ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
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ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. / 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; Boeckh-Behrens, Tobias; Berndt, Maria; Ikenberg, Benno; Wiestler, Benedikt; Kirschke, Jan S.
in: SCI DATA, Jahrgang 9, Nr. 1, 762, 10.12.2022.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
AU - Hernandez Petzsche, Moritz R
AU - de la Rosa, Ezequiel
AU - Hanning, Uta
AU - Wiest, Roland
AU - Valenzuela, Waldo
AU - Reyes, Mauricio
AU - Meyer, Maria
AU - Liew, Sook-Lei
AU - Kofler, Florian
AU - Ezhov, Ivan
AU - Robben, David
AU - Hutton, Alexandre
AU - Friedrich, Tassilo
AU - Zarth, Teresa
AU - Bürkle, Johannes
AU - Baran, The Anh
AU - Menze, Björn
AU - Broocks, Gabriel
AU - Meyer, Lukas
AU - Zimmer, Claus
AU - Boeckh-Behrens, Tobias
AU - Berndt, Maria
AU - Ikenberg, Benno
AU - Wiestler, Benedikt
AU - Kirschke, Jan S
N1 - © 2022. The Author(s).
PY - 2022/12/10
Y1 - 2022/12/10
N2 - 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.
AB - 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.
KW - Humans
KW - Ischemic Stroke
KW - Stroke/diagnostic imaging
KW - Magnetic Resonance Imaging/methods
KW - Image Processing, Computer-Assisted/methods
KW - Benchmarking
U2 - 10.1038/s41597-022-01875-5
DO - 10.1038/s41597-022-01875-5
M3 - SCORING: Journal article
C2 - 36496501
VL - 9
JO - SCI DATA
JF - SCI DATA
SN - 2052-4463
IS - 1
M1 - 762
ER -