README.md

---
license: cc-by-4.0
task_categories:
  - image-segmentation
tags:
  - medical
  - histopathology
  - whole-slide-imaging
  - veterinary
  - canine
  - dermatology
  - oncology
size_categories:
  - n<1K
---

# CATCH — CAnine CuTaneous Cancer Histology

350 whole-slide images of **canine** skin tumors (H&E), covering **7 tumor
subtypes** with dense multi-class region annotations by a veterinary
pathologist. Mirrored for the MedOtter segmentation benchmark.

> Wilm F., Fragoso M., Marzahl C., Qiu J., Puget C., Diehl L., Bertram C.A.,
> Klopfleisch R., Maier A., Breininger K.*, Aubreville M.* —
> *Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset*,
> **Scientific Data** 9, 588 (2022). doi:10.1038/s41597-022-01692-w
>
> Data DOI: [10.7937/TCIA.2M93-FX66](https://doi.org/10.7937/TCIA.2M93-FX66) ·
> Source: [TCIA CATCH collection](https://www.cancerimagingarchive.net/collection/catch/)

## What this mirror contains — read before using

The originals are 350 Aperio `.svs` slides totalling **522 GB** at 0.2533 µm/px,
distributed by TCIA behind an Aspera plugin. This mirror stores each slide
rendered at the **4 µm/px pyramid level** — the exact resolution the CATCH
paper's own segmentation baseline operates at (512×512 px ≙ 2048×2048 µm) —
as a lossless PNG, paired with a 13-class indexed mask rasterized at the same
level. It is a **derived, downsampled** representation, not the original WSIs.

For full-resolution work, use TCIA. The complete original polygon annotations
are included here as `CATCH.json` so any other pyramid level can be re-derived.

| | |
|---|---|
| Slides | 350 |
| Patients | 282 |
| Resolution | 4.05 µm/px (pyramid level 2, ≈16× downsample, all 350 slides) |
| Image size | median 6025×4644, max 11812×6046, mean 29.7 Mpx |
| Polygons | 12,424 |
| Classes | 13 (+ label 0 = unannotated) |
| Splits | train 245 / val 35 / test 70 (official, patient-level) |

## Splits

The official split from the authors' [CanineCutaneousTumors](https://github.com/DeepPathology/CanineCutaneousTumors)
repo, balanced at **35 / 5 / 10 slides per subtype**. It is patient-level:
**no patient appears in two splits** (verified across all 282 patients).

A slide's patient is the filename prefix `<Subtype>_<NN>` — 41 patients
contribute 2 slides, 8 contribute 3, 2 contribute 4, and 1 contributes 6.
**Group on `patient_id`, not on `slide`.**

## Labels

`mask` is a single-channel uint8 PNG. **Label 0 means *unannotated*, not
background** — CATCH has no background class by design, and the authors
exclude unannotated tissue from training and evaluation. Treat 0 as
*don't-care*, or synthesize a background class by Otsu-thresholding the white
point per slide, which is what the paper's baseline does.

| ID | Class | Group | Slides present |
|---|---|---|---|
| 0 | unannotated | — | 350 |
| 1 | Bone | Tissue | 21 |
| 2 | Cartilage | Tissue | 4 |
| 3 | Dermis | Tissue | 322 |
| 4 | Epidermis | Tissue | 321 |
| 5 | Subcutis | Tissue | 246 |
| 6 | Inflamm/Necrosis | Tissue | 149 |
| 7 | Melanoma | Tumor | 50 |
| 8 | Plasmacytoma | Tumor | 50 |
| 9 | Mast Cell Tumor | Tumor | 50 |
| 10 | PNST | Tumor | 50 |
| 11 | SCC | Tumor | 50 |
| 12 | Trichoblastoma | Tumor | 50 |
| 13 | Histiocytoma | Tumor | 50 |

**Bone (21 slides) and Cartilage (4 slides) are too rare for class-averaged
metrics** — the authors exclude both from their own baseline. Do the same.

**Exactly one tumor class occurs per slide**, and it always equals the slide's
subtype (verified 350/350). `tumor_class_id` / `tumor_class_name` give it
directly, so a binary tumor-vs-rest target needs no lookup.

### Polygons are hierarchical — rasterize in file order

Annotations nest: the dermis encircles a tumor mass, and islands of normal
dermis sit inside the tumor. Masks here are rasterized in **COCO file order**,
which is the authors' documented sort — *"polygons are sorted in increasing
order of their hierarchy level, i.e. polygons enclosed by another will be read
out after their enclosing polygon"* — so a later fill correctly overwrites the
region it sits within.

