ArticleNeuroImage. Clinical2018
A user-guided tool for semi-automated cerebral microbleed detection and volume segmentation: Evaluating vascular injury and data labelling for machine learning.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01764529 (Modifiers of Disease Severity and Progression in Cerebral Cavernous Malformations), which is not on this map. Cited by 26 papers, 1 of them a synthesis 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.
Modifiers of Disease Severity and Progression in Cerebral Cavernous Malformations
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature.BMC medicine · 2021Pooled it
- Trial
- Radiation therapy increases long-term gonadotropin deficiency, hypothalamic-pituitary dysfunction, and brainstem necrosis in pediatric patients with brain tumor.Radiation oncology journal · 2026Article
- From microbleeds to axonal damage: current evidence and future directions.Frontiers in radiology · 2026Review
- 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
- The Application of Augmented Reality Technology in Perioperative Visual Guidance: Technological Advances and Innovation Challenges.Sensors (Basel, Switzerland) · 2024Review
- A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language.Bioengineering (Basel, Switzerland) · 2024Article
- aXonica: A support package for MRI based Neuroimaging.Biotechnology notes (Amsterdam, Netherlands) · 2024Article
- Preliminary evidence of high prevalence of cerebral microbleeds in astronauts with spaceflight experience.Frontiers in physiology · 2024Article
- DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- 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
- Cerebral Microbleeds Assessment and Quantification in COVID-19 Patients With Neurological Manifestations.Frontiers in neurology · 2022Article
- Lobar Cerebral Microbleeds Are Associated With Cognitive Decline in Patients With Type 2 Diabetes Mellitus.Frontiers in neurology · 2022Article
- Review: Neurological Complications From Therapies for Pediatric Brain Tumors.Frontiers in oncology · 2022Review
- An Integrated Analysis of Clinical, Genomic, and Imaging Features Reveals Predictors of Neurocognitive Outcomes in a Longitudinal Cohort of Pediatric Cancer Survivors, Enriched with CNS Tumors (Rad ART Pro).Frontiers in oncology · 2022Article
- Using transfer learning for automated microbleed segmentation.Frontiers in neuroimaging · 2022Article
- No Evidence of a Difference in Susceptibility-Weighted Imaging Lesion Burden or Functional Network Connectivity between Children with Typical and Delayed Recovery Two Weeks Post-Concussion.Journal of neurotrauma · 2021Observational
- Automated detection of cerebral microbleeds on T2*-weighted MRI.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Background and purpose: With extensive research efforts in place to address the clinical relevance of cerebral microbleeds (CMBs), there remains a need for fast and accurate methods to detect and quantify CMB burden. Although some computer-aided detection algorithms have been proposed in the literature with high sensitivity, their specificity remains consistently poor. More sophisticated machine learning methods appear to be promising in their ability to minimize false positives (FP) through high-level feature extraction and the discrimination of hard-mimics. To achieve superior performance, these methods require sizable amounts of precisely labelled training data. Here we present a user-guided tool for semi-automated CMB detection and volume segmentation, offering high specificity for routine use and FP labelling capabilities to ease and expedite the process of generating labelled training data. Materials and methods: Existing computer-aided detection methods reported by our group were extended to include fully-automated segmentation and user-guided CMB classification with FP labelling. The algorithm's performance was evaluated on a test set of ten patients exhibiting radiotherapy-induced CMBs on MR images. Results: The initial algorithm's base sensitivity was maintained at 86.7%. FP's were reduced to inter-rater variations and segmentation results were in 98% agreement with ground truth labelling. There was an approximate 5-fold reduction in the time users spent evaluating CMB burden with the algorithm versus without computer aid. The Intra-class Correlation Coefficient for inter-rater agreement was 0.97 CI[0.92,0.99]. Conclusions: This development serves as a valuable tool for routine evaluation of CMB burden and data labelling to improve CMB classification with machine learning. The algorithm is available to the public on GitHub (https://github.com/LupoLab-UCSF/CMB_labeler).
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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.