README.md

metadata

license:
  - cc-by-sa-4.0
  - cc-by-nc-4.0
  - cc-by-3.0
  - cc-by-4.0
pretty_name: FLIP mock trust data
tags:
  - medical-imaging
  - omop-cdm
  - dicom
  - synthetic
  - federated-learning

FLIP mock trust data

Mock data for the dev and test trusts of FLIP, the Federated Learning Interoperability Platform. Everything here is either synthetic or derived from a public research dataset under its licence — no patient data. Licensing is per project (see the table and the per-project notes): the spleen- and cxr-derived content is CC BY-SA 4.0, the prostate-derived content (prostate_project) is CC BY-NC 4.0 and therefore non-commercial; the pathology tables (pathology_project) are metadata derived from IDC's TCGA-BRCA collection (CC BY 3.0) and its Pan-Cancer-Nuclei-Seg annotations (CC BY 4.0); the DICOM vocabulary bundle keeps its own Apache 2.0.

Versions

There is exactly one copy of every artefact, on main, at the paths below. A data version is a git tag on this repository (20260729, 20260901, …), and FLIP pins one in trust/.data_version. Every consumer fetches https://huggingface.co/datasets/aicentreflip/trust-data/resolve/<version>/<path>, so an old version stays reachable at its tag forever and is never duplicated as a second directory or a suffixed filename. Publishing a new version is one commit on main (the changed files) plus one tag: FLIP/trust/publish_trust_data.py.

tag	what changed
20260729	two-project (spleen, cxr) two-trust cut: OMOP tables, DICOM sets, vocab-free pgdata, Orthanc storage
20260901	spleen_project measurement rows carry the millimetre unit for SliceThickness (FLIP#1098); pgdata rebuilt
20260902	prostate_project added — PI-CAI fold 0, the first cohort published for the seed pipeline alone (no snapshot)
20260903	prostate_project DICOM re-cut with synthetic patient identities (name, birth date, referring physician, study description) — tables unchanged
20260911	pathology_project added — 24 IDC TCGA-BRCA whole-slide images described in OMOP; tables and the slide manifest only, the imaging stays on IDC (no dicom/ set)
20260917	brain_mri_project added — 40 MSD Task01 brain tumour cases as four-series MR studies, tables and metadata table only; spleen_project re-cut on the same deterministic converter (every identity, accession and UID changed) and its dicom/spleen_project.tar.gz removed from main — both DICOM sets now regenerate locally from the public MSD archives (FLIP#1221)
20261014	spleen_project metadata table re-cut: the converter now stores every slice in DICOM display order (ImageOrientationPatient [1,0,0,0,1,0], ImagePositionPatient moved with it), so the regenerated CT no longer renders rotated 180° in 2-D viewers (FLIP#1495). OMOP tables, identities and UIDs unchanged; brain_mri_project's regenerated DICOMs are reoriented the same way, but nothing published for it changes

The pre-tag layout — files with a version in their name or path (trust<N>_pgdata_<v>.tar, trust<N>_orthanc_data_<v>.tar, omop-csv/<v>/, dicom/<v>/) — was removed from main on 2026-09-11, once FLIP v0.6.0 made every consumer fetch by tag. Those files remain reachable at the tags whose trees carried them (20260729 … 20260911); nothing new is ever added at a versioned path.

What is here

path	contents	licence	source
omop-csv/<project>/*.csv	OMOP CDM 5.4 tables for each mock cohort, one canonical dataset; every row carries source_trust	per project	generated by the converters in FLIP/fl-tutorials/datasets/
omop-csv/<project>/source/…	the inputs each OMOP export was built from (DICOM metadata table; PI-CAI's marksheet; the pathology slide manifest)	per project	same
dicom/<project>.tar.gz	the DICOM instances behind each cohort, <accession>/*.dcm, one archive per project — only where the imaging has no public source to regenerate from (cxr_project, prostate_project). pathology_project has none (its slides are fetched from IDC); spleen_project and brain_mri_project have none from 20260917 on (regenerated locally from the MSD archives by FLIP's deterministic converter)	per project	see per-project notes below
trust<N>/trust<N>_pgdata.tar	vocab-free PostgreSQL data volumes, two-trust snapshot (spleen + cxr) — the pre-20260917 spleen cut	CC BY-SA 4.0	built from omop-csv/
trust<N>/trust<N>_orthanc_data.tar	Orthanc storage volumes, two-trust snapshot (spleen + cxr) — the pre-20260917 spleen cut	CC BY-SA 4.0	built from the DICOM sets
omop-vocab/vocab_dicom_*.zip	the DICOM vocabulary for OMOP	Apache 2.0	DICOM2OMOP

No licensed OHDSI vocabularies (SNOMED CT, LOINC, …) are on this dataset; FLIP loads those at seed time from a source each deployment licenses itself.

