ArticleScientific reports2021
DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in 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 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 1 citations in OpenAlex.
- Accuracy of Deep Learning in Detecting Cerebral Microbleeds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies.NeuroImage · 2023Pooled it
- A geometric-to-neural cascade for cerebral microbleed detection in susceptibility-weighted MRI.bioRxiv : the preprint server for biology · 2026Article
- Automated detection of cerebral microbleeds on ex-vivo MRI scans of community-based older adults.NeuroImage · 2026Article
- Association of Lacunes with Risk Factors, Cognition, and Atrophy: The Multi-Ethnic Study of Atherosclerosis (MESA).medRxiv : the preprint server for health sciences · 2026Article
- MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.Scientific reports · 2025Article
- Review
- Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.Journal of neuroscience methods · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- 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
- Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.CNS neuroscience & therapeutics · 2024Review
- Heart rate fragmentation and brain MRI markers of small vessel disease in MESA.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging.Frontiers in neuroscience · 2024Article
- Neuroimaging techniques, gene therapy, and gut microbiota: frontier advances and integrated applications in Alzheimer's Disease research.Frontiers in aging neuroscience · 2024Review
- Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.Neuroimage. Reports · 2023Article
- Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.Frontiers in neuroinformatics · 2023Article
- Left Atrial Function and Arrhythmias in Relation to Small Vessel Disease on Brain MRI: The Multi-Ethnic Study of Atherosclerosis.Journal of the American Heart Association · 2022Article
Corrections and comments
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Authors and funding
10 authors at 7 institutions in 2 countries.
Funding
Abstract
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.
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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.