ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| author | Petzsche, Moritz Roman Hernandez de la Rosa, Ezequiel Hanning, Uta Wiest, Roland Pinilla, Waldo Enrique Valenzuela Reyes, Mauricio Meyer, Maria Ines Liew, Sook-Lei Kofler, Florian Ezhov, Ivan Robben, David Hutton, Alexander Friedrich, Tassilo Zarth, Teresa Bürkle, Johannes Baran, The Anh Menze, Bjoern Broocks, Gabriel Meyer, Lukas Zimmer, Claus Boeckh-Behrens, Tobias Berndt, Maria Ikenberg, Benno Wiestler, Benedikt Kirschke, Jan S. |
| author_facet | Petzsche, Moritz Roman Hernandez de la Rosa, Ezequiel Hanning, Uta Wiest, Roland Pinilla, Waldo Enrique Valenzuela Reyes, Mauricio Meyer, Maria Ines Liew, Sook-Lei Kofler, Florian Ezhov, Ivan Robben, David Hutton, Alexander Friedrich, Tassilo Zarth, Teresa Bürkle, Johannes Baran, The Anh Menze, Bjoern Broocks, Gabriel Meyer, Lukas Zimmer, Claus Boeckh-Behrens, Tobias Berndt, Maria Ikenberg, Benno Wiestler, Benedikt Kirschke, Jan S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_06694 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset Petzsche, Moritz Roman Hernandez de la Rosa, Ezequiel Hanning, Uta Wiest, Roland Pinilla, Waldo Enrique Valenzuela Reyes, Mauricio Meyer, Maria Ines Liew, Sook-Lei Kofler, Florian Ezhov, Ivan Robben, David Hutton, Alexander Friedrich, Tassilo Zarth, Teresa Bürkle, Johannes Baran, The Anh Menze, Bjoern Broocks, Gabriel Meyer, Lukas Zimmer, Claus Boeckh-Behrens, Tobias Berndt, Maria Ikenberg, Benno Wiestler, Benedikt Kirschke, Jan S. Computer Vision and Pattern Recognition 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. |
| title | ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2206.06694 |