Per-project provenance and licence

spleen_project

CT volumes from the Medical Segmentation Decathlon, Task09 Spleen (Memorial Sloan Kettering Cancer Center; Antonelli et al., Nature Communications 2022, medicaldecathlon.com), converted from NIfTI to DICOM with synthetic patient identities — names, NHS-style numbers, dates, sex, accession numbers and UIDs are all generated; none belongs to a real person. The pixel data is MSD's.

Tables and the metadata table only, from 20260917. MSD is open data, so the DICOM set is not re-hosted: FLIP regenerates it from the Task09 archive with a deterministic converter (fl-tutorials/datasets/spleen/, on datasets/utils/dicom_writer.py — every UID, date and identity a pure function of the case id, so the regenerated tree is byte-identical anywhere and matches these tables exactly; make -C fl-tutorials verify-spleen-dicom checks that both ways) and seeds a trust from the local tree. That cut replaced the plastimatch-era converter, so every spleen identity, accession and UID changed at 20260917; the earlier set (41 studies, 3,650 instances) stays at the tags up to 20260911 as dicom/spleen_project.tar.gz, and the two-trust volume snapshots still carry it.

Licence: CC BY-SA 4.0, as MSD Task09 is (dataset.json: "licence": "CC-BY-SA 4.0"). Attribution to the Medical Segmentation Decathlon and MSKCC is required by anything that reuses the spleen-derived content here, and derivatives must carry the same licence.

brain_mri_project

Tables and the metadata table only — no imaging. Forty cases from the Medical Segmentation Decathlon, Task01 Brain Tumour (BraTS 2016/17; Antonelli et al. 2022) — the first 40 training cases in natural order — each described as one MR study of four series (FLAIR, T1w, T1Gd, T2w) with synthetic patient identities as the other projects carry, split 20/20 across two trusts (source_trust round-robin over the cases). The DICOM set is regenerated locally from the public Task01 archive by the same deterministic converter (fl-tutorials/datasets/brain_mri/), so what is published is the small thing that makes a run reproducible: source/dicom_metadata.csv, one row per series, naming every case, identity, accession and UID the regenerated tree must contain, and the six OMOP tables derived from it (person, visit_occurrence, procedure_occurrence — LOINC 24587-8 MR Brain WO and W contrast IV — image_occurrence per series with SNOMED Brain structure, image_feature and measurement for the DICOM attributes). The tumour labels are not in OMOP.

Licence: CC BY-SA 4.0, as MSD Task01 is. Attribution to the Medical Segmentation Decathlon and the BraTS challenge is required by anything that reuses the brain-derived content here, and derivatives must carry the same licence.

cxr_project

Chest radiographs produced by a generative model trained by the London AI Centre for Value Based Healthcare, with a synthetic radiology report per image (the conditioning / pathologies columns of its metadata table) and synthetic patient identities as above. No real radiograph is reproduced.

Licence: CC BY-SA 4.0.

prostate_project

Biparametric prostate MRI (axial T2W, ADC, high-b-value DWI — three series per study) from fold 0 of the PI-CAI Public Training and Development Dataset (Saha, Twilt, Bosma, van Ginneken et al.; Zenodo record 6624726): 300 studies of 295 patients from three Dutch centers (RUMC, ZGT, PCNN), converted from the released .mha to DICOM with deterministic UIDs, PI-CAI's own anonymised identifiers (PatientID = PI-CAI patient_id, AccessionNumber = <patient_id>_<study_id>) and, from tag 20260903, synthetic patient identities as the other projects carry — a name, a birth date consistent with PI-CAI's recorded age, a referring physician and a study description, all generated deterministically from the PI-CAI ids; none belongs to a real person. The clinical marksheet (PSA, PSA density, prostate volume, ISUP grade group, csPCa, PI-RADS) is carried into OMOP as measurements and observations. source_trust is one contributing center per trust: ZGT → 1 (76 studies), PCNN → 2 (69), RUMC → 3 (155; FLIP's two-trust dev stack loads sources 1 and 2, and RUMC waits for a third trust).

