Automated Detection of Vulnerable Plaque for Intravascular Optical Coherence Tomography Images

Liu, Ran; Zhang, Yanzhen; Zheng, Yangting; Liu, Yaqiong; Zhao, Yang; Lin, Yi

doi:10.1007/s13239-019-00425-2

Cited by 27 publications

(28 citation statements)

References 23 publications

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“…Concerning OCT, Wang et al [ 49 ] proposed a computer-aided method for quantification of fibrous cap (FC) thickness to indicate vulnerable plaques. Liu [ 50 ] proposed an automatic detection system of vulnerable plaque for IVOCT images based on a deep convolutional neural network (DCNN). The system is mainly composed of four modules: pre-processing, deep convolutional neural networks (DCNNs), post-processing, and ensemble.…”

Section: Application Of Ai In Coronary Atherosclerotic Plaque Analysismentioning

confidence: 99%

Artificial Intelligence in Cardiovascular Atherosclerosis Imaging

Zhang¹,

Han

Shao

et al. 2022

JPM

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At present, artificial intelligence (AI) has already been applied in cardiovascular imaging (e.g., image segmentation, automated measurements, and eventually, automated diagnosis) and it has been propelled to the forefront of cardiovascular medical imaging research. In this review, we presented the current status of artificial intelligence applied to image analysis of coronary atherosclerotic plaques, covering multiple areas from plaque component analysis (e.g., identification of plaque properties, identification of vulnerable plaque, detection of myocardial function, and risk prediction) to risk prediction. Additionally, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging of atherosclerotic plaques, as well as lessons that can be learned from other areas. The continuous development of computer science and technology may further promote the development of this field.

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Section: Application Of Ai In Coronary Atherosclerotic Plaque Analysismentioning

confidence: 99%

Artificial Intelligence in Cardiovascular Atherosclerosis Imaging

Zhang¹,

Han

Shao

et al. 2022

JPM

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“…Overall, OCT imaging is useful in the early differential detection of gastrointestinal tumors. The intraluminal optical tomography scanner ( 62 , 69 )could become a helpful reference for rapid, low-cost, non-invasive light biopsy, early differential diagnosis, and treatment of gastrointestinal cancers ( Table 2 ).…”

Section: Application Of Oct In Oncologymentioning

confidence: 99%

Research progress on the application of optical coherence tomography in the field of oncology

et al. 2022

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Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the “gold standard” for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin’s surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.

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“…To assess the vulnerability of plaques, quantifying multiple plaque components and subtypes is essential. Liu et al, developed an ensemble method to combine the outputs of multiple networks to improve the accuracy of detecting vulnerable regions [ 150 ]. By combining the Adaboost, YOLO, SSD, and Faster region-based CNN outputs, a precision and recall of 88.84% and 95.02%, respectively, were reached, with a total detection quality of 88.46%.…”

Section: Plaque Characteristics and Subtypesmentioning

confidence: 99%

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

et al. 2022

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Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

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Automated Detection of Vulnerable Plaque for Intravascular Optical Coherence Tomography Images

Cited by 27 publications

References 23 publications

Artificial Intelligence in Cardiovascular Atherosclerosis Imaging

Artificial Intelligence in Cardiovascular Atherosclerosis Imaging

Research progress on the application of optical coherence tomography in the field of oncology

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

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