ArticleBiomedical optics express2020
Automatic stent reconstruction in optical coherence tomography based on a deep convolutional model.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Diagnostic accuracy of optical flow ratio: an individual patient-data meta-analysis.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2023Pooled it
- 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
- Intravascular imaging for acute coronary syndrome.NPJ cardiovascular health · 2025Review
- Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review.European heart journal. Digital health · 2025Review
- Enhancing percutaneous coronary intervention using TriVOCTNet: a multi-task deep learning model for comprehensive intravascular optical coherence tomography analysis.Physical and engineering sciences in medicine · 2025Article
- Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Intracoronary Optical Coherence Tomography: Technological Innovations and Clinical Implications in Cardiology.Current treatment options in cardiovascular medicine · 2025Review
- Advancements and applications of artificial intelligence in cardiovascular imaging: a comprehensive review.European heart journal. Imaging methods and practice · 2024Review
- Review
- Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses.Biomedical optics express · 2024Article
- Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms.JACC. Asia · 2023Review
- Angiography-based coronary flow reserve: The feasibility of automatic computation by artificial intelligence.Cardiology journal · 2023Article
- Optical flow ratio for assessing stenting result and physiological significance of residual disease.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2021Article
- Stent detection with very thick tissue coverage in intravascular OCT.Biomedical optics express · 2021Article
- Hemodynamic alternations following stent deployment and post-dilation in a heavily calcified coronary artery: In silico and ex-vivo approaches.Computers in biology and medicine · 2021Article
- Identification of the type of stent with three-dimensional optical coherence tomography: the SPQR study.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2021Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intravascular optical coherence tomography (IVOCT) can accurately assess stent apposition and expansion, thus enabling the optimisation of a stenting procedure to minimize the risk of device failure. This paper presents a deep convolutional based model for automatic detection and segmentation of stent struts. The input of pseudo-3D images aggregated the information from adjacent frames to refine the probability of strut detection. In addition, multi-scale shortcut connections were implemented to minimize the loss of spatial resolution and refine the segmentation of strut contours. After training, the model was independently tested in 21,363 cross-sectional images from 170 IVOCT image pullbacks. The proposed model obtained excellent segmentation (0.907 Dice and 0.838 Jaccard) and detection metrics (0.943 precision, 0.940 recall and 0.936 F1-score), significantly better than conventional features-based algorithms. This performance was robust and homogenous among IVOCT pullbacks with different sources of acquisition (clinical centres, imaging operators, type of stent, time of acquisition and challenging scenarios). In addition, excellent agreement between the model and a commercialized software was observed in the quantification of clinically relevant parameters. In conclusion, the deep-convolutional model can accurately detect stent struts in IVOCT images, thus enabling the fully-automatic quantification of stent parameters in an extremely short time. It might facilitate the application of quantitative IVOCT analysis in real-world clinical scenarios.
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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.