⚠️ **Do not sort by the `area` field instead.** `area` is the shoelace area, so
a polygon drawn as a *ring* around a tumor reports a **smaller** area than the
blob it encloses, while a standard polygon fill (`cv2.fillPoly`, `PIL
ImageDraw.polygon`) fills its outer boundary solid. Sorting area-descending
therefore paints the ring last and **buries the tumor completely**. Measured on
this data, it destroys the entire tumor annotation on `Plasmacytoma_08_1`,
`Trichoblastoma_31_2` and `Trichoblastoma_34_1`.

File order reproduces the per-class slide presence of the source polygons
**exactly** for all 13 classes across all 350 slides; area-descending does not.

## Columns

`image` · `mask` · `slide` · `stem` · `subtype` · `patient_id` · `split` ·
`scanner` · `tumor_class_id` · `tumor_class_name` · `width` · `height` · `mpp` ·
`downsample` · `level0_width` · `level0_height` · `annotated_frac` ·
`classes_present`

`annotated_frac` is the fraction of canvas carrying a label (median ≈ 0.50;
much of the remainder is glass, not untraced tissue).

## Overlap with other datasets — leakage warnings

- **Multi-Scanner Canine Cutaneous SCC** ([Zenodo 7418555](https://zenodo.org/records/7418555))
  re-scans **44 of CATCH's 50 SCC slides** on 4 additional scanners (220 images,
  also distributed at 4 µm/px). It is **not joinable by name** — its files are
  renumbered `scc_01`…`scc_44` and the shipped COCO/SQLite carry no CATCH
  provenance — but it **is joinable by 4 µm/px image dimensions**: each of its
  44 Aperio-CS2 images matches exactly one CATCH SCC slide, **44/44 with zero
  ambiguity**. The six SCC slides *not* re-scanned are `SCC_08_1`, `SCC_11_1`,
  `SCC_12_3`, `SCC_16_1`, `SCC_27_1`, `SCC_28_1`; exposure by split is
  train 30/35, val 5/5, test 9/10, over 29 of the 33 SCC patients.
- **MIDOG++ / MIDOG 2022 Domain 4** is 50 canine cutaneous mast cell tumor
  cases from the same archive, scanner and resolution as CATCH's 50 MCT
  slides. The reuse is undocumented and **no cross-reference ID exists** — the
  naming schemes are not joinable. Treat the MCT subset as potentially
  contaminated if you also use MIDOG.
- **CCMCT / MITOS_WSI_CCMCT** (32 canine cutaneous MCT WSIs) may likewise
  overlap the MCT subset. Also unjoinable.
- **No overlap with human histopathology sets** (TCGA-derived, PanNuke,
  MoNuSeg, MoNuSAC, NuCLS, CoNIC, CAMELYON, …) — different species.

⚠️ Web summaries claiming "CATCH is on Zenodo as 4 µm/px TIFFs" are **wrong**;
that record is the 44-slide Multi-Scanner SCC derivative, not CATCH.

## Annotation provenance

One annotation tier is released. Pathologist **M. Fragoso** drew ~82% of the
annotations; the remainder was drawn by three medical students and then
reviewed for correctness and completeness by M. Fragoso. The distributed
SQLite has exactly one entry in its `Persons` table — a single merged layer,
with no algorithmic pre-annotation. Two further veterinary pathologists
annotated one ROI on each of the 70 test slides for an inter-rater study;
**that data was never published** and is not part of this dataset.

Reported reliability (paper Table 3, generalized conformity index): tumor
0.8514, epidermis 0.7512. Dermis/subcutis are the weakest pair, and
inflammation/necrosis vs tumor is the other main confusion axis.

## License

**CC BY 4.0**, as stated for all three data rows on the
[TCIA collection page](https://www.cancerimagingarchive.net/collection/catch/),
the authors' designated distribution channel.

⚠️ **Discrepancy, disclosed for transparency:** the `licenses` block *inside*
the official `CATCH.json` declares `Attribution-NonCommercial-NoDerivs 2.0`.
This appears to be a COCO-export template default rather than a deliberate
choice, and it is contradicted by TCIA's own Data Access table and by the
CC BY 4.0 paper. We treat the TCIA statement as controlling. If your use is
commercial or derivative-heavy, verify with the authors first.

Users must also abide by the
[TCIA Data Usage Policy](https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/).
Please cite the paper and the data DOI above.

## Reproducing this mirror

Level 2 of each remote `.svs` is read via HTTP byte-range requests — a `.svs`
is a pyramidal TIFF, so pulling only that level costs **0.79% of each file
(4.1 GB total instead of 522 GB)** and needs no Aspera client and no login.
`CATCH.json` is then rasterized in file order at the same level.