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README.md
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---
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license: cc-by-nc-sa-4.0
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pretty_name: "ISLES'24 (Ischemic Stroke Lesion Segmentation Challenge 2024)"
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task_categories:
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- image-segmentation
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tags:
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- medical
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- medical-imaging
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- stroke
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- brain
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- ct
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- cta
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- ct-perfusion
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- mri
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- dwi
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- segmentation
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- isles
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size_categories:
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- n<1K
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---
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# ISLES'24 - Ischemic Stroke Lesion Segmentation Challenge 2024
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A real-world, **longitudinal, multimodal** acute-stroke dataset: acute admission
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CT (NCCT, CTA, 4D CT-perfusion + derived perfusion maps), follow-up MRI
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(DWI/ADC), and clinical tabular data, with the **final infarct lesion** as the
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segmentation target.
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> **This repository mirrors the public TRAINING set only (149 cases).** The
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> 98-case ISLES'24 test set is withheld by the organizers for challenge scoring
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> on Grand Challenge and is **not** part of any public release.
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## Provenance
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- **Official source:** Zenodo record **16748089** - *"ISLES'24 - A Real-World
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Longitudinal Multimodal Stroke Dataset"*, `train.7z`, CC BY-NC-SA 4.0
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(author-provided; not a third-party re-host).
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- **Challenge:** https://isles-24.grand-challenge.org/
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- **Code / data-loading reference:** https://github.com/ezequieldlrosa/isles24
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- This mirror is an **unmodified raw copy** of the released volumes (no
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resampling, no intensity changes), with two documented exceptions below.
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## Counts & faithfulness notes
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- **149 training cases** (`sub-stroke0001` ... `sub-stroke0189`, non-contiguous):
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University Hospital Munich (TUM) + University Hospital Zurich.
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- The companion paper reports **N=150** for training; the public Zenodo release
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ships **149** (one case excluded upstream). This mirror = the 149 released.
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- **Dropped duplicate:** the upstream release contains one stray uncompressed
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file `sub-stroke0142_ses-02_lesion-msk.nii` (a native-space leftover). Subject
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0142 also has the standard co-registered `*_space-ncct_lesion-msk.nii.gz` like
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every other case, so this mirror keeps the standard GT and omits the stray.
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## Structure (BIDS)
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```
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<sub>/ sub-stroke0001 ... sub-stroke0189 (149)
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ses-01 = acute admission CT
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ses-02 = follow-up MRI (2-9 days later)
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raw_data/<sub>/ses-01/ # native acquisition space
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*_ncct.nii.gz *_cta.nii.gz *_ctp.nii.gz (4D, 55 timepoints)
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perfusion-maps/ *_{cbf,cbv,mtt,tmax}.nii.gz
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derivatives/<sub>/ # ALL co-registered to NCCT (space-ncct)
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ses-01/ *_space-ncct_cta.nii.gz *_space-ncct_ctp.nii.gz (4D)
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*_space-ncct_lvo-msk.nii.gz # large-vessel occlusion (CTA), binary
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*_space-ncct_cow-msk.nii.gz # Circle-of-Willis anatomy (CTA), multi-class
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perfusion-maps/ *_space-ncct_{cbf,cbv,mtt,tmax}.nii.gz
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ses-02/ *_space-ncct_dwi.nii.gz *_space-ncct_adc.nii.gz
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*_space-ncct_lesion-msk.nii.gz # <-- GROUND TRUTH (final infarct), binary
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phenotype/<sub>/ses-*/ *_demographic_baseline.csv *_outcome.csv
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```
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**Co-registration:** within each subject, the raw NCCT and every
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`derivatives/*space-ncct*` volume (CTA, perfusion maps, DWI/ADC, all masks)
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share an identical voxel grid (shape, spacing, affine). The raw NCCT itself is
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the reference space (hence there is no `space-ncct_ncct`). So a fully aligned
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input+label stack = raw `ncct` + the `derivatives` `space-ncct` modalities +
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`space-ncct_lesion-msk`.
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## Ground truth
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| Mask | Space | Type | Role |
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|------|-------|------|------|
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| `space-ncct_lesion-msk` (ses-02) | NCCT | binary {0,1} | **Gold standard** - final infarct lesion, the challenge target |
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| `space-ncct_lvo-msk` (ses-01) | NCCT | binary {0,1} | Auxiliary - large-vessel occlusion (on CTA) |
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| `space-ncct_cow-msk` (ses-01) | NCCT | multi-class | Auxiliary - Circle-of-Willis anatomy (on CTA) |
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The **gold-standard lesion mask** is derived from the follow-up **DWI**: masks
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were auto-generated by **DeepISLES** (the productized ISLES'22 winning ensemble)
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and then **quality-controlled / manually corrected and verified by two
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neuroradiologists** (>10 years' experience). The neuroradiologist-verified DWI
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lesion is the reference standard; it is provided co-registered into NCCT space.
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## Cross-dataset overlap (leakage note)
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- **No patient reuse** from **ISLES'22** (a different, MRI-only DWI cohort).
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ISLES'24 is a new acute-stroke CT cohort. Modality and patients differ.
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- There is **center-level** overlap (Munich contributes to both editions) and a
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**methodological** dependency (ISLES'24 GT masks are produced by DeepISLES,
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which was trained on ISLES'22) - but neither is shared imaging.
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- No shared TCIA / BraTS / Medical Segmentation Decathlon lineage. Identifiers
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are challenge-internal BIDS `sub-stroke####` IDs only.
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## License
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**CC BY-NC-SA 4.0** (per the Zenodo deposit). Non-commercial, share-alike,
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attribution required.
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## Citation
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```bibtex
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@article{delarosa2024isles24,
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title = {ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?},
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author = {de la Rosa, Ezequiel and Su, Ruisheng and Reyes, Mauricio and Riedel, Eda Otman and Baazaoui, Hakim and Wiest, Roland and Kofler, Florian and Kirschke, Jan S. and Wiestler, Benedikt and Menze, Bjoern},
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journal = {arXiv preprint arXiv:2408.10966},
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year = {2024}
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}
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```
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Data: Zenodo record 16748089 (de la Rosa et al., 2025). Please also credit the
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ISLES'24 challenge organizers and the contributing hospitals (TUM Munich, USZ
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Zurich).
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