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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… See the full description on the dataset page: https://huggingface.co/datasets/hugging-science/isles24-stroke.

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Dataset Card

ISLES'24 Stroke Training Dataset

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

Dataset Description

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

SessionModalityDescription
ses-01 (Acute)ncctNon-contrast CT
ses-01 (Acute)ctaCT Angiography
ses-01 (Acute)ctp4D CT Perfusion time series
ses-01 (Acute)tmaxTime-to-maximum perfusion map
ses-01 (Acute)mttMean transit time map
ses-01 (Acute)cbfCerebral blood flow map
ses-01 (Acute)cbvCerebral blood volume map
ses-02 (Follow-up)dwiDiffusion-weighted MRI
ses-02 (Follow-up)adcApparent diffusion coefficient

Derivative Masks

MaskDescription
lesion_maskBinary infarct segmentation (from follow-up MRI)
lvo_maskLarge vessel occlusion mask (from CTA)
cow_maskCircle of Willis anatomy (multi-label, auto-generated from CTA)

Clinical Variables

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

VariableSource FileDescription
agedemographic_baseline.xlsxPatient age at admission
sexdemographic_baseline.xlsxPatient sex (M/F)
nihss_admissiondemographic_baseline.xlsxNIH Stroke Scale score at admission
mrs_admissiondemographic_baseline.xlsxModified Rankin Scale at admission
mrs_3monthoutcome.xlsxModified Rankin Scale at 3 months (primary outcome)

Usage

python
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:

bibtex
@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:

bibtex
@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}
}

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