ArticleScientific reports2020
A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.
Article in Scientific reports, 2020. 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.
- Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT.European stroke journal · 2026Pooled it
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Topogram-based anatomical labelling of CT series: anatomy-aware CT data processing using deep learning.European radiology · 2026Article
- A Resource-Efficient Hybrid Deep Learning Framework with External Validation for Intracranial Hemorrhage Detection in CT Scans.Diagnostics (Basel, Switzerland) · 2026Article
- Development and validation of an automated MRI-based pipeline for temporal classification of intracerebral hemorrhage using U-Net segmentation and machine learning.Radiological physics and technology · 2026Article
- A multi-task deep learning pipeline for classification, detection, and weakly supervised 3D segmentation of intraparenchymal hematoma on brain CT.Scientific reports · 2026Article
- Development and validation of intracranial hypertension prediction models based on radiomic features in patients with traumatic brain injury: an exploratory study based on CENTER-TBI data.Critical care (London, England) · 2025Article
- Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography.Applied sciences (Basel, Switzerland) · 2025Article
- Automated Hematoma Detection and Outcome Prediction in Patients With Traumatic Brain Injury.CNS neuroscience & therapeutics · 2024Article
- Need for Transparency and Clinical Interpretability in Hemorrhagic Stroke Artificial Intelligence Research: Promoting Effective Clinical Application.Yonsei medical journal · 2024Article
- Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta-Analysis.Stroke (Hoboken, N.J.) · 2024Article
- Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages.Magnetic resonance imaging · 2023Article
- CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images.BMC medical informatics and decision making · 2023Article
- A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023Article
- Article
- Artificial Intelligence in Healthcare Competition (TEKNOFEST-2021): Stroke Data Set.The Eurasian journal of medicine · 2022Article
- Computational Approaches for Acute Traumatic Brain Injury Image Recognition.Frontiers in neurology · 2022Review
- A Review on Computer Aided Diagnosis of Acute Brain Stroke.Sensors (Basel, Switzerland) · 2021Review
- Current approaches and advances in the imaging of stroke.Disease models & mechanisms · 2021Review
- A deep-learning method using computed tomography scout images for estimating patient body weight.Scientific reports · 2021Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying convolutional neural networks with deep supervision (CNN-DS) for accurate hematoma segmentation and volume quantification in computed tomography (CT) scans. Non-contrast whole-head CT scans of 55 patients with hemorrhagic stroke were used. Individual scans were standardized to 64 axial slices of 128 × 128 voxels. Each voxel was annotated independently by experienced raters, generating a binary label of hematoma versus normal brain tissue based on majority voting. The dataset was split randomly into training (n = 45) and testing (n = 10) subsets. A CNN-DS model was built applying the training data and examined using the testing data. Performance of the CNN-DS solution was compared with three previously established methods. The CNN-DS achieved a Dice coefficient score of 0.84 ± 0.06 and recall of 0.83 ± 0.07, higher than patch-wise U-Net (< 0.76). CNN-DS average running time of 0.74 ± 0.07 s was faster than PItcHPERFeCT (> 1412 s) and slice-based U-Net (> 12 s). Comparable interrater agreement rates were observed between "method-human" vs. "human-human" (Cohen's kappa coefficients > 0.82). The fully automated CNN-DS approach demonstrated expert-level accuracy in fast segmentation and quantification of hematoma, substantially improving over previous methods. Further research is warranted to test the CNN-DS solution as a software tool in clinical settings for effective stroke management.
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