ArticleAJNR. American journal of neuroradiology2019
Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI.
Article in AJNR. American journal of neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- The relevance of rich club regions for functional outcome post-stroke is enhanced in women.Human brain mapping · 2023Trial
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.Scientific reports · 2025Article
- DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Prediction of stroke severity: systematic evaluation of lesion representations.Annals of clinical and translational neurology · 2024Article
- Taxonomy of Acute Stroke: Imaging, Processing, and Treatment.Diagnostics (Basel, Switzerland) · 2024Review
- Development of statistical auto-segmentation method for diffusion restriction gray matter lesions in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease.Scientific reports · 2024Article
- Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity.Brain communications · 2024Article
- The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.European radiology · 2023Article
- Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach.Scientific reports · 2022Article
- Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- Face mask recognition system using CNN model.Neuroscience informatics · 2022Review
- Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging.Scientific reports · 2022Article
- A Review on Computer Aided Diagnosis of Acute Brain Stroke.Sensors (Basel, Switzerland) · 2021Review
- Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images.Medical image analysis · 2021Article
- Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.Medical image analysis · 2021Article
- Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study.Scientific reports · 2021Article
- Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children.Contrast media & molecular imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
background and purposeAccurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps. MATERIALS AND
methodsConvolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by the Dice score, sensitivity, and precision) were compared with one another and with ensembles of 5 networks. To assess the generalizability of the approach, we applied the best-performing model to an independent Evaluation Cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesion volumes was calculated across multiple thresholds (21, 31, 51, and 70 cm
resultsAn ensemble of convolutional neural networks trained on DWI, ADC, and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (
conclusionsAcute infarcts are more accurately segmented using ensembles of convolutional neural networks trained with multiparametric maps than by using a single model trained with a solo map. Automated lesion segmentation has high agreement with manual techniques for identifying patients with large lesion volumes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.