Add FluXray 1.0 (label-exact synthetic radiographs) under FluXray/

.gitattributes

@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+FluXray/thunder_dbs/1.0/data.mdb filter=lfs diff=lfs merge=lfs -text
+FluXray/thunder_dbs/1.0/filter_kept.csv filter=lfs diff=lfs merge=lfs -text
+FluXray/thunder_dbs/1.0/samples.csv filter=lfs diff=lfs merge=lfs -text

FluXray/LICENSE

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+CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
+Derived from MOOSE / ENHANCE-PET 1.6k; per-sample licenses in samples.csv.

FluXray/README.md

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+---
+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/`):
+
+```bash
+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

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FluXray/thunder_dbs/1.0/protocol.yml

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+built_at: '2026-06-27T10:30:23.151042+00:00'
+dataset: FluXray
+image_source: flux
+label_names:
+- background
+- skull
+- scapulae
+- clavicles
+- humeri
+- phalanges
+- metacarpals
+- carpals
+- radii
+- ulnae
+- tibiae
+- femurs
+- patellae
+- fibulae
+- metatarsals
+- tarsals
+- toes
+- rib_1
+- rib_2
+- rib_3
+- rib_4
+- rib_5
+- rib_6
+- rib_7
+- rib_8
+- rib_9
+- rib_10
+- rib_11
+- rib_12
+- sternum
+- vertebra_c1
+- vertebra_c2
+- vertebra_c3
+- vertebra_c4
+- vertebra_c5
+- vertebra_c6
+- vertebra_c7
+- vertebra_t1
+- vertebra_t2
+- vertebra_t3
+- vertebra_t4
+- vertebra_t5
+- vertebra_t6
+- vertebra_t7
+- vertebra_t8
+- vertebra_t9
+- vertebra_t10
+- vertebra_t11
+- vertebra_t12
+- vertebra_l1
+- vertebra_l2
+- vertebra_l3
+- vertebra_l4
+- vertebra_l5
+- hips
+- sacrum
+- lumbar_spine
+- thoracolumbar_spine
+- kidneys
+- liver
+- spleen
+- lungs
+- heart
+layout: FluXray/thunder_dbs/<version>
+num_classes: 63
+output_size:
+- 256
+- 256
+protocol_name: all_structures_flexray_v4
+schema_version: 1
+seg_default_threshold: 0.0
+seg_label_thresholds:
+  lungs: 0.1
+seg_storage: overlapping_channel_mask
+seg_threshold_protocol: all_structures_flexray_v4
+source_mask_dtype: float16
+version: '1.0'

FluXray/thunder_dbs/1.0/samples.csv

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+size 21792137