---
license: cc-by-nc-sa-4.0
task_categories:
  - image-segmentation
  - object-detection
language:
  - en
tags:
  - medical
  - pathology
  - histopathology
  - h-and-e
  - pd-l1
  - lung
  - nsclc
  - ignite
size_categories:
  - n<1K
configs:
  - config_name: he
    data_files:
      - split: train
        path: he/train-*
      - split: test
        path: he/test-*
  - config_name: pdl1
    data_files:
      - split: train
        path: pdl1/train-*
      - split: validation
        path: pdl1/validation-*
      - split: test
        path: pdl1/test-*
  - config_name: nuclei
    data_files:
      - split: train
        path: nuclei/train-*
      - split: validation
        path: nuclei/validation-*
      - split: test
        path: nuclei/test-*
---

# IGNITE Data Toolkit (mirror)

Mirror of the **IGNITE Data Toolkit** by Spronck et al. (Radboud UMC),
originally distributed on [Zenodo (10.5281/zenodo.15674785)](https://zenodo.org/records/15674785)
and accompanied by [DIAGNijmegen/ignite-data-toolkit](https://github.com/DIAGNijmegen/ignite-data-toolkit).
The dataset accompanies *"A tissue and cell-level annotated H&E and PD-L1 histopathology image
dataset in non-small cell lung cancer"* ([arXiv:2507.16855](https://arxiv.org/abs/2507.16855)).

**License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) -
non-commercial, share-alike. Attribution to the original authors is required.

## Contents

155 unique patients, 887 fully annotated regions of interest from a multi-stain,
multi-centric, multi-scanner cohort (Radboud UMC, Sacro Cuore Don Calabria, TCGA-LUAD/LUSC).
The release splits into three task-defined subsets, exposed here as named configs:

| Config   | Task                                  | ROIs |
|----------|---------------------------------------|-----:|
| `he`     | H&E tissue compartment segmentation   |  408 |
| `pdl1`   | PD-L1+ tumor cell detection           |  344 |
| `nuclei` | PD-L1 IHC nuclei detection            |  135 |

## H&E tissue segmentation (`he` config)

Splits follow `data_overview.csv`:

| Split | ROIs | Notes                                                            |
|-------|-----:|------------------------------------------------------------------|
| train |  269 | Train pool — paper uses 5-fold CV via the `validation_fold` col. |
| test  |  139 | Held-out evaluation set (62 TCGA + 77 Radboud ROIs).             |

Each row is **one ROI** with paired image/mask in two field-of-view variants:

| Column                 | Type    | Description                                                  |
|------------------------|---------|--------------------------------------------------------------|
| `image`                | `Image` | Base ROI (inner annotated region only)                       |
| `mask`                 | `Image` | 16-class pixel mask aligned to `image`                       |
| `image_with_context`   | `Image` | Same ROI extended to a 1792x1792 view (annotated context)    |
| `mask_with_context`    | `Image` | 16-class pixel mask aligned to `image_with_context`          |
| `validation_fold`      | string  | 5-fold CV assignment (`fold0`..`fold4`); empty for test rows |
| `patient_id`           | int32   | Patient identifier                                           |
| `roi_id`               | int32   | ROI index within patient                                     |
| `name`                 | string  | `patient<id>_he_roi<idx>` (matches the original release)     |
| `source`               | string  | `rumc`, `scdc`, or `tcga`                                    |
| `specimen_type`        | string  | `resection`, `biopsy`, or `tissue_microarray`                |
| `organ`                | string  | Anatomical site (lung, liver, bone, brain, ...)              |
| `histological_subtype` | string  | `adenocarcinoma`, `squamous_cell_carcinoma`, ...             |
| `stain`                | string  | Always `H&E` for this config                                 |
| `scanner`              | string  | WSI scanner model                                            |
| `shape`                | string  | Original `(height, width)` tuple as a string                 |
| `area_mm2`             | float32 | Annotated tissue area in mm^2                                |
| `original_tcga_id`     | string  | TCGA case ID for TCGA-sourced ROIs (empty otherwise)         |

