Datasets:
license: cc-by-nc-4.0
pretty_name: FluXray
tags:
- medical
- x-ray
- segmentation
- synthetic
FluXray: label-exact synthetic radiographs
138063 generatively edited digitally reconstructed radiographs with exact, overlapping segmentation masks for 63 structures, rendered from the 1597 annotated whole-body CTs of the MOOSE / ENHANCE-PET 1.6k dataset and restyled with a language-conditioned image editor (FLUX.2 klein), then quality-filtered (see the FleXray paper). Splits: {'train': 124198, 'val': 13865, 'test': 0}.
Layout
FluXray/thunder_dbs/1.0/ is a ThunderDB (LMDB) that the flexray package
reads directly; point GENERATED_DATAPATH at the root of the flexray-data download
(the directory containing FluXray/):
pip install flexray
export GENERATED_DATAPATH=/path/to/flexray-data
# training config
# data:
# Xray:
# FluXray: {version: "1.0"}
| File | Contents |
|---|---|
data.mdb |
The LMDB database. One entry per sample keyed <subject>_<pose_id>, holding img (float16, 256 x 256, intensities in [0, 1]) and seg (uint8, 63 x 256 x 256 binary masks, one channel per structure; channels may overlap). Index entries: _samples (ordered keys), _subjects, _splits (train/val/test key lists), _metadata (per-sample pose, split, MOOSE subject, source site, license) and _attrs (build provenance, label_names in channel order). Read with thunderpack.ThunderDB.open(path, "r") or through flexray. |
lock.mdb |
LMDB lock file; recreated automatically and safe to ignore. |
protocol.yml |
The 63 label names in channel order (label_names), the mask thresholds used to binarize the rendered soft masks, and the build recipe. |
samples.csv |
One row per sample: sample_id, MOOSE subject_id, split, pose_id, camera position x/y/z (mm) and rotation alpha_deg/beta_deg/gamma_deg, visible_label_count, source_site, license. |
filter_kept.csv, filter_dropped.csv |
Quality-control scores for the renders that passed and failed the filter. raw and flux are the soft Dice of a reference segmentation model against the exact labels on the unedited and edited render; soft_dice_delta_flux_minus_raw is their difference. Per pose_id, median_delta and q_upper_delta give the median and 0.98 quantile of the delta, lower_bound = 2 * median - q_upper, and keep = delta >= lower_bound. Pose columns mirror samples.csv; global_index is build bookkeeping. |
filter_thresholds.csv |
The per-pose filter statistics behind those decisions: delta mean/median/std/min/max before and after filtering, n_before, n_after, n_dropped, frac_dropped and the applied lower_bound. |
../../README.md, ../../LICENSE |
This card and the license summary (in FluXray/). |
License
CC BY-NC 4.0 for the collection as a whole. Each image inherits the license of its
MOOSE source site (license column in samples.csv and per-sample metadata):
- CC-BY-4.0: 50282 samples
- CC-BY-NC-4.0: 87781 samples
License text: https://creativecommons.org/licenses/by-nc/4.0/ (CC BY-NC 4.0) and https://creativecommons.org/licenses/by/4.0/ (CC BY 4.0). Changes from the source: CT volumes were projected to DRRs, restyled with FLUX.2 klein, quality-filtered, and paired with projected masks; no source CT or PET data is included.
Attribution
Please cite the FleXray paper and the ENHANCE.PET 1.6k dataset: Ferrara D. et al. (2026). Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative. Scientific Data. ENHANCE.PET 1.6k - Whole-/Total-Body [18F]FDG-PET/CT with CT-Derived Segmentations was accessed on 2026-02-16 from https://registry.opendata.aws/enhance-pet-1-6k.
The CC BY-NC 4.0 images derive from the AutoPET Challenge subset of ENHANCE.PET 1.6k, which additionally requires citing the source collection: Gatidis, S., Kuestner, T. (2022). A whole-body FDG-PET/CT dataset with manually annotated tumor lesions (FDG-PET-CT-Lesions) (Version 2) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/gkr0-xv29