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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, Vol. 9, No. 1, 762, 10.12.2022.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Hernandez Petzsche, MR, de la Rosa, E, Hanning, U, Wiest, R, Valenzuela, W, Reyes, M, Meyer, M, Liew, S-L, Kofler, F, Ezhov, I, Robben, D, Hutton, A, Friedrich, T, Zarth, T, Bürkle, J, Baran, TA, Menze, B, Broocks, G, Meyer, L, Zimmer, C, Boeckh-Behrens, T, Berndt, M, Ikenberg, B, Wiestler, B & Kirschke, JS 2022, 'ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset', SCI DATA, vol. 9, no. 1, 762. https://doi.org/10.1038/s41597-022-01875-5

APA

Hernandez Petzsche, M. R., de la Rosa, E., Hanning, U., Wiest, R., Valenzuela, W., Reyes, M., Meyer, M., Liew, S-L., Kofler, F., Ezhov, I., Robben, D., Hutton, A., Friedrich, T., Zarth, T., Bürkle, J., Baran, T. A., Menze, B., Broocks, G., Meyer, L., ... Kirschke, J. S. (2022). ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. SCI DATA, 9(1), [762]. https://doi.org/10.1038/s41597-022-01875-5

Vancouver

Hernandez Petzsche MR, de la Rosa E, Hanning U, Wiest R, Valenzuela W, Reyes M et al. ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset. SCI DATA. 2022 Dec 10;9(1). 762. https://doi.org/10.1038/s41597-022-01875-5

Bibtex

@article{b3cfc90cd32f4de1bb2682daa199ef40,
title = "ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset",
abstract = "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.",
keywords = "Humans, Ischemic Stroke, Stroke/diagnostic imaging, Magnetic Resonance Imaging/methods, Image Processing, Computer-Assisted/methods, Benchmarking",
author = "{Hernandez Petzsche}, {Moritz R} and {de la Rosa}, Ezequiel and Uta Hanning and Roland Wiest and Waldo Valenzuela and Mauricio Reyes and Maria Meyer and Sook-Lei Liew and Florian Kofler and Ivan Ezhov and David Robben and Alexandre Hutton and Tassilo Friedrich and Teresa Zarth and Johannes B{\"u}rkle and Baran, {The Anh} and Bj{\"o}rn Menze and Gabriel Broocks and Lukas Meyer and Claus Zimmer and Tobias Boeckh-Behrens and Maria Berndt and Benno Ikenberg and Benedikt Wiestler and Kirschke, {Jan S}",
note = "{\textcopyright} 2022. The Author(s).",
year = "2022",
month = dec,
day = "10",
doi = "10.1038/s41597-022-01875-5",
language = "English",
volume = "9",
journal = "SCI DATA",
issn = "2052-4463",
publisher = "NATURE PUBLISHING GROUP",
number = "1",

}

RIS

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 -