Trial reportPloS one2019
Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: Application to hemodynamics modeling.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- Robust lumen segmentation based on temporal residual U-Net using spatiotemporal features in intravascular optical coherence tomography images.Journal of biomedical optics · 2025Article
- 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
- 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
- Artificial Intelligence for the Interventional Cardiologist: Powering and Enabling OCT Image Interpretation.Interventional cardiology (London, England) · 2024Review
- Rapid automated lumen segmentation of coronary optical coherence tomography images followed by 3D reconstruction of coronary arteries.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Wavelet-based U-shape network for bioabsorbable vascular stents segmentation in IVOCT images.Frontiers in physiology · 2024Article
- Three dimensional reconstruction of coronary artery stents from optical coherence tomography: experimental validation and clinical feasibility.Scientific reports · 2021Article
- 3D reconstruction of coronary artery bifurcations from coronary angiography and optical coherence tomography: feasibility, validation, and reproducibility.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
BACKGROUND /
objectivesAutomatic algorithms for stent struts segmentation in optical coherence tomography (OCT) images of coronary arteries have been developed over the years, particularly with application on metallic stents. The aim of this study is three-fold: (1) to develop and to validate a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images, (2) to develop a method for automatic OCT pullback quality assessment, and (3) to demonstrate the applicability of the segmentation algorithm for the creation of patient-specific stented coronary artery for local hemodynamics analysis.
methodsThe proposed OCT segmentation algorithm comprises four steps: (1) image pre-processing, (2) lumen segmentation, (3) stent struts segmentation, (4) strut-based lumen correction. This segmentation process is then followed by an automatic OCT pullback image quality assessment. This method classifies the OCT pullback image quality as 'good' or 'poor' based on the number of regions detected by the stent segmentation. The segmentation algorithm was validated against manual segmentation of 1150 images obtained from 23 in vivo OCT pullbacks.
resultsWhen considering the entire set of OCT pullbacks, lumen segmentation showed results comparable with manual segmentation and with previous studies (sensitivity ~97%, specificity ~99%), while stent segmentation showed poorer results compared to manual segmentation (sensitivity ~63%, precision ~83%). The OCT pullback quality assessment algorithm classified 7 pullbacks as 'poor' quality cases. When considering only the 'good' classified cases, the performance indexes of the scaffold segmentation were higher (sensitivity >76%, precision >86%).
conclusionsThis study proposes a segmentation algorithm for the detection of lumen contours and stent struts in low quality OCT images of patients treated with polymeric bioresorbable scaffolds. The segmentation results were successfully used for the reconstruction of one coronary artery model that included a bioresorbable scaffold geometry for computational fluid dynamics analysis.
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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 Socratic graph.