Computer Science > Computer Vision and Pattern Recognition
arXiv:2206.06694 (cs)
[Submitted on 14 Jun 2022]
Title:ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Authors:Moritz Roman Hernandez Petzsche,Ezequiel de la Rosa,Uta Hanning,Roland Wiest,Waldo Enrique Valenzuela Pinilla,Mauricio Reyes,Maria Ines Meyer,Sook-Lei Liew,Florian Kofler,Ivan Ezhov,David Robben,Alexander Hutton,Tassilo Friedrich,Teresa Zarth,Johannes Bürkle, TheAnh Baran,Bjoern Menze,Gabriel Broocks,Lukas Meyer,Claus Zimmer,Tobias Boeckh-Behrens,Maria Berndt,Benno Ikenberg,Benedikt Wiestler,Jan S. Kirschke
View a PDF of the paper titled ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset, by Moritz Roman Hernandez Petzsche and 24 other authors
View PDFAbstract:Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in the duration of hospital stay to predict outcome by visualizing infarct core size and location. Furthermore, it may be used to characterize stroke etiology, e.g. differentiation between (cardio)-embolic and non-embolic stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. Previous iterations of the Ischemic Stroke Lesion Segmentation (ISLES) challenge have aided in the generation of identifying benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions. This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n=250 and a test dataset of n=150. All training data will be made publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge with the goal of finding algorithmic methods to enable the development and benchmarking of robust and accurate segmentation algorithms for ischemic stroke.
| Comments: | 12 pages, 2 figures |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2206.06694 [cs.CV] |
| (orarXiv:2206.06694v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2206.06694 arXiv-issued DOI via DataCite | |
| Journal reference: | Scientific data 9.1 (2022): 762 |
| Related DOI: | https://doi.org/10.1038/s41597-022-01875-5 DOI(s) linking to related resources |
Submission history
From: Ezequiel de la Rosa [view email][v1] Tue, 14 Jun 2022 08:54:40 UTC (2,002 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset, by Moritz Roman Hernandez Petzsche and 24 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.