README.md

metadata

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 (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)