Datasets:

MedOtter
/

PUMA

case_id stringlengths 28 31	cohort stringclasses 2 values	roi_index int32 1 103	split stringclasses 1 value	nuclei_count int32 168 1.12k	nuclei_class_ids listlengths 1 9	tissue_class_ids listlengths 1 4	mpp float32 0.23 0.23	magnification int32 40 40
training_set_metastatic_roi_001	metastatic	1	train	633	[ 1, 2, 7, 10 ]	[ 3, 5 ]	0.2263	40
training_set_metastatic_roi_002	metastatic	2	train	824	[ 1, 2, 3, 4, 7 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_003	metastatic	3	train	390	[ 1, 2, 3, 7 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_004	metastatic	4	train	591	[ 1, 2, 7, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_005	metastatic	5	train	299	[ 1, 2, 3, 5, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_006	metastatic	6	train	281	[ 1, 2, 3, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_007	metastatic	7	train	266	[ 1, 2, 3, 4, 5, 7 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_008	metastatic	8	train	168	[ 1, 2, 10 ]	[ 3, 5 ]	0.2263	40
training_set_metastatic_roi_009	metastatic	9	train	502	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_010	metastatic	10	train	251	[ 1, 2, 3, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_011	metastatic	11	train	385	[ 1, 2, 3, 6, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_012	metastatic	12	train	407	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_013	metastatic	13	train	852	[ 1, 2, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_014	metastatic	14	train	474	[ 2, 3, 4, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_015	metastatic	15	train	295	[ 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_016	metastatic	16	train	381	[ 1, 2, 3, 4, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_017	metastatic	17	train	659	[ 1, 2, 4, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_018	metastatic	18	train	265	[ 1, 2, 3, 9, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_019	metastatic	19	train	415	[ 2, 3, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_020	metastatic	20	train	287	[ 1, 2, 3, 5, 6, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_021	metastatic	21	train	559	[ 1, 2 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_022	metastatic	22	train	624	[ 1, 2, 3, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_023	metastatic	23	train	425	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_024	metastatic	24	train	270	[ 2, 9, 10 ]	[ 3, 5 ]	0.2263	40
training_set_metastatic_roi_025	metastatic	25	train	639	[ 1, 2, 3, 4, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_026	metastatic	26	train	560	[ 1, 2, 3, 4, 5, 7, 9, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_027	metastatic	27	train	459	[ 1, 2, 3, 4, 5, 9, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_028	metastatic	28	train	633	[ 1, 2, 3, 4, 5, 6, 7, 9, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_029	metastatic	29	train	546	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_030	metastatic	30	train	771	[ 1, 2, 3, 4, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_031	metastatic	31	train	232	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_032	metastatic	32	train	450	[ 1, 2, 3, 4, 5, 6, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_033	metastatic	33	train	263	[ 1, 2, 3, 6 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_034	metastatic	34	train	377	[ 1, 2, 3, 4, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_035	metastatic	35	train	307	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_036	metastatic	36	train	567	[ 1, 2, 3, 5, 6, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_037	metastatic	37	train	534	[ 1, 2, 3, 5, 7, 9, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_038	metastatic	38	train	503	[ 1, 2, 3, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_039	metastatic	39	train	693	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_040	metastatic	40	train	297	[ 1, 2, 3, 7 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_041	metastatic	41	train	547	[ 1, 2, 3, 4, 5, 6 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_042	metastatic	42	train	466	[ 1, 2, 5, 6, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_043	metastatic	43	train	252	[ 1, 2, 3, 4, 5, 6, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_044	metastatic	44	train	475	[ 1, 2, 3, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_045	metastatic	45	train	325	[ 1, 2, 3, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_046	metastatic	46	train	438	[ 1, 2, 3, 4, 7, 9, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_047	metastatic	47	train	481	[ 1, 2, 3, 4, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_048	metastatic	48	train	333	[ 1, 2, 3, 6, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_049	metastatic	49	train	361	[ 1, 2, 3, 6 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_050	metastatic	50	train	565	[ 1, 2, 3, 4, 5, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_051	metastatic	51	train	780	[ 2 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_052	metastatic	52	train	466	[ 1, 2, 3, 5, 7, 10 ]	[ 1, 2, 3, 5 ]	0.2263	40
