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SHR Neuro Krebs Kardio Lipid

Wu, O; Winzeck, S; Giese, AK; Hancock, BL; Etherton, MR; Bouts, MJRJ; Donahue, K; Schirmer, MD; Irie, RE; Mocking, SJT; McIntosh, EC; Bezerra, R; Kamnitsas, K; Frid, P; Wasselius, J; Cole, JW; Xu, H; Holmegaard, L; Jiménez-Conde, J; Lemmens, R; Lorentzen, E; McArdle, PF; Meschia, JF; Roquer, J; Rundek, T; Sacco, RL; Schmidt, R; Sharma, P; Slowik, A; Stanne, TM; Thijs, V; Vagal, A; Woo, D; Bevan, S; Kittner, SJ; Mitchell, BD; Rosand, J; Worrall, BB; Jern, C; Lindgren, AG; Maguire, J; Rost, NS.
Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.
Stroke. 2019; 50(7): 1734-1741. [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Schmidt Reinhold
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Number of Figures: 5
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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- 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.

Find related publications in this database (Keywords)
diffusion magnetic resonance imaging
machine learning
phenotype
risk factors
stroke
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