ArticleIEEE access : practical innovations, open solutions2020
Segmentation of Coronary Calcified Plaque in Intravascular OCT Images Using a Two-Step Deep Learning Approach.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 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
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Artificial Intelligence for Computer-Aided Detection in Endovascular Interventions: Clinical Applications, Validation, and Translational Perspectives.Bioengineering (Basel, Switzerland) · 2026Review
- Computational Analysis of Intravascular OCT Images for Future Clinical Support: A Comprehensive Review.IEEE reviews in biomedical engineering · 2026Review
- Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA.Diagnostics (Basel, Switzerland) · 2025Review
- Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis.European heart journal. Digital health · 2025Review
- 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
- Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy-intravascular ultrasound and optical coherence tomography with histology.European heart journal. Digital health · 2025Article
- Deep learning model DeepNeo predicts neointimal tissue characterization using optical coherence tomography.Communications medicine · 2025Article
- Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review.European heart journal. Digital health · 2025Review
- Intracoronary Optical Coherence Tomography: Technological Innovations and Clinical Implications in Cardiology.Current treatment options in cardiovascular medicine · 2025Review
- Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death.Bioengineering (Basel, Switzerland) · 2024Article
- Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence.Biomedical optics express · 2024Article
- Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve.The international journal of cardiovascular imaging · 2024Article
- A transformer-based pyramid network for coronary calcified plaque segmentation in intravascular optical coherence tomography images.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.Scientific reports · 2024Article
- 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
- A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion.Scientific reports · 2023Article
- Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images.Scientific reports · 2023Article
- The Role of Intracoronary Imaging for the Management of Calcified Lesions.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
We developed a fully automated, two-step deep learning approach for characterizing coronary calcified plaque in intravascular optical coherence tomography (IVOCT) images. First, major calcification lesions were detected from an entire pullback using a 3D convolutional neural network (CNN). Second, a SegNet deep learning model with the Tversky loss function was used to segment calcified plaques in the major calcification lesions. The fully connected conditional random field and the frame interpolation of the missing calcification frames were used to reduce classification errors. We trained/tested the networks on a large dataset comprising 8,231 clinical images from 68 patients with 68 vessels and 4,320
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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.