hugging-science/isles24-stroke

ISLES'24 Stroke Training Dataset

Multi-center longitudinal multimodal acute ischemic stroke training dataset from the ISLES'24 Challenge.

Overview

149 acute ischemic stroke training cases with:

• Admission imaging (ses-01): Non-contrast CT, CT angiography, 4D CT perfusion
• Follow-up imaging (ses-02): Post-treatment MRI (DWI, ADC)
• Clinical data: Demographics, patient history, admission NIHSS, 3-month mRS outcomes
• Annotations: Infarct masks, large vessel occlusion masks, Circle of Willis anatomy

Note: The ISLES'24 paper describes a training set of 150 cases; the Zenodo v7 training archive contains 149 publicly released subjects.

Dataset Structure

Imaging Modalities

Session	Modality	Description
ses-01 (Acute)	ncct	Non-contrast CT
ses-01 (Acute)	cta	CT Angiography
ses-01 (Acute)	ctp	4D CT Perfusion time series
ses-01 (Acute)	tmax	Time-to-maximum perfusion map
ses-01 (Acute)	mtt	Mean transit time map
ses-01 (Acute)	cbf	Cerebral blood flow map
ses-01 (Acute)	cbv	Cerebral blood volume map
ses-02 (Follow-up)	dwi	Diffusion-weighted MRI
ses-02 (Follow-up)	adc	Apparent diffusion coefficient

Derivative Masks

Mask	Description
lesion_mask	Binary infarct segmentation (from follow-up MRI)
lvo_mask	Large vessel occlusion mask (from CTA)
cow_mask	Circle of Willis anatomy (multi-label, auto-generated from CTA)

Clinical Variables

Clinical variables are extracted from per-subject XLSX files in the phenotype/ directory:

Variable	Source File	Description
age	demographic_baseline.xlsx	Patient age at admission
sex	demographic_baseline.xlsx	Patient sex (M/F)
nihss_admission	demographic_baseline.xlsx	NIH Stroke Scale score at admission
mrs_admission	demographic_baseline.xlsx	Modified Rankin Scale at admission
mrs_3month	outcome.xlsx	Modified Rankin Scale at 3 months (primary outcome)

Usage

from datasets import load_dataset

ds = load_dataset("hugging-science/isles24-stroke", split="train")

# Access a subject
example = ds[0]
print(example["subject_id"])      # "sub-stroke0001"
print(example["ncct"])            # Non-contrast CT array
print(example["dwi"])             # Diffusion-weighted MRI
print(example["lesion_mask"])     # Ground truth segmentation
print(example["nihss_admission"]) # NIH Stroke Scale at admission
print(example["mrs_3month"])      # Modified Rankin Scale at 3 months

Data Organization

The source data follows BIDS structure. This tree shows the actual Zenodo v7 layout:

train/
├── clinical_data-description.xlsx
├── raw_data/
│   └── sub-stroke0001/
│       └── ses-01/
│           ├── sub-stroke0001_ses-01_ncct.nii.gz
│           ├── sub-stroke0001_ses-01_cta.nii.gz
│           ├── sub-stroke0001_ses-01_ctp.nii.gz
│           └── perfusion-maps/
│               ├── sub-stroke0001_ses-01_tmax.nii.gz
│               ├── sub-stroke0001_ses-01_mtt.nii.gz
│               ├── sub-stroke0001_ses-01_cbf.nii.gz
│               └── sub-stroke0001_ses-01_cbv.nii.gz
├── derivatives/
│   └── sub-stroke0001/
│       ├── ses-01/
│       │   ├── perfusion-maps/
│       │   │   ├── sub-stroke0001_ses-01_space-ncct_tmax.nii.gz
│       │   │   ├── sub-stroke0001_ses-01_space-ncct_mtt.nii.gz
│       │   │   ├── sub-stroke0001_ses-01_space-ncct_cbf.nii.gz
│       │   │   └── sub-stroke0001_ses-01_space-ncct_cbv.nii.gz
│       │   ├── sub-stroke0001_ses-01_space-ncct_cta.nii.gz
│       │   ├── sub-stroke0001_ses-01_space-ncct_ctp.nii.gz
│       │   ├── sub-stroke0001_ses-01_space-ncct_lvo-msk.nii.gz
│       │   └── sub-stroke0001_ses-01_space-ncct_cow-msk.nii.gz
│       └── ses-02/
│           ├── sub-stroke0001_ses-02_space-ncct_dwi.nii.gz
│           ├── sub-stroke0001_ses-02_space-ncct_adc.nii.gz
│           └── sub-stroke0001_ses-02_space-ncct_lesion-msk.nii.gz
└── phenotype/
    └── sub-stroke0001/
        ├── ses-01/
        └── ses-02/

Citation

When using this dataset, please cite:

@article{riedel2024isles,
  title={ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset},
  author={Riedel, Evamaria Olga and de la Rosa, Ezequiel and Baran, The Anh and
          Hernandez Petzsche, Moritz and Baazaoui, Hakim and Yang, Kaiyuan and
          Musio, Fabio Antonio and Huang, Houjing and Robben, David and
          Seia, Joaquin Oscar and Wiest, Roland and Reyes, Mauricio and
          Su, Ruisheng and Zimmer, Claus and Boeckh-Behrens, Tobias and
          Berndt, Maria and Menze, Bjoern and Rueckert, Daniel and
          Wiestler, Benedikt and Wegener, Susanne and Kirschke, Jan Stefan},
  journal={arXiv preprint arXiv:2408.11142},
  year={2024}
}

@article{delarosa2024isles,
  title={ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?},
  author={de la Rosa, Ezequiel and Su, Ruisheng and Reyes, Mauricio and
          Wiest, Roland and Riedel, Evamaria Olga and Kofler, Florian and
          others and Menze, Bjoern},
  journal={arXiv preprint arXiv:2408.10966},
  year={2024}
}

If using Circle of Willis masks, also cite:

@article{yang2023benchmarking,
  title={Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical
         Segmentation of the Circle of Willis for CTA and MRA},
  author={Yang, Kaiyuan and Musio, Fabio and Ma, Yue and Juchler, Norman and
          Paetzold, Johannes C and Al-Maskari, Rami and others and Menze, Bjoern},
  journal={arXiv preprint arXiv:2312.17670},
  year={2023}
}

Related Resources

• ISLES 2024 Challenge
• Zenodo Dataset (DOI: 10.5281/zenodo.17652035)
• Dataset Paper (arXiv:2408.11142)
• Challenge Paper (arXiv:2408.10966)