| --- |
| dataset_info: |
| features: |
| - name: case_id |
| dtype: string |
| - name: cohort |
| dtype: string |
| - name: roi_index |
| dtype: int32 |
| - name: split |
| dtype: string |
| - name: image |
| dtype: image |
| - name: nuclei_mask |
| dtype: image |
| - name: tissue_mask |
| dtype: image |
| - name: nuclei_count |
| dtype: int32 |
| - name: nuclei_class_ids |
| list: int32 |
| - name: tissue_class_ids |
| list: int32 |
| - name: mpp |
| dtype: float32 |
| - name: magnification |
| dtype: int32 |
| splits: |
| - name: train |
| num_bytes: 381386223 |
| num_examples: 206 |
| download_size: 381439088 |
| dataset_size: 381386223 |
| configs: |
| - config_name: default |
| 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) |
|
|
