ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916827693580288 |
|---|---|
| author | Riedel, Evamaria Olga de la Rosa, Ezequiel Baran, The Anh Petzsche, Moritz Hernandez Baazaoui, Hakim Yang, Kaiyuan Musio, Fabio Antonio Huang, Houjing Robben, David Seia, Joaquin Oscar Wiest, Roland Reyes, Mauricio Su, Ruisheng Zimmer, Claus Boeckh-Behrens, Tobias Berndt, Maria Menze, Bjoern Rueckert, Daniel Wiestler, Benedikt Wegener, Susanne Kirschke, Jan Stefan |
| author_facet | Riedel, Evamaria Olga de la Rosa, Ezequiel Baran, The Anh Petzsche, Moritz Hernandez Baazaoui, Hakim Yang, Kaiyuan Musio, Fabio Antonio Huang, Houjing Robben, David Seia, Joaquin Oscar Wiest, Roland Reyes, Mauricio Su, Ruisheng Zimmer, Claus Boeckh-Behrens, Tobias Berndt, Maria Menze, Bjoern Rueckert, Daniel Wiestler, Benedikt Wegener, Susanne Kirschke, Jan Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11142 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset Riedel, Evamaria Olga de la Rosa, Ezequiel Baran, The Anh Petzsche, Moritz Hernandez Baazaoui, Hakim Yang, Kaiyuan Musio, Fabio Antonio Huang, Houjing Robben, David Seia, Joaquin Oscar Wiest, Roland Reyes, Mauricio Su, Ruisheng Zimmer, Claus Boeckh-Behrens, Tobias Berndt, Maria Menze, Bjoern Rueckert, Daniel Wiestler, Benedikt Wegener, Susanne Kirschke, Jan Stefan Computer Vision and Pattern Recognition 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. |
| title | ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.11142 |