---
license: cc0-1.0
task_categories:
  - image-segmentation
tags:
  - medical
  - pathology
  - h-and-e
  - breast
  - segmentation
  - bcss
size_categories:
  - n<1K
---

# BCSS — Breast Cancer Semantic Segmentation (Amgad et al. 2019)

Re-hosted mirror of the **Breast Cancer Semantic Segmentation** dataset
(Amgad et al., *Bioinformatics* 2019), originally distributed via the
[PathologyDataScience/BCSS](https://github.com/PathologyDataScience/BCSS)
GitHub repo and rebuilt here from the
[`nabil-m/bcss`](https://huggingface.co/datasets/nabil-m/bcss) HF mirror.

The data is **CC0 1.0** (public domain, no rights reserved); the upstream
codebase is MIT-licensed but covers software, not data. Redistribution is
unrestricted.

## Composition

| Split | ROIs |
|-------|-----:|
| train | 151  |

151 ROI patches extracted from TCGA breast cancer whole-slide images.
Patches are **color-normalized** RGB at the upstream MPP=0.25 µm/px
(40× equivalent), with native ROI resolution typically 2–4k px per
side. There is **no official train/val/test split** — group-shuffle by
`patient_id` downstream for honest evaluation.

## Schema

| Column      | Type     | Description                                           |
|-------------|----------|-------------------------------------------------------|
| `image`     | `Image`  | RGB ROI (PNG, color-normalized, variable size)        |
| `mask`      | `Image`  | Indexed 22-class mask (`L`, values 0..21)             |
| `image_id`  | `string` | Filename stem incl. xmin/ymin                         |
| `patient_id`| `string` | TCGA-XX-YYYY prefix                                   |
| `xmin`      | `int32`  | ROI bbox xmin in WSI base-magnification pixels        |
| `ymin`      | `int32`  | ROI bbox ymin in WSI base-magnification pixels        |

## Mask labels

| Code | Class                  | | Code | Class                |
|-----:|------------------------|-|-----:|----------------------|
| 0    | outside_roi (don't care) | | 11   | other_immune_infiltrate |
| 1    | tumor                  | | 12   | mucoid_material      |
| 2    | stroma                 | | 13   | normal_acinus_or_duct |
| 3    | lymphocytic_infiltrate | | 14   | lymphatics           |
| 4    | necrosis_or_debris     | | 15   | undetermined         |
| 5    | glandular_secretions   | | 16   | nerve                |
| 6    | blood                  | | 17   | skin_adnexa          |
| 7    | exclude                | | 18   | blood_vessel         |
| 8    | metaplasia_NOS         | | 19   | angioinvasion        |
| 9    | fat                    | | 20   | dcis                 |
| 10   | plasma_cells           | | 21   | other                |

**Code 0 (`outside_roi`) is a "don't care" region** — the original paper
recommends excluding it from any loss. For binary tumor evaluation,
the canonical foreground is class 1.

## License

CC0 1.0 Universal — public domain. No rights reserved.

## Citation

```bibtex
@article{amgad2019structured,
  title   = {Structured crowdsourcing enables convolutional segmentation of histology images},
  author  = {Amgad, Mohamed and Elfandy, Habiba and Hussein, Hagar and others},
  journal = {Bioinformatics},
  volume  = {35},
  number  = {18},
  pages   = {3461--3467},
  year    = {2019},
  doi     = {10.1093/bioinformatics/btz083}
}
```