qhjqhj00 isles24-segmentation-eval
ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset — Riedel et al. (2024) (arXiv:2408.11142, 2024)
What this evaluates
Evaluates the ability of models to perform 3D medical image segmentation for stroke lesion (infarct) and vessel occlusion detection using longitudinal multimodal CT and MRI scans.
Datasets
- ISLES'24 — total 245; splits: train (149), test (96); repo https://github.com/ezequieldrosa/isles24
Metrics
Dice Similarity Coefficient (DSC)(primary) — range: [0, 1]- DSC = 2 * |Prediction ∩ Ground Truth| / (|Prediction| + |Ground Truth|). Standard metric for binary segmentation challenges in medical imaging.
Input / output format
Input: 3D NIfTI medical images (NCCT, CTA, CTP, DWI, ADC, FLAIR) co-registered to NCCT space, plus tabular clinical data.
Output: 3D binary segmentation masks (NIfTI) indicating infarct lesions and/or vessel occlusion locations.
Scoring recipe
def compute_dice(pred_mask, gt_mask):
intersection = np.sum(pred_mask & gt_mask)
union = np.sum(pred_mask) + np.sum(gt_mask)
return (2.0 * intersection) / union if union > 0 else 0.0
Common pitfalls
- Test set is hidden and evaluated via the ISLES'24 Grand Challenge website, not locally.
- Images require strict co-registration to NCCT space; misalignment severely impacts segmentation metrics.
- Clinical tabular data is randomly altered by ±5% for anonymization, so models should not rely on exact numerical values for clinical variables.
Evidence (verbatim from paper)
The loss was a combined CE, Dice, Skeleton Recall 37 , and a customized topology loss.
Citation
@misc{riedel2024isles24,
title={ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset},
author={Riedel et al. (2024)},
year={2024},
note={arXiv:2408.11142}
}
- arXiv: 2408.11142