ArticleBiomedical optics express2013
Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images.
Article in Biomedical optics express, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 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
- Optical coherence tomography for broad spectrum of thyroid carcinoma: a qualitative and quantitative analysis.Biomedical engineering letters · 2026Article
- Automated lipid detection in spectroscopic optical coherence tomography using a weakly supervised deep learning network.Biomedical optics express · 2026Article
- Automated Artificial Intelligence Mapping of Coronary Plaque Calcification: A Comparison with Manual Intravascular Image Analysis.Journal of clinical medicine · 2025Article
- 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
- Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence.Biomedical optics express · 2024Article
- Real-time closed-loop tissue-specific laser osteotomy using deep-learning-assisted optical coherence tomography.Biomedical optics express · 2023Article
- Sk-Conv and SPP-based UNet for lesion segmentation of coronary optical coherence tomography.Technology and health care : official journal of the European Society for Engineering and Medicine · 2023Article
- Automatic assessment of calcified plaque and nodule by optical coherence tomography adopting deep learning model.The international journal of cardiovascular imaging · 2022Article
- Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization.International journal of environmental research and public health · 2021Review
- Layer-based, depth-resolved computation of attenuation coefficients and backscattering fractions in tissue using optical coherence tomography.Biomedical optics express · 2021Article
- Method for parametric imaging of attenuation by intravascular optical coherence tomography.Biomedical optics express · 2021Article
- Application of machine learning in understanding atherosclerosis: Emerging insights.APL bioengineering · 2021Review
- Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.PloS one · 2021Article
- Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture.Frontiers in cardiovascular medicine · 2021Article
- Learning With Fewer Images via Image Clustering: Application to Intravascular OCT Image Segmentation.IEEE access : practical innovations, open solutions · 2021Article
- Article
- Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks.Journal of biomedical optics · 2020Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Intravascular optical coherence tomography (IVOCT) is rapidly becoming the method of choice for the in vivo investigation of coronary artery disease. While IVOCT visualizes atherosclerotic plaques with a resolution <20µm, image analysis in terms of tissue composition is currently performed by a time-consuming manual procedure based on the qualitative interpretation of image features. We illustrate an algorithm for the automated and systematic characterization of IVOCT atherosclerotic tissue. The proposed method consists in a supervised classification of image pixels according to textural features combined with the estimated value of the optical attenuation coefficient. IVOCT images of 64 plaques, from 49 in vivo IVOCT data sets, constituted the algorithm's training and testing data sets. Validation was obtained by comparing automated analysis results to the manual assessment of atherosclerotic plaques. An overall pixel-wise accuracy of 81.5% with a classification feasibility of 76.5% and per-class accuracy of 89.5%, 72.1% and 79.5% for fibrotic, calcified and lipid-rich tissue respectively, was found. Moreover, measured optical properties were in agreement with previous results reported in literature. As such, an algorithm for automated tissue characterization was developed and validated using in vivo human data, suggesting that it can be applied to clinical IVOCT data. This might be an important step towards the integration of IVOCT in cardiovascular research and routine clinical practice.
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