ArticleComputational and mathematical methods in medicine2020
Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review.IEEE reviews in biomedical engineering · 2026Review
- Application of dual branch and bidirectional feedback feature extraction networks for real time accurate positioning of stents.Scientific reports · 2025Article
- Intracoronary Optical Coherence Tomography: Technological Innovations and Clinical Implications in Cardiology.Current treatment options in cardiovascular medicine · 2025Review
- Machine-learning detection and variability of mesospheric frontal waves observed by VIIRS day/night band.Earth, planets, and space : EPS · 2025Article
- Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN.Current oncology (Toronto, Ont.) · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An artificial stent implantation is one of the most effective ways to treat coronary artery diseases. It is vital in vascular medical imaging, such as intravascular optical coherence tomography (IVOCT), to be able to track the position of stents in blood vessels effectively. We trained two models, the "You Only Look Once" version 3 (YOLOv3) and the Region-based Fully Convolutional Network (R-FCN), to detect metal support struts in IVOCT, respectively. After rotating the original images in the training set for data augmentation, and modifying the scale of the conventional anchor box in both two algorithms to fit the size of the target strut, YOLOv3 and R-FCN achieved precision, recall, and AP all above 95% in 0.4 IoU threshold. And R-FCN performs better than YOLOv3 in all relevant indicators.
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Registered trials
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