| --- |
| license: cc-by-nc-sa-4.0 |
| pretty_name: ISLES'24 (Ischemic Stroke Lesion Segmentation Challenge 2024) |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - medical-imaging |
| - stroke |
| - brain |
| - ct |
| - cta |
| - ct-perfusion |
| - mri |
| - dwi |
| - segmentation |
| - isles |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| dataset_info: |
| features: |
| - name: sub_id |
| dtype: string |
| - name: shape |
| dtype: string |
| - name: n_slices |
| dtype: int32 |
| - name: display_slice |
| dtype: int32 |
| - name: lesion_voxels |
| dtype: int64 |
| - name: has_lvo |
| dtype: bool |
| - name: cow_classes |
| dtype: int32 |
| - name: ncct |
| dtype: image |
| - name: cta |
| dtype: image |
| - name: tmax |
| dtype: image |
| - name: dwi |
| dtype: image |
| - name: adc |
| dtype: image |
| - name: lesion_overlay_ncct |
| dtype: image |
| - name: lesion_overlay_dwi |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 79728529 |
| num_examples: 149 |
| download_size: 79735586 |
| dataset_size: 79728529 |
| --- |
| |
| # ISLES'24 - Ischemic Stroke Lesion Segmentation Challenge 2024 |
|
|
| A real-world, **longitudinal, multimodal** acute-stroke dataset: acute admission |
| CT (NCCT, CTA, 4D CT-perfusion + derived perfusion maps), follow-up MRI |
| (DWI/ADC), and clinical tabular data, with the **final infarct lesion** as the |
| segmentation target. |
|
|
| > **This repository mirrors the public TRAINING set only (149 cases).** The |
| > 98-case ISLES'24 test set is withheld by the organizers for challenge scoring |
| > on Grand Challenge and is **not** part of any public release. |
|
|
| ## Provenance |
|
|
| - **Official source:** Zenodo record **16748089** - *"ISLES'24 - A Real-World |
| Longitudinal Multimodal Stroke Dataset"*, `train.7z`, CC BY-NC-SA 4.0 |
| (author-provided; not a third-party re-host). |
| - **Challenge:** https://isles-24.grand-challenge.org/ |
| - **Code / data-loading reference:** https://github.com/ezequieldlrosa/isles24 |
| - This mirror is an **unmodified raw copy** of the released volumes (no |
| resampling, no intensity changes), with two documented exceptions below. |
|
|
| ## Counts & faithfulness notes |
|
|
| - **149 training cases** (`sub-stroke0001` ... `sub-stroke0189`, non-contiguous): |
| University Hospital Munich (TUM) + University Hospital Zurich. |
| - The companion paper reports **N=150** for training; the public Zenodo release |
| ships **149** (one case excluded upstream). This mirror = the 149 released. |
| - **Dropped duplicate:** the upstream release contains one stray uncompressed |
| file `sub-stroke0142_ses-02_lesion-msk.nii` (a native-space leftover). Subject |
| 0142 also has the standard co-registered `*_space-ncct_lesion-msk.nii.gz` like |
| every other case, so this mirror keeps the standard GT and omits the stray. |
|
|
| ## Structure (BIDS) |
|
|
| ``` |
| <sub>/ sub-stroke0001 ... sub-stroke0189 (149) |
| ses-01 = acute admission CT |
| ses-02 = follow-up MRI (2-9 days later) |
| |
| raw_data/<sub>/ses-01/ # native acquisition space |
| *_ncct.nii.gz *_cta.nii.gz *_ctp.nii.gz (4D, 55 timepoints) |
| perfusion-maps/ *_{cbf,cbv,mtt,tmax}.nii.gz |
| |
| derivatives/<sub>/ # ALL co-registered to NCCT (space-ncct) |
| ses-01/ *_space-ncct_cta.nii.gz *_space-ncct_ctp.nii.gz (4D) |
| *_space-ncct_lvo-msk.nii.gz # large-vessel occlusion (CTA), binary |
| *_space-ncct_cow-msk.nii.gz # Circle-of-Willis anatomy (CTA), multi-class |
| perfusion-maps/ *_space-ncct_{cbf,cbv,mtt,tmax}.nii.gz |
| ses-02/ *_space-ncct_dwi.nii.gz *_space-ncct_adc.nii.gz |
| *_space-ncct_lesion-msk.nii.gz # <-- GROUND TRUTH (final infarct), binary |
| |
| phenotype/<sub>/ses-*/ *_demographic_baseline.csv *_outcome.csv |
| ``` |
|
|
| **Co-registration:** within each subject, the raw NCCT and every |
| `derivatives/*space-ncct*` volume (CTA, perfusion maps, DWI/ADC, all masks) |
| share an identical voxel grid (shape, spacing, affine). The raw NCCT itself is |
| the reference space (hence there is no `space-ncct_ncct`). So a fully aligned |
| input+label stack = raw `ncct` + the `derivatives` `space-ncct` modalities + |
| `space-ncct_lesion-msk`. |
|
|
| ## Ground truth |
|
|
| | Mask | Space | Type | Role | |
| |------|-------|------|------| |
| | `space-ncct_lesion-msk` (ses-02) | NCCT | binary {0,1} | **Gold standard** - final infarct lesion, the challenge target | |
| | `space-ncct_lvo-msk` (ses-01) | NCCT | binary {0,1} | Auxiliary - large-vessel occlusion (on CTA) | |
| | `space-ncct_cow-msk` (ses-01) | NCCT | multi-class | Auxiliary - Circle-of-Willis anatomy (on CTA) | |
|
|
| The **gold-standard lesion mask** is derived from the follow-up **DWI**: masks |
| were auto-generated by **DeepISLES** (the productized ISLES'22 winning ensemble) |
| and then **quality-controlled / manually corrected and verified by two |
| neuroradiologists** (>10 years' experience). The neuroradiologist-verified DWI |
| lesion is the reference standard; it is provided co-registered into NCCT space. |
|
|
| ## Cross-dataset overlap (leakage note) |
|
|
| - **No patient reuse** from **ISLES'22** (a different, MRI-only DWI cohort). |
| ISLES'24 is a new acute-stroke CT cohort. Modality and patients differ. |
| - There is **center-level** overlap (Munich contributes to both editions) and a |
| **methodological** dependency (ISLES'24 GT masks are produced by DeepISLES, |
| which was trained on ISLES'22) - but neither is shared imaging. |
| - No shared TCIA / BraTS / Medical Segmentation Decathlon lineage. Identifiers |
| are challenge-internal BIDS `sub-stroke####` IDs only. |
|
|
| ## License |
|
|
| **CC BY-NC-SA 4.0** (per the Zenodo deposit). Non-commercial, share-alike, |
| attribution required. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{delarosa2024isles24, |
| 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 Riedel, Eda Otman and Baazaoui, Hakim and Wiest, Roland and Kofler, Florian and Kirschke, Jan S. and Wiestler, Benedikt and Menze, Bjoern}, |
| journal = {arXiv preprint arXiv:2408.10966}, |
| year = {2024} |
| } |
| ``` |
|
|
| Data: Zenodo record 16748089 (de la Rosa et al., 2025). Please also credit the |
| ISLES'24 challenge organizers and the contributing hospitals (TUM Munich, USZ |
| Zurich). |
|
|
