Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data

Wu, Ona; Winzeck, Stefan; Giese, Anne‐Katrin; L. Hancock, Brandon; R. Etherton, Mark; J.R.J. Bouts, Mark; Donahue, Kathleen; D. Schirmer, Markus; E. Irie, Robert; J. T. Mocking, Steven; C. McIntosh, Elissa; Bezerra, Raquel; Kamnitsas, Konstantinos; Frid, Petrea; Wassélius, Johan; W. Cole, John; Xu, Huichun; Holmegaard, Lukas; Jiménez-Conde, Jordi; Lemmens, Robin; Lorentzen, Eric; F. McArdle, Patrick; F. Meschia, James; Roquer, Jaume; Rundek, Tatjana; L. Sacco, Ralph; Schmidt, Reinhold; Sharma, Pankaj; Słowik, Agnieszka; M. Stanne, Tara; Thijs, Vincent; Vagal, Achala; Woo, Daniel; Bevan, Steve; J. Kittner, Steven; D. Mitchell, Braxton; Rosand, Jonathan; B. Worrall, Bradford; Jern, Christina; Lindgren, Arne; Maguire, Jane; S. Rost, Natalia; behalf of the MRI-GENIE and GISCOME Investigators, on

doi:10.1161/strokeaha.119.025373

Public

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data

Published in Stroke • Jun 10, 2019

Authors:

Ona Wu

Stefan Winzeck

Anne‐Katrin Giese

Abstract

Background and Purpose— We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods— Isch...

Subject

Medicine

Magnetic resonance imaging

Interquartile range

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