--- 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](https://github.com/londonaicentre/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//`, 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_pgdata_.tar`, `trust_orthanc_data_.tar`, `omop-csv//`, `dicom//`) — 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//*.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//source/…` | the inputs each OMOP export was built from (DICOM metadata table; PI-CAI's marksheet; the pathology slide manifest) | per project | same | | `dicom/.tar.gz` | the DICOM instances behind each cohort, `/*.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/trust_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/trust_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](https://github.com/paulnagy/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](http://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](https://zenodo.org/records/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` = `_`) 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`](https://github.com/DIAGNijmegen/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](https://creativecommons.org/licenses/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](https://portal.imaging.datacommons.cancer.gov/) (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--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