ArticleJournal of digital imaging2019
Toward Automatic Detection of Radiation-Induced Cerebral Microbleeds Using a 3D Deep Residual Network.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Accuracy of Deep Learning in Detecting Cerebral Microbleeds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis.La Radiologia medica · 2022Pooled it
- Deep Learning-Based Detection, Classification, and Segmentation of Cerebral Microbleeds.The Eurasian journal of medicine · 2026Article
- Deep learning-based detection of cerebral microbleeds on 2D T2*-weighted GRE MRI: toward ARIA-H risk assessment in Alzheimer's treatment.Frontiers in aging neuroscience · 2026Article
- Review
- Automated Detection of Cerebral Microbleeds on Two-dimensional Gradient-recalled Echo T2* Weighted Images Using a Morphology Filter Bank and Convolutional Neural Network.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Automatic Detection and Classification of Cerebral Microbleeds Using 3D CNN.Journal of image and graphics · 2025Article
- SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.Scientific reports · 2024Article
- A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language.Bioengineering (Basel, Switzerland) · 2024Article
- Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds.Acta neurochirurgica · 2024Article
- Automatic detection of cerebral microbleeds using susceptibility weighted imaging and artificial intelligence.Quantitative imaging in medicine and surgery · 2024Article
- Diagnosis of cerebral microbleed via VGG and extreme learning machine trained by Gaussian map bat algorithm.Journal of ambient intelligence and humanized computing · 2023Article
- Article
- Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.Frontiers in neuroinformatics · 2023Article
- Comparison of quantitative susceptibility mapping methods on evaluating radiation-induced cerebral microbleeds and basal ganglia at 3T and 7T.NMR in biomedicine · 2022Article
- Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.Skeletal radiology · 2022Article
- Cerebral Microbleed Automatic Detection System Based on the "Deep Learning".Frontiers in medicine · 2022Article
- DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI.Scientific reports · 2021Article
- Automated detection of cerebral microbleeds on T2*-weighted MRI.Scientific reports · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Cerebral microbleeds, which are small focal hemorrhages in the brain that are prevalent in many diseases, are gaining increasing attention due to their potential as surrogate markers of disease burden, clinical outcomes, and delayed effects of therapy. Manual detection is laborious and automatic detection and labeling of these lesions is challenging using traditional algorithms. Inspired by recent successes of deep convolutional neural networks in computer vision, we developed a 3D deep residual network that can distinguish true microbleeds from false positive mimics of a previously developed technique based on traditional algorithms. A dataset of 73 patients with radiation-induced cerebral microbleeds scanned at 7 T with susceptibility-weighted imaging was used to train and evaluate our model. With the resulting network, we maintained 95% of the true microbleeds in 12 test patients and the average number of false positives was reduced by 89%, achieving a detection precision of 71.9%, higher than existing published methods. The likelihood score predicted by the network was also evaluated by comparing to a neuroradiologist's rating, and good correlation was observed.
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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.