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README.md

@@ -36,4 +36,124 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+tags:
+- medical
+- histopathology
+- melanoma
+- segmentation
+- h&e
+license: cc0-1.0
+pretty_name: PUMA — Melanoma Nuclei & Tissue Segmentation
 ---
+
+# PUMA — Panoptic Segmentation of Nuclei and Tissue in Advanced Melanoma
+
+H&E histopathology ROIs from advanced melanoma with expert nuclei **and** tissue
+annotations. Mirror of the official Zenodo release for use in the MedOtter
+benchmark suite.
+
+> ⚠️ **This is the public training split only: 206 of the challenge's 310 ROIs.**
+> The 104 test ROIs are embargoed until **2029-10-10** and are not public
+> anywhere. Treat this as a single-split dataset.
+
+## Contents
+
+| | |
+|---|---|
+| ROIs | 206 (103 primary + 103 metastatic melanoma) |
+| Image | 1024×1024 RGB, 40×, ~0.226 µm/px |
+| Nuclei | 97,429 annotated, 10 classes |
+| Tissue | 5 foreground classes + background |
+| Splits | `train` only (see embargo note above) |
+| Licence | **CC0 1.0** |
+
+Source images are uncompressed RGBA TIFF with a constant-255 alpha channel;
+the alpha is dropped here (verified constant across all 206 files) and pixels
+are stored as lossless PNG.
+
+## Label maps
+
+**Tissue** (`tissue_mask`) — values follow the **challenge evaluation**
+convention, i.e. what the PUMA leaderboard scores against:
+
+| Value | Class |
+|---|---|
+| 0 | background (`tissue_white_background`, unpainted) |
+| 1 | stroma |
+| 2 | blood vessel |
+| 3 | tumor |
+| 4 | epidermis |
+| 5 | necrosis |
+
+⚠️ A **second official map exists** and disagrees: the organizers' QuPath
+export script uses `tumor=1, stroma=2, epidermis=3, necrosis=4,
+blood_vessel=5`. We use the evaluation convention because that is what the
+leaderboard and published PUMA models use. We do, however, keep the QuPath
+**paint order** (`tumor < stroma < epidermis < necrosis < blood_vessel`, later
+wins), so the masks stay geometrically identical to the official exports.
+In practice the order decides only ~0.001% of pixels.
+
+**Nuclei** (`nuclei_mask`) — the single official map:
+
+| Value | Class | | Value | Class |
+|---|---|---|---|---|
+| 1 | lymphocyte | | 6 | melanophage |
+| 2 | tumor | | 7 | endothelium |
+| 3 | stroma | | 8 | epithelium |
+| 4 | plasma cell | | 9 | neutrophil |
+| 5 | histiocyte | | 10 | apoptosis |
+
+The challenge's Track 1 uses a 3-class collapse of this map
+(`lymphocyte→1, tumor→2, all others→3`); Track 2 uses all 10.
+
+## Fields
+
+`case_id`, `cohort` (`primary`/`metastatic`), `roi_index`, `split`, `image`,
+`nuclei_mask`, `tissue_mask`, `nuclei_count`, `nuclei_class_ids`,
+`tissue_class_ids`, `mpp`, `magnification`.
+
+`nuclei_class_ids` / `tissue_class_ids` list the classes actually present in
+each ROI — useful because several classes are rare: **necrosis appears in only
+9/206 ROIs**, epidermis in 28/206, neutrophil in 30/206. Sampling a handful of
+ROIs at random will report zero for those classes.
+
+## Instance-level annotation
+
+Semantic masks merge touching nuclei of the same class. The original QuPath
+GeoJSON files are therefore included verbatim at the repo root:
+
+* `01_training_dataset_geojson_nuclei.zip`
+* `01_training_dataset_geojson_tissue.zip`
+
+Coordinates are in ROI pixel space (0–1024).
+
+## Provenance & caveats
+
+* Official source: [Zenodo record 15050523](https://zenodo.org/records/15050523)
+  (v5, 2025-03-19). Earlier versions differ — **v1/v2 were CC BY 4.0**, v3+ are
+  CC0; v3 dropped `metastatic_roi_103` (205 ROIs) and v5 restored a corrected
+  copy. The paper cites the stale v3 DOI and the challenge page links v4.
+* In the source archive, `training_set_metastatic_roi_103` uses a `.tiff`
+  extension while all 205 others use `.tif` — `glob("*.tif")` silently drops it.
+* The Zenodo description says "103 primary and 102 metastatic"; the archives
+  actually contain **103 + 103** (verified).
+* Ground truth is the expert tier: nuclei were initialised by a
+  PanNuke-pretrained HoVer-Net, corrected by a medical expert, then reviewed and
+  corrected by a dermatopathologist. Tissue was drawn manually throughout.
+  Reported human ceiling: nuclei F1 0.857 (intra-observer) / 0.802 (inter);
+  tissue Dice 0.90.
+* Because the nuclei GT was *seeded* by a PanNuke-pretrained model, models
+  pretrained on PanNuke may carry a mild prior advantage on boundary detail.
+  This is model lineage, not data leakage.
+* No patient/image overlap with PanNuke, MoNuSAC, NuCLS, CoNIC/Lizard, BCSS,
+  Pan-Cancer-Nuclei-Seg, DSB2018 or TCGA-SKCM — PUMA is single-institution
+  material and carries no cross-reference IDs.
+
+## Citation
+
+Schuiveling M, Liu H, Eek D, Breimer GE, Suijkerbuijk KPM, Blokx WAM, Veta M.
+*A novel dataset for nuclei and tissue segmentation in melanoma with baseline
+nuclei segmentation and tissue segmentation benchmarks.* GigaScience.
+2025;14:giaf011. doi:10.1093/gigascience/giaf011
+
+Dataset: doi:10.5281/zenodo.15050523 (CC0 1.0)