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

doi:10.1161/STROKEAHA.119.025373

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Community

Back to search

Article

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

Stroke - 1 Jul 2019

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

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Brain Ischemia

Share to Diffusion Magnetic Resonance Imaging

Share to Image Processing, Computer-Assisted

Share to Machine Learning

Share to Neural Networks, Computer

Share to Observer Variation

Share to Socioeconomic Factors

Share to Stroke

Source

Open in PubMed Open full text

Identifiers

PMID: 31177973
DOI: 10.1161/STROKEAHA.119.025373
PMCID: PMC6728139

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data.PMID 31177973Read ↗

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic strokeSemantic proximity Read ↗

White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts – The MRI-GENIE StudySemantic proximity Read ↗

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome PredictionSemantic proximity Read ↗

Scaling behaviors of deep learning and linear algorithms for the prediction of stroke severitySemantic proximity Read ↗

Clinically Scalable Deep Learning for Stroke Multicenter Validation of an Ultra-Efficient NCCT-Based Diagnostic FrameworkSemantic proximity Read ↗

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after strokeSemantic proximity Read ↗

Deep learning algorithms for automatic segmentation of acute cerebral infarcts on diffusion-weighted images: Effects of training data sample size, transfer learning, and data featuresSemantic proximity Read ↗

Machine Learning in Stroke Lesion Segmentation and Recovery Forecasting: A ReviewSemantic proximity Read ↗

Automated Deep Learning–Based Demyelination Load Segmentation in Metachromatic LeukodystrophySemantic proximity Read ↗

Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methodsSemantic proximity Read ↗

Automated lesion segmentation with BIANCA: impact of population-level features, classification algorithm and locally adaptive thresholdingSemantic proximity Read ↗

MAESTRO: A Public, Generalizable Model for Stroke Lesion Segmentation from T1 MRI Across the Recovery ContinuumSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.