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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).
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