ArticleFrontiers in neuroscience2020
Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT.
Article in Frontiers in neuroscience, 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.
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?Medicine · 2022Pooled it
- Integrated CT pipeline for automatic intracranial hemorrhage evaluation with GPT-enhanced clinical decision support.European radiology · 2026Article
- Advanced multi-label brain hemorrhage segmentation using an attention-based residual U-Net model.BMC medical informatics and decision making · 2025Article
- Semiautomated Hemorrhage Volume Quantification in Aneurysmal Subarachnoid Hemorrhage.Neurocritical care · 2025Article
- Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.Scientific reports · 2025Article
- A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence.Bioengineering (Basel, Switzerland) · 2024Article
- DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation.IET systems biology · 2024Article
- Automated Hematoma Detection and Outcome Prediction in Patients With Traumatic Brain Injury.CNS neuroscience & therapeutics · 2024Article
- Detection of Subarachnoid Hemorrhage Using CNN with Dynamic Factor and Wandering Strategy-Based Feature Selection.Diagnostics (Basel, Switzerland) · 2024Article
- Spatial attention-based CSR-Unet framework for subdural and epidural hemorrhage segmentation and classification using CT images.BMC medical imaging · 2024Article
- Continuum topological derivative - A novel application tool for segmentation of CT and MRI images.Neuroimage. Reports · 2024Article
- Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta-Analysis.Stroke (Hoboken, N.J.) · 2024Article
- Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.Frontiers in neuroscience · 2024Article
- Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke.Frontiers in neuroinformatics · 2024Article
- Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features.Insights into imaging · 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
- Accuracy of automated intracerebral hemorrhage volume measurement on non-contrast computed tomography: a Swedish Stroke Register cohort study.Neuroradiology · 2023Article
- Building nonenhanced CT based radiomics model in discriminating arteriovenous malformation related hematomas from hypertensive intracerebral hematomas.Frontiers in neuroscience · 2023Article
- Unified ICH quantification and prognosis prediction in NCCT images using a multi-task interpretable network.Frontiers in neuroscience · 2023Article
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11 authors.
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Abstract
backgroundThe ABC/2 method is usually applied to evaluate intracerebral hemorrhage (ICH) volume on computed tomography (CT), although it might be inaccurate and not applicable in estimating extradural or subdural hemorrhage (EDH, SDH) volume due to their irregular hematoma shapes. This study aimed to evaluate deep framework optimized for the segmentation and quantification of ICH, EDH, and SDH.
methodsThe training datasets were 3,000 images retrospectively collected from a collaborating hospital (Hospital A) and segmented by the Dense U-Net framework. Three experienced radiologists determined the ground truth by marking the pixels as hemorrhage area. We utilized the Dice and intra-class correlation coefficients (ICC) to test the reliability of the ground truth. Moreover, the testing datasets consisted of 211 images (internal test) from Hospital A, and 86 ICH images (external test) from another hospital (Hospital B). In this study, we chose scatter plots, ICC, and Pearson correlation coefficients (PCC) with ground truth to evaluate the performance of the deep framework. Furthermore, to validate the effectiveness of the deep framework, we did a comparative analysis of the hemorrhage volume estimation between the deep model and the ABC/2 method.
resultsThe high Dice (0.89-0.95) and ICC (0.985-0.997) showed the consistency of the manual segmentations among the radiologists and the reliability of the ground truth. For the internal test, the Dice coefficients of ICH, EDH, and SDH were 0.90 ± 0.06, 0.88 ± 0.12, and 0.82 ± 0.16, respectively. For the external test, the segmentation Dice was 0.86 ± 0.09. Comparatively, the ICC and PCC of ICH volume estimations were 0.99 performed by Dense U-Net that overmatched the ABC/2 method.
conclusionThis study revealed the excellent performance of hematoma segmentation and volume evaluation based on Dense U-Net, which indicated our deep framework might contribute to efficiently developing treatment strategies for intracranial hemorrhage in clinics.
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