infrastructure to make dataset quality verifiable before acceptance.

Reference Procurement Language — drop-in contract clauses that make a VIDS PASS the acceptance condition for vendor deliveries. Free to use under CC BY 4.0.

Dataset Documentation Assessment — a diagnostic review that measures documentation coverage in vendor samples or existing datasets, including datasets not yet structured for the validator. It does not determine VIDS conformance. Delivered within 72 hours.

Assessment Methodology — the 22 published dimensions used in documentation assessments, dimension by dimension.

Validation Report — the machine-readable validator output a vendor delivers and a buyer reproduces.

See the full For Buyers section →

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Citation

VIDS: A Verified Imaging Dataset Standard for Medical AI Dr. Joan S. Muthu, John Shalen — Princeton Medical Systems · arXiv:2604.17525

@misc{muthu2026vids,
  title   = {VIDS: A Verified Imaging Dataset Standard for Medical AI},
  author  = {Muthu, Joan S. and Shalen, John},
  year    = {2026},
  eprint  = {2604.17525},
  archivePrefix = {arXiv},
  primaryClass  = {eess.IV},
  url     = {https://arxiv.org/abs/2604.17525}
}

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VIDS is an open standard. Specification: CC BY 4.0 · Tools: Apache 2.0

GitHub PyPI Full Specification For Buyers