Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
PublisherAmerican Heart Association
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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.
Identifier to cite or link to this itemhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068808645&doi=10.1161%2fSTROKEAHA.119.025373&partnerID=40&md5=efa8585e17bc75443b9e4d908d1596a2; http://hdl.handle.net/10713/10561
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- Issue date: 2019 Jun
- Machine learning identifies stroke features between species.
- Authors: Castaneda-Vega S, Katiyar P, Russo F, Patzwaldt K, Schnabel L, Mathes S, Hempel JM, Kohlhofer U, Gonzalez-Menendez I, Quintanilla-Martinez L, Ziemann U, la Fougere C, Ernemann U, Pichler BJ, Disselhorst JA, Poli S
- Issue date: 2021
- Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms.
- Authors: Woo I, Lee A, Jung SC, Lee H, Kim N, Cho SJ, Kim D, Lee J, Sunwoo L, Kang DW
- Issue date: 2019 Aug
- White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype.
- Authors: Giese AK, Schirmer MD, Dalca AV, Sridharan R, Donahue KL, Nardin M, Irie R, McIntosh EC, Mocking SJT, Xu H, Cole JW, Giralt-Steinhauer E, Jimenez-Conde J, Jern C, Kleindorfer DO, Lemmens R, Wasselius J, Lindgren A, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Worrall BB, Woo D, Kittner SJ, McArdle PF, Mitchell BD, Rosand J, Meschia JF, Wu O, Golland P, Rost NS, International Stroke Genetics Consortium and the MRI-GENIE Investigators.
- Issue date: 2020 Jul 7
- White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study.
- Authors: Schirmer MD, Dalca AV, Sridharan R, Giese AK, Donahue KL, Nardin MJ, Mocking SJT, McIntosh EC, Frid P, Wasselius J, Cole JW, Holmegaard L, Jern C, Jimenez-Conde J, Lemmens R, Lindgren AG, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Woo D, Vagal A, Xu H, Kittner SJ, McArdle PF, Mitchell BD, Rosand J, Worrall BB, Wu O, Golland P, Rost NS, MRI-GENIE Investigators.
- Issue date: 2019