Hernandez Petzsche, Moritz R; de la Rosa, Ezequiel; Hanning, Uta; Wiest, Roland; Valenzuela, Waldo; Reyes, Mauricio; Meyer, Maria; Liew, Sook-Lei; Kofler, Florian; Ezhov, Ivan; Robben, David; Hutton, Alexandre; Friedrich, Tassilo; Zarth, Teresa; Bürkle, Johannes; Baran, The Anh; Menze, Björn; Broocks, Gabriel; Meyer, Lukas; Zimmer, Claus; ... (2022). ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. Scientific data, 9(1), p. 762. Nature Publishing Group 10.1038/s41597-022-01875-5
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Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify 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 ( https://doi.org/10.5281/zenodo.7153326 ). 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 is 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 ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research |
UniBE Contributor: |
Wiest, Roland Gerhard Rudi, Valenzuela, Waldo Enrique, Reyes, Mauricio |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
2052-4463 |
Publisher: |
Nature Publishing Group |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
12 Dec 2022 11:05 |
Last Modified: |
02 Mar 2023 23:37 |
Publisher DOI: |
10.1038/s41597-022-01875-5 |
PubMed ID: |
36496501 |
BORIS DOI: |
10.48350/175724 |
URI: |
https://boris.unibe.ch/id/eprint/175724 |
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