Big data approaches to phenotyping acute ischemic stroke using automated lesion segmentation of multi-center MRI data

BACKGROUND AND PURPOSE: We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted (DWI) datasets and explored the potential role of this tool for phenotyping acute ischemic stroke. METHODS: Ischemic stroke data sets from t...

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Publicado no:Stroke
Main Authors: Wu, Ona, Winzeck, Stefan, Giese, Anne-Katrin, Hancock, Brandon L., Etherton, Mark R., Bouts, Mark J. R. J., Donahue, Kathleen, Schirmer, Markus D., Irie, Robert E., Mocking, Steven J. T., McIntosh, Elissa C., Bezerra, Raquel, Kamnitsas, Konstantinos, Frid, Petrea, Wasselius, Johan, Cole, John W., Xu, Huichun, Holmegaard, Lukas, Jiménez-Conde, Jordi, Lemmens, Robin, Lorentzen, Eric, McArdle, Patrick F., Meschia, James F., Roquer, Jaume, Rundek, Tatjana, Sacco, Ralph L., Schmidt, Reinhold, Sharma, Pankaj, Slowik, Agnieszka, Stanne, Tara M., Thijs, Vincent, Vagal, Achala, Woo, Daniel, Bevan, Stephen, Kittner, Steven J., Mitchell, Braxton D., Rosand, Jonathan, Worrall, Bradford B., Jern, Christina, Lindgren, Arne G., Maguire, Jane, Rost, Natalia S.
Formato: Artigo
Idioma:Inglês
Publicado em: 2019
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Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC6728139/
https://ncbi.nlm.nih.gov/pubmed/31177973
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1161/STROKEAHA.119.025373
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