ArticleBiomedical optics express2016
Automatic classification of atherosclerotic plaques imaged with intravascular OCT.
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- Automated lipid detection in spectroscopic optical coherence tomography using a weakly supervised deep learning network.Biomedical optics express · 2026Article
- Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Systems.Reviews in cardiovascular medicine · 2025Review
- Automated comprehensive evaluation of coronary artery plaque in IVOCT using deep learning.iScience · 2025Article
- Histology-Grounded Automated Plaque Subtype Segmentation in Intravascular Optical Coherence Tomography.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- Validation of biomechanical assessment of coronary plaque vulnerability based on intravascular optical coherence tomography and digital subtraction angiography.Quantitative imaging in medicine and surgery · 2024Article
- Automatic assessment of atherosclerotic plaque features by intracoronary imaging: a scoping review.Frontiers in cardiovascular medicine · 2024Article
- Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography.Biomedical optics express · 2023Article
- Automatic assessment of calcified plaque and nodule by optical coherence tomography adopting deep learning model.The international journal of cardiovascular imaging · 2022Article
- Artificial Intelligence in Cardiovascular Atherosclerosis Imaging.Journal of personalized medicine · 2022Review
- Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions.Journal of biophotonics · 2021Review
- Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture.Frontiers in cardiovascular medicine · 2021Article
- Artificial Intelligence in Intracoronary Imaging.Current cardiology reports · 2020Review
- Automatic A-line coronary plaque classification using combined deep learning and textural features in intravascular OCT images.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Atherosclerotic Plaque Tissue Characterization: An OCT-Based Machine Learning Algorithm WithFrontiers in bioengineering and biotechnology · 2020Article
- Segmentation of Coronary Calcified Plaque in Intravascular OCT Images Using a Two-Step Deep Learning Approach.IEEE access : practical innovations, open solutions · 2020Article
- Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images.Biomedical optics express · 2019Article
- AI-AssistedClinical cancer research : an official journal of the American Association for Cancer Research · 2019Article
- Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets.Journal of biomedical optics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Intravascular optical coherence tomography (IV-OCT) allows evaluation of atherosclerotic plaques; however, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present a novel computational method for automated IV-OCT plaque characterization. This method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The performance of our proposed method was evaluated with IV-OCT scans of cadaveric human coronary arteries and corresponding tissue histopathology. Our results suggest that this methodology allows automated identification of fibrotic and lipid-containing plaques. Moreover, this novel computational method has the potential to enable high throughput atherosclerotic plaque characterization.
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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.