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dc.contributor.authorWu, O.
dc.contributor.authorWinzeck, S.
dc.contributor.authorGiese, A.-K.
dc.date.accessioned2019-09-13T14:49:29Z
dc.date.available2019-09-13T14:49:29Z
dc.date.issued2019
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068808645&doi=10.1161%2fSTROKEAHA.119.025373&partnerID=40&md5=efa8585e17bc75443b9e4d908d1596a2
dc.identifier.urihttp://hdl.handle.net/10713/10561
dc.description.abstractBackground 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- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.en_US
dc.description.urihttps://doi.org/10.1161/STROKEAHA.119.025373en_US
dc.language.isoen-USen_US
dc.publisherAmerican Heart Associationen_US
dc.relation.ispartofStroke
dc.subjectdiffusion magnetic resonance imagingen_US
dc.subjectmachine learningen_US
dc.subjectphenotypeen_US
dc.subjectrisk factorsen_US
dc.subjectstrokeen_US
dc.titleBig Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1161/STROKEAHA.119.025373
dc.identifier.pmid31177973


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