training_set_metastatic_roi_053	metastatic	53	train	530	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_054	metastatic	54	train	405	[ 1, 2, 7 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_055	metastatic	55	train	1,118	[ 1, 2, 3, 4, 5, 7 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_056	metastatic	56	train	346	[ 1, 2, 3, 7, 9, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_057	metastatic	57	train	608	[ 1, 2, 3, 4, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_058	metastatic	58	train	673	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_059	metastatic	59	train	746	[ 1, 2, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_060	metastatic	60	train	354	[ 1, 2, 3, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_061	metastatic	61	train	383	[ 1, 2, 9, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_062	metastatic	62	train	395	[ 1, 2, 3, 5, 7, 9, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_063	metastatic	63	train	489	[ 1, 2, 3, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_064	metastatic	64	train	658	[ 1, 2, 3, 4, 5, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_065	metastatic	65	train	553	[ 1, 2, 3, 4, 5, 7, 9, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_066	metastatic	66	train	808	[ 1, 2, 3, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_067	metastatic	67	train	905	[ 1, 2, 3, 4, 5, 7 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_068	metastatic	68	train	301	[ 1, 2, 3, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_069	metastatic	69	train	585	[ 1, 2, 3, 7 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_070	metastatic	70	train	448	[ 2, 3, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_071	metastatic	71	train	450	[ 1, 2, 3, 4, 5, 7, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_072	metastatic	72	train	754	[ 1, 2, 4, 7, 9, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_073	metastatic	73	train	755	[ 1, 2, 4, 7 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_074	metastatic	74	train	428	[ 1, 2, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_075	metastatic	75	train	376	[ 1, 2, 3, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_076	metastatic	76	train	432	[ 1, 2, 3, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_077	metastatic	77	train	434	[ 1, 2, 3, 6 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_078	metastatic	78	train	421	[ 1, 2, 3, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_079	metastatic	79	train	365	[ 1, 2, 3, 4, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_080	metastatic	80	train	331	[ 1, 2, 3, 4, 5, 6, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_081	metastatic	81	train	395	[ 1, 2, 3, 7 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_082	metastatic	82	train	514	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_083	metastatic	83	train	222	[ 1, 2, 3, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_084	metastatic	84	train	364	[ 1, 2, 4, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_085	metastatic	85	train	483	[ 1, 2, 3, 5, 6, 7 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_086	metastatic	86	train	459	[ 1, 2, 3, 5, 6, 7, 9 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_087	metastatic	87	train	623	[ 1, 2, 3, 7, 10 ]	[ 2, 3 ]	0.2263	40
training_set_metastatic_roi_088	metastatic	88	train	357	[ 1, 2, 3, 5, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_089	metastatic	89	train	437	[ 1, 2, 3, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_090	metastatic	90	train	489	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_091	metastatic	91	train	289	[ 1, 2, 3, 5, 7, 10 ]	[ 1, 3 ]	0.2263	40
training_set_metastatic_roi_092	metastatic	92	train	247	[ 1, 2, 3, 5, 6, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_093	metastatic	93	train	576	[ 1, 2, 3, 5, 6, 7 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_094	metastatic	94	train	671	[ 1, 2, 4, 5, 7, 10 ]	[ 1, 2, 3 ]	0.2263	40
training_set_metastatic_roi_095	metastatic	95	train	315	[ 1, 2, 3, 9, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_096	metastatic	96	train	376	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_097	metastatic	97	train	460	[ 1, 2 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_098	metastatic	98	train	625	[ 1, 2, 4, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_099	metastatic	99	train	400	[ 1, 2, 10 ]	[ 3 ]	0.2263	40
training_set_metastatic_roi_100	metastatic	100	train	305	[ 1, 2, 9, 10 ]	[ 3 ]	0.2263	40

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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.


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)

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