Labels (also shipped as `he_label_map.json`):

| ID | Class                | ID | Class                |
|---:|----------------------|---:|----------------------|
| 0  | Unannotated          | 9  | Erythrocytes         |
| 1  | Background           | 10 | Bronchial epithelium |
| 2  | Tumor epithelium     | 11 | Mucus/Plasma/Fluids  |
| 3  | Reactive epithelium  | 12 | Cartilage/Bone       |
| 4  | Stroma               | 13 | Macrophages          |
| 5  | Inflammation         | 14 | Muscle               |
| 6  | Alveolar tissue      | 15 | Liver                |
| 7  | Fatty tissue         | 16 | Keratinization       |
| 8  | Necrotic tissue      |    |                      |

The paper's evaluation pipeline treats class `0` ("Unannotated", i.e. surrounding context
in `_with_context` masks) as an **ignore label** during Dice/IoU computation. Downstream
loaders should mirror that to reproduce paper-comparable scores.

> **Mirror-specific note:** In the original Zenodo release, base ROI masks (the inner-crop view)
> store class label `L` as the byte value `(256 - L) mod 256` (e.g. label 4 -> byte 252).
> The `_with_context` masks already store labels directly. In this HuggingFace mirror **both
> `mask` and `mask_with_context` are written with the canonical 0..16 labels** - base masks were
> pre-decoded during upload, so downstream code does not need to handle the encoding quirk.

The paper recommends training-time 5-fold CV via the `validation_fold` column on the `train`
split, and reports final numbers on the held-out `test` split.

## PD-L1 / nuclei detection (`pdl1`, `nuclei` configs)

These configs hold images plus per-image metadata only (same columns as `he` except no
`mask`/`_with_context` fields and no `validation_fold`). The detection ground truth is
in **MS-COCO JSON** format and is shipped as raw sidecar files because COCO-style nested
annotations are a poor fit for columnar parquet:

| Path                                | Subset | Notes                                      |
|-------------------------------------|--------|--------------------------------------------|
| `coco/pdl1_annotations.json`        | pdl1   | Main annotations                           |
| `coco/pdl1_test_set_all_readers.json` | pdl1 | Multi-reader test set                      |
| `coco/nuclei_annotations.json`      | nuclei | Main annotations                           |
| `coco/nuclei_test_set_all_readers.json` | nuclei | Multi-reader test set                  |

Use the row's `name` field (== `image_id` in COCO `images[*].file_name = "<name>.png"`)
to look up bounding-box / point annotations.

Splits follow `data_overview.csv` directly (no fold column for the detection tasks).

## Loading

```python
from datasets import load_dataset

# H&E tissue segmentation
he_train = load_dataset("Angelou0516/IGNITE", "he", split="train")  # 269 ROIs
he_test  = load_dataset("Angelou0516/IGNITE", "he", split="test")   # 139 ROIs
print(he_test[0]["mask_with_context"])  # PIL Image L-mode, labels 0..16

# PD-L1+ tumor cell detection
pdl1 = load_dataset("Angelou0516/IGNITE", "pdl1")

# PD-L1 IHC nuclei detection
nuc = load_dataset("Angelou0516/IGNITE", "nuclei")
```

For detection COCO annotations, download the JSON sidecars with `huggingface_hub.hf_hub_download`.

## Sidecar files (raw)

- `he_label_map.json` — class id -> name
- `data_overview.csv` — per-ROI metadata (887 rows x 17 cols), authoritative for splits / folds
- `coco/*.json` — detection annotations (4 files, see table above)

## Citation

```bibtex
@article{Spronck2025ignite,
  title   = {A tissue and cell-level annotated H\&E and PD-L1 histopathology image dataset in non-small cell lung cancer},
  author  = {Spronck, Joey and van Eekelen, Leander and van Midden, Dominique and others},
  journal = {arXiv preprint arXiv:2507.16855},
  year    = {2025},
  doi     = {10.48550/arXiv.2507.16855}
}
```

Mirror maintained by `Angelou0516`. For the official authoritative release see the
[Zenodo record](https://zenodo.org/records/15674785) and
[GitHub toolkit](https://github.com/DIAGNijmegen/ignite-data-toolkit).