ArticleEuropean radiology2021
Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Deep learning-based automated segmentation of intracerebral haemorrhage, intraventricular haemorrhage and perihaematomal oedema on non-contrast CT.European stroke journal · 2026Pooled it
- Systematic review of commercial artificial intelligence tools for the detection and volume quantification in intracerebral hemorrhage.European radiology · 2026Pooled it
- Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.Biomedical engineering online · 2024Pooled it
- Convolutional Neural Network in Microsurgery Treatment of Spontaneous Intracerebral Hemorrhage.Computational and mathematical methods in medicine · 2022Trial
- A Resource-Efficient Hybrid Deep Learning Framework with External Validation for Intracranial Hemorrhage Detection in CT Scans.Diagnostics (Basel, Switzerland) · 2026Article
- AI-assisted versus fully automated volumetry for hemorrhage quantification in spontaneous ICH with ventricular extension: A multi-center study.European journal of radiology open · 2026Article
- Comparing manual and automated methods for extracting perihematomal features in predicting hemorrhage expansion in deep intracerebral hemorrhage.Scientific reports · 2026Article
- Real-world evaluation of the accuracy of the Viz.AI automated intracranial hemorrhage volume calculation tool.Journal of neurointerventional surgery · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- Cross-institutional automated multilabel segmentation for acute intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT.Radiology advances · 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
- PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features.Physical and engineering sciences in medicine · 2024Article
- Article
- Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.Cancer medicine · 2024Article
- Mesenchymal Stem Cell Therapy in Acute Intracerebral Hemorrhage: A Dose-Escalation Safety and Tolerability Trial.Neurocritical care · 2024Article
- Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network.Scientific reports · 2024Article
- CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.European radiology · 2024Article
- Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography? A Systematic Review and Meta-Analysis.Stroke (Hoboken, N.J.) · 2024Article
- Review
- Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesTo evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT.
methodsThree hundred and eighty primary ICH patients who underwent CT at hospital arrival were divided into a training cohort (n = 300) and a validation cohort (n = 80). An independent cohort with 80 patients was used for testing. Ground truth (segmentation masks) was manually generated by radiologists. Model performance on lesion segmentation and volumetric measurement of ICH, IVH, and PHE were evaluated by comparing the model results with the segmentations performed by radiologists.
resultsIn the test cohort, the Dice scores of lesion segmentation were 0.92, 0.79, and 0.71 for ICH, IVH, and PHE, respectively. The sensitivities were 0.93 for ICH, 0.88 for IVH, and 0.81 for PHE. The positive predictive values were 0.92, 0.76, and 0.69 for ICH, IVH, and PHE, respectively. Excellent concordance (concordance correlation coefficients [CCCs] ≥ 0.98) of ICH and IVH and good concordance of PHE (CCCs ≥ 0.92) were demonstrated between manually and automatically measured volumes. The model took approximately 15 s to provide automatic segmentation and volume analysis for each patient.
conclusionOur model demonstrates good reliability for automatic segmentation and volume measurement of ICH, IVH, and PHE in primary ICH, which can be useful to reduce the effort and time of doctors to calculate volumes of ICH, IVH, and PHE. KEY POINTS: • Deep learning algorithms can provide automatic and reliable assessment of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT. • Non-contrast CT-based deep learning method can be helpful to provide efficient and accurate measurements of ICH, IVH, and PHE in primary ICH patients, thereby reducing the effort and time of doctors to segment and calculate volumes of ICH, IVH, and PHE in primary ICH patients.
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