# ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset

arXiv: https://arxiv.org/abs/2408.11142 · HTML: https://mlsanity.com/paper/2408.11142/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 20 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset](https://mlsanity.com/paper/2206.06694/) (2022)
- [Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning](https://mlsanity.com/paper/1812.02496/) (2018)
- [A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge](https://mlsanity.com/paper/2403.19425/) (2024)
- [AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning](https://mlsanity.com/paper/2010.01617/) (2020)
- [APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge](https://mlsanity.com/paper/2309.15243/) (2023)

## Built on by (most cited first, 6 of 6)

- [Benchmarking DINOv3 for Multi-Task Stroke Analysis on Non-Contrast CT](https://mlsanity.com/paper/2509.23132/) (2025)
- [Robust automatic brain vessel segmentation in 3D CTA scans using dynamic 4D-CTA data](https://mlsanity.com/paper/2602.00391/) (2026)
- [Bi-temporal Image-driven Acute Stroke Evolution Analysis](https://mlsanity.com/paper/2602.07535/) (2026)
- [AsymFeX: A Symmetry-Driven Framework for Ischemic Stroke Segmentation Across Imaging Modalities and Stroke Stages](https://mlsanity.com/paper/2608.19769/) (2026)
- [From Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation](https://mlsanity.com/paper/2608.26856/) (2026)
- [Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images](https://mlsanity.com/paper/2609.16889/) (2026)

## Abstract (arXiv)

Stroke remains a leading cause of global morbidity and mortality, imposing a heavy socioeconomic burden. Advances in endovascular reperfusion therapy and CT and MR imaging for treatment guidance have significantly improved patient outcomes. Developing machine learning algorithms that can create accurate models of brain function from stroke images for tasks like lesion identification and tissue survival prediction requires large, diverse, and well annotated public datasets. While several high-quality image datasets in stroke exist, they include only single time point data. Data over different time points are essential to accurately identify lesions and predict prognosis. Here, we provide comprehensive longitudinal stroke data, including (sub-)acute CT imaging with angiography and perfusion, follow-up MRI after 2-9 days, and acute and longitudinal clinical data up to a three-month outcome. The dataset also includes vessel occlusion masks from acute CT angiography and delineated infarction masks in follow-up MRI. This multicenter dataset consists of 245 cases and is a solid basis for developing powerful machine-learning algorithms to facilitate clinical decision-making.