ArticleNeuroImage. Clinical2017
Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin.
Article in NeuroImage. Clinical, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 128 citations in OpenAlex.
- The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.Frontiers in neurology · 2024Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Association of Lacunes with Risk Factors, Cognition, and Atrophy: The Multi-Ethnic Study of Atherosclerosis (MESA).medRxiv : the preprint server for health sciences · 2026Article
- Occupational dust exposure and cerebral small vessel disease: a public health perspective on prevention and early detection.Frontiers in medicine · 2026Review
- Deep learning-based automated detection of fetal corpus callosum abnormalities in prenatal ultrasound.Frontiers in pediatrics · 2026Article
- MultiModal craniocerebral diagnose based on 3D CT and image reports.Scientific reports · 2025Article
- Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts.Journal of medical Internet research · 2025Article
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications.Current neuropharmacology · 2025Review
- Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI.Frontiers in neurology · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Computational imaging for rapid detection of grade-I cerebral small vessel disease (cSVD).Heliyon · 2024Article
- Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.CNS neuroscience & therapeutics · 2024Review
- Quantitative Analysis of Multimodal MRI Markers and Clinical Risk Factors for Cerebral Small Vessel Disease Based on Deep Learning.International journal of general medicine · 2024Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.Frontiers in neuroscience · 2023Article
- Segmentation of incident lacunes during the course of ischemic cerebral small vessel diseases.Frontiers in neurology · 2023Article
- A Survey of Dental Caries Segmentation and Detection Techniques.TheScientificWorldJournal · 2022Review
- Region Convolutional Neural Network for Brain Tumor Segmentation.Computational intelligence and neuroscience · 2022Article
- Conservative Treatment and Rehabilitation Training for Rectus Femoris Tear in Basketball Training Based on Computer Vision.Applied bionics and biomechanics · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of stroke, gait impairment, and dementia and are a primary imaging feature of the small vessel disease. Quantification of lacunes may be of great importance to elucidate the mechanisms behind neuro-degenerative disorders and is recommended as part of study standards for small vessel disease research. However, due to the different appearance of lacunes in various brain regions and the existence of other similar-looking structures, such as perivascular spaces, manual annotation is a difficult, elaborative and subjective task, which can potentially be greatly improved by reliable and consistent computer-aided detection (CAD) routines. In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN). We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system.
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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.