ArticleScientific reports2021
Automated detection of cerebral microbleeds on T2*-weighted MRI.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 40 citations in OpenAlex.
- Automated detection of cerebral microbleeds on ex-vivo MRI scans of community-based older adults.NeuroImage · 2026Article
- Vascular Microbleeds Without Brain Atrophy: A Microvascular Signature of Mid-Stage 5xFAD Pathology.bioRxiv : the preprint server for biology · 2025Article
- Common and distinct circulating microRNAs in four neurovascular disorders.Biochemistry and biophysics reports · 2025Article
- 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
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- Phase contrast-derived cerebral blood flow is associated with neurodegeneration and cerebrovascular injury in older adults.Frontiers in neuroscience · 2025Article
- A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language.Bioengineering (Basel, Switzerland) · 2024Article
- Automatic detection of cerebral microbleeds using susceptibility weighted imaging and artificial intelligence.Quantitative imaging in medicine and surgery · 2024Article
- Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease.Brain sciences · 2023Article
- Sex-Specific Patterns of Cerebral Atrophy and Enlarged Perivascular Spaces in Patients with Cerebral Amyloid Angiopathy and Dementia.AJNR. American journal of neuroradiology · 2023Article
- The role of magnetic resonance imaging in the diagnosis and prognosis of dementia.Biomolecules & biomedicine · 2023Review
- Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.Frontiers in neuroinformatics · 2023Article
- Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology.Diagnostics (Basel, Switzerland) · 2022Article
- Cerebral Microbleed Automatic Detection System Based on the "Deep Learning".Frontiers in medicine · 2022Article
- A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging.Frontiers in neuroscience · 2022Review
- Cerebral Microbleed DetectionFrontiers in computational neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Cerebral microbleeds, observed as small, spherical hypointense regions on gradient echo (GRE) or susceptibility weighted (SWI) magnetic resonance imaging (MRI) sequences, reflect small hemorrhagic infarcts, and are associated with conditions such as vascular dementia, small vessel disease, cerebral amyloid angiopathy, and Alzheimer's disease. The current gold standard for detecting and rating cerebral microbleeds in a research context is visual inspection by trained raters, a process that is both time consuming and subject to poor reliability. We present here a novel method to automate microbleed detection on GRE and SWI images. We demonstrate in a community-based cohort of older adults that the method is highly sensitive (greater than 92% of all microbleeds accurately detected) across both modalities, with reasonable precision (fewer than 20 and 10 false positives per scan on GRE and SWI, respectively). We also demonstrate that the algorithm can be used to identify microbleeds over longitudinal scans with a higher level of sensitivity than visual ratings (50% of longitudinal microbleeds correctly labeled by the algorithm, while manual ratings was 30% or lower). Further, the algorithm identifies the anatomical localization of microbleeds based on brain atlases, and greatly reduces time spent completing visual ratings (43% reduction in visual rating time). Our automatic microbleed detection instrument is ideal for implementation in large-scale studies that include cross-sectional and longitudinal scanning, as well as being capable of performing well across multiple commonly used MRI modalities.
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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.