Detecting Vascular Bifurcation in IVOCT Images Using Convolutional Neural Networks With Transfer Learning

Miyagawa, M.; Costa, Marly Guimarães Fernandes; Gutierrez, Marco Antônio; Costa, João Pedro Guimarães Fernandes; Filho, Cícero Ferreira Fernandes Costa

doi:10.1109/access.2019.2918017

Cited by 18 publications

(10 citation statements)

References 27 publications

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“…Pre-processing was performed in various steps of binarization, morphological gradient, Hough transform, and cropping. 35 Macrophage accumulation was detected using normalized-intensity standard deviation approach. 36 It should be considered that macrophage accumulation results in plaque development and progression, but it is not considered as coronary plaque.…”

Section: Related Studiesmentioning

confidence: 99%

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A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images

Abdolmanafi

Duong

Ibrahim³

et al. 2021

Medical Physics

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Purpose: Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arterial wall tissues. Optical Coherence Tomography (OCT) is a light-based imaging system used in cardiology to analyze intracoronary tissue layers and pathological formations including plaque accumulation. This state-of-the-art catheter-based imaging system provides intracoronary cross-sectional images with high resolution of 10-15 µm. But interpretation of the acquired images is operator dependent, which is not only very time-consuming but also highly error prone from one observer to another. An automatic and accurate coronary plaque tagging using OCT image post-processing can contribute to wide adoption of the OCT system and reducing the diagnostic error rate. Method: In this study, we propose a combination of spatial pyramid pooling module with dilated convolutions for semantic segmentation to extract atherosclerotic tissues regardless of their types and training a sparse auto-encoder to reconstruct the input features and enlarge the training data as well as plaque type characterization in OCT images. Results: The results demonstrate high precision of the proposed model with reduced computational complexity, which can be appropriate for real-time analysis of OCT images. At each step of the work, measured accuracy, sensitivity, specificity of more than 93% demonstrate high performance of the model. Conclusion:The main focus of this study is atherosclerotic tissue characterization using OCT imaging. This contributes to wide adoption of the OCT imaging system by providing clinicians with a fully automatic interpretation of various atherosclerotic tissues. Future studies will be focused on analyzing atherosclerotic vulnerable plaques, those coronary plaques which are prone to rupture.

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Section: Related Studiesmentioning

confidence: 99%

“…Combination of four different CNN models is applied to detect vascular bifurcation using OCT imaging. Pre‐processing was performed in various steps of binarization, morphological gradient, Hough transform, and cropping 35 . Macrophage accumulation was detected using normalized‐intensity standard deviation approach 36 .…”

Section: Related Studiesmentioning

confidence: 99%

A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images

Abdolmanafi

Duong

Ibrahim³

et al. 2021

Medical Physics

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“…Several studies have demonstrated that a pretrained CNN could be adapted to the medical image classification. [29][30][31] For instance, Christodoulidis et al 30 fine-tuned different CNN layers for classification of interstitial lung diseases. Their studies suggested that the ensemble learn could help for the unbalance medical images.…”

Section: Related Workmentioning

confidence: 99%

“…31 The labeled MRI data were used to train the fully connected layers while keeping the rest network layer fixed. Different