The segmentation masks that pair with these studies — whole gland (Bosma22b) and zonal PZ/TZ (HeviAI23) from picai_labels — are not on this dataset; FLIP's tutorial downloads them from that repository and uploads them into a project's XNAT as data enrichment.

Licence: CC BY-NC 4.0 — PI-CAI's images and labels are both released under CC BY-NC 4.0, so unlike the rest of this dataset the prostate-derived content is non-commercial. Reuse must credit the PI-CAI challenge (see Citation) and may not be commercial; anyone using FLIP's mock data commercially must leave prostate_project out.

pathology_project

Tables and a manifest only — no imaging. Twenty-four haematoxylin-and-eosin whole-slide images from the TCGA-BRCA collection of the NCI Imaging Data Commons (DICOM Slide Microscopy), one slide per patient, twelve from each of two TCGA tissue source sites (A8, A7 → source_trust 1 and 2). The slides and their Pan-Cancer-Nuclei-Seg nuclei annotations (DICOM Microscopy Bulk Simple Annotations) are not re-hosted here: FLIP's tutorial downloads them from IDC's public buckets at run time (make -C fl-tutorials download-idc-pathology-data), and the annotations reach a trust's XNAT by data enrichment. What is published is the small thing that makes a run reproducible — source/manifest.csv, the lockfile naming every selected slide (its UIDs, site and the IDC index version the selection was resolved against, since IDC issues versioned releases and series come and go) — and the four OMOP tables derived from it. The OMOP rows are metadata: TCGA barcodes as person_source_value/accession_id, study and series UIDs, IDC's recorded study dates, and the Slide microscopy modality concept. TCGA pathology DICOM is de-identified, so demographics are OMOP's "No matching concept" (0) and year_of_birth is the deliberately implausible sentinel 1900 rather than an invented value.

Licence: CC BY 3.0 for the TCGA-BRCA-derived metadata and CC BY 4.0 for the annotation-derived columns, as IDC records for the collection and the pan_cancer_nuclei_seg_dicom analysis result respectively (the license_short_name column of the IDC index). Attribution to TCGA / the NCI Imaging Data Commons and, for the annotations, to Hou et al. (see Citation) is required.

Provenance

The code that produced every table here is in the FLIP repository under fl-tutorials/datasets/, and each project's OMOP export is verified to reproduce byte-for-byte from its published inputs before it is tagged (make -C fl-tutorials reproduce-<project>-omop, the gate fl-tutorials/datasets/utils/verify_omop_tables.py; the run output is recorded in the FLIP pull request that published the tag). The DICOM sets are the original converter outputs, checked against the published tables before publishing (trust/orthanc/publish_dicom.py): every accession number, study UID and patient ID present, both ways.

Citation

If you use the spleen-derived content, cite the Medical Segmentation Decathlon:

Antonelli, M., Reinke, A., Bakas, S. et al. The Medical Segmentation Decathlon. Nat Commun 13, 4128 (2022). https://doi.org/10.1038/s41467-022-30695-9

If you use the prostate-derived content, cite the PI-CAI challenge and its public dataset:

Saha, A., Twilt, J. J., Bosma, J. S., van Ginneken, B. et al. The PI-CAI Challenge: Public Training and Development Dataset (2022). Zenodo. https://doi.org/10.5281/zenodo.6624726

If you use the pathology-derived content, cite the NCI Imaging Data Commons and the nuclei annotations:

Fedorov, A., Longabaugh, W. J. R., Pot, D. et al. NCI Imaging Data Commons. Cancer Research 81(16), 4188–4193 (2021). https://doi.org/10.1158/0008-5472.CAN-21-0950

Hou, L., Gupta, R., Van Arnam, J. S. et al. Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types. Sci Data 7, 185 (2020). https://doi.org/10.1038/s41597-020-0528-1