ArticleRadiology. Artificial intelligence2020
Improved Segmentation and Detection Sensitivity of Diffusion-weighted Stroke Lesions with Synthetically Enhanced Deep Learning.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Exploiting Cross-modal Collaboration and Discrepancy for Semi-supervised Ischemic Stroke Lesion Segmentation from Multi-sequence MRI Images.Journal of imaging informatics in medicine · 2026Article
- Automated quantification of the Alberta Stroke Programme Early CT Score (ASPECTS) and infarct core on diffusion-weighted imaging in acute ischaemic stroke: multi-centre validation and severity stratification.Brain communications · 2026Article
- Evaluating Deep Learning-Based Commercial Software for Detecting Ischemic Lesions on DWI in Stroke Patients.Diagnostics (Basel, Switzerland) · 2025Article
- DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.Nature communications · 2025Article
- Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images.Scientific reports · 2025Article
- Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis.Insights into imaging · 2024Review
- Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls.Radiology. Artificial intelligence · 2023Article
- Prognostic value of diffusion-weighted imaging in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease.European radiology · 2022Article
- Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging.Scientific reports · 2022Article
- Optimizing Deep Learning Algorithms for Segmentation of Acute Infarcts on Non-Contrast Material-enhanced CT Scans of the Brain Using Simulated Lesions.Radiology. Artificial intelligence · 2021Article
- MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.BMC bioinformatics · 2021Article
- Editorial: Machine Learning in Neuroimaging.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare the segmentation and detection performance of a deep learning model trained on a database of human-labeled clinical stroke lesions on diffusion-weighted (DW) images to a model trained on the same database enhanced with synthetic stroke lesions. MATERIALS AND
methodsIn this institutional review board-approved study, a stroke database of 962 cases (mean patient age ± standard deviation, 65 years ± 17; 255 male patients; 449 scans with DW positive stroke lesions) and a normal database of 2027 patients (mean age, 38 years ± 24; 1088 female patients) were used. Brain volumes with synthetic stroke lesions on DW images were produced by warping the relative signal increase of real strokes to normal brain volumes. A generic three-dimensional (3D) U-Net was trained on four different databases to generate four different models:
resultsThe performance of the 3D U-Net model trained on the CS40DB (mean Dice score, 0.72) was better than models trained on the CS2DB (Dice score, 0.70;
conclusionDeep learning training for segmentation and detection of stroke lesions on DW images was significantly improved by enhancing the training set with synthetic lesions.
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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.