| --- |
| 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). |
|
|
