ArticleKorean journal of radiology2019
Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms.
Article in Korean journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.Biomedical engineering online · 2026Pooled it
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- Evaluating Deep Learning-Based Commercial Software for Detecting Ischemic Lesions on DWI in Stroke Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts.Journal of imaging informatics in medicine · 2025Article
- Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.Scientific reports · 2025Article
- Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.Scientific reports · 2025Article
- Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.Journal of imaging informatics in medicine · 2025Article
- Evaluation of Siemens Healthineers' StrokeSegApp for automated diffusion and perfusion lesion segmentation in patients with ischemic stroke.Frontiers in neurology · 2025Article
- Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets.Journal of imaging informatics in medicine · 2024Article
- Development of statistical auto-segmentation method for diffusion restriction gray matter lesions in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease.Scientific reports · 2024Article
- Artificial intelligence in ischemic stroke images: current applications and future directions.Frontiers in neurology · 2024Review
- Proposed Protocols for Artificial Intelligence Imaging Database in Acute Stroke Imaging.Neurointervention · 2023Article
- The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.European radiology · 2023Article
- Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds.Frontiers in neurology · 2023Article
- Predicting DWI-FLAIR mismatch on NCCT: the role of artificial intelligence in hyperacute decision making.Frontiers in neurology · 2023Article
- Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach.Scientific reports · 2022Article
- Identifying stroke diagnosis-related features from medical imaging reports to improve clinical decision-making support.BMC medical informatics and decision making · 2022Article
- 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
- Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.European radiology · 2021Article
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10 authors.
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Abstract
objectiveTo develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. MATERIALS AND
methodsBetween September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols.
resultsThe CNN algorithms were significantly superior to conventional algorithms (
conclusionThe CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.
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