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Published June 20, 2025 | Version v1
Dataset Open

IGNITE data toolkit: a tissue and cell-level annotated H&E and PD-L1 histopathology image dataset in non-small cell lung cancer

Creators

  • 1. ROR icon Radboud University Medical Center
  • 2. ROR icon Ospedale Sacro Cuore Don Calabria
  • 3. ROR icon Ospedale di Bolzano
  • 4. University and Hospital Trust of Verona
  • 5. ROR icon Karolinska Institutet
  • 6. ROR icon Biopticka Laborator (Czechia)

Description

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We introduce the IGNITE data toolkit, a multi-stain, multi-centric, and multi-scanner dataset of annotated non-small cell lung cancer (NSCLC) whole-slide images (WSIs). We publicly release 887 fully annotated regions of interest (ROI) from 155 unique patients across three complementary tasks:

  1. Multi-class semantic segmentation of tissue compartments in H&E-stained slides, with 16 classes spanning primary and metastatic NSCLC
  2. Nuclei detection in PD-L1 stained immunohistochemistry (IHC)
  3. Positive tumor cell detection in PD-L1 IHC

Technical info

Repository content

This repository contains four zip files, with each of the files having the following directory structure when unpacked:

.
└── {images,annotations,models,inference,figures}/
    ├── he/               # Files pertaining to the H&E tissue compartment segmentation dataset...
    └── pdl1/
        ├── nuclei/    # ... the PD-L1 IHC nuclei detection dataset..
        └── pdl1/       # ... and the PD-L1 positive tumor cell detection dataset

The four zip files contain the following:

  • 'annotations.zip' contains single-channel PNG masks for the H&E tissue compartment segmentation dataset (the label map is under 'he_label_map.json'); the zip file also contains MS COCO-formatted JSON files for the nuclei/PD-L1 positive tumor cell detection datasets
  • 'figures.zip' contains neatly visualized inference and evaluation metric figures from our paper
  • 'images.zip' contains PNG images of the ROIs released in the toolkit
  • 'inference.zip' contains raw inference of the models for the respective datasets
  • 'models.zip' contains the weights for our final models used for the technical validation of the toolkit

File ID nomenclature

All patients were assigned a unique anonymous patient ID incrementing from 1. Images/masks are named according to the patient/dataset/ROI they originate from, following the naming scheme <anonymous_patient_id>_<dataset>_<roi_id>.<suffix>, e.g 'patient1_he_roi1.png. Note that some patients occur in multiple datasets, but always keep the same anonymous patient ID. However, their ROIs are always different across datasets, e.g. 'patient1_he_roi1.png' , 'patient1_nuclei_roi1.png'  and 'patient1_pdl1_roi1.png' all refer to separate, non-overlapping regions.

Dataset overview

Lastly, we include a 'data_overview.csv' file that documents metadata per ROI. We provide a table below that lists what metadata each column contains.

Column Contents
‘patient_id’ Unique anonymous patient ID. See ‘File ID nomenclature’.
‘roi_id’ ROI ID, see ‘File ID nomenclature’.
‘name’ Full name of ROI, e.g. ‘patient1_he_roi1’
‘task’ Dataset label: ’he_tissue_segmentation’, ‘nuclei_detection’ or ‘pdl1_detection’
‘source’ (Hospital) data source: ‘rumc’, ‘scdc’ or ‘tcga’
‘specimen_type’ WSI specimen type: ‘biopsy’, ‘resection’ or ‘tissue_micro_array’
‘organ’ Organ the tissue originated from
‘histological_subtype’ NSCLC subtype of the parent WSI (not necessarily of the ROI, as it may not contain tumor cells).
‘stain’ ‘H&E’ or ‘PDL1_{monoclone}’
‘scanner’ Scanner used to digitize the image
‘image_path’ Image path relative to ‘data/’
‘annotation_path’ Annotation path relative to ‘data/’
‘shape’ (width,height) shape of the ROI. Important caveat: for ROIs released with non-annotated context borders, this shape refers only to the annotated part of the image.
‘area_mm2’ Annotated ROI area in mm^2
‘split’ Dataset split: train/validation/test
‘validation_fold’ For H&E tissue compartment segmentation dataset, validation fold of 5 fold cross validation
'original_tcga_id' For cases originating from the TCGA dataset, we list their original TCGA ID.

 

Files

data_overview.csv

Files (8.2 GB)

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Additional details

Funding

Dutch Research Council
Predicting Lung Cancer Immunotherapy Response. It’s personal. 18388

Software

Repository URL
https://github.com/DIAGNijmegen/ignite-data-toolkit
Programming language
Python
Development Status
Wip