scholarly journals Characterization of Coronary Atherosclerotic Plaque Composition Based on Convolutional Neural Network (CNN)

2019 ◽  
Vol 16 (s1) ◽  
pp. 57-57
Author(s):  
Yifan Yin ◽  
Chunliu He ◽  
Biao Xu and Zhiyong Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Atherosclerotic Plaque ◽  
Plaque Composition ◽  
Coronary Atherosclerotic Plaque

scholarly journals A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

2017 ◽  
Vol 21 (1) ◽  
pp. 48-55 ◽  
Author(s):  
Karim Lekadir ◽  
Alfiia Galimzianova ◽  
Angels Betriu ◽  
Maria del Mar Vila ◽  
Laura Igual ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Carotid Ultrasound ◽  
Plaque Composition

scholarly journals Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture

2021 ◽  
Vol 8 ◽  
Author(s):  
Yifan Yin ◽  
Chunliu He ◽  
Biao Xu ◽  
Zhiyong Li
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Convolutional Neural Network ◽  
Atherosclerotic Plaque ◽  
Network Architecture ◽  
Coronary Plaque ◽  
Optical Coherence ◽  
Plaque Characterization ◽  
Coronary Atherosclerotic Plaque ◽  
Oct Imaging

Background: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging. However, plaque characterization relies on the interpretation of large datasets by well-trained observers. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. The TwopathCNN architecture and cascaded structure show significant improvement in performance (p < 0.05). CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques.

Association of Statin Treatment With Progression of Coronary Atherosclerotic Plaque Composition

2021 ◽  
Author(s):  
Alexander R. van Rosendael ◽  
Inge J. van den Hoogen ◽  
Umberto Gianni ◽  
Xiaoyue Ma ◽  
Sara W. Tantawy ◽  
...  
Keyword(s):  
Atherosclerotic Plaque ◽  
Statin Treatment ◽  
Plaque Composition ◽  
Coronary Atherosclerotic Plaque

scholarly journals In Vivo Characterization of Coronary Atherosclerotic Plaque by Use of Optical Coherence Tomography

Circulation ◽  
2005 ◽  
Vol 111 (12) ◽  
pp. 1551-1555 ◽  
Author(s):  
Ik-Kyung Jang ◽  
Guillermo J. Tearney ◽  
Briain MacNeill ◽  
Masamichi Takano ◽  
Fabian Moselewski ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Atherosclerotic Plaque ◽  
Optical Coherence ◽  
Coronary Atherosclerotic Plaque ◽  
In Vivo Characterization
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