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Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks
- Source :
- Journal of Biomedical Optics
- Publication Year :
- 2020
- Publisher :
- Society of Photo-Optical Instrumentation Engineers, 2020.
-
Abstract
- Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores=94% for non-zeros padding and F1-score=96% for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability.
- Subjects :
- Paper
Computer science
Biomedical Engineering
Image processing
Plaque, Amyloid
01 natural sciences
Padding
Convolutional neural network
010309 optics
Biomaterials
0103 physical sciences
Medical imaging
Humans
Segmentation
General
plaque calcification
Contextual image classification
business.industry
Deep learning
Calcinosis
deep learning
Pattern recognition
Image segmentation
intravascular optical coherence tomography
Atomic and Molecular Physics, and Optics
Plaque, Atherosclerotic
Electronic, Optical and Magnetic Materials
Artificial intelligence
Neural Networks, Computer
atherosclerosis
business
Tomography, Optical Coherence
Subjects
Details
- Language :
- English
- ISSN :
- 15602281 and 10833668
- Volume :
- 25
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Journal of Biomedical Optics
- Accession number :
- edsair.doi.dedup.....6413805f6250939ac5dbf88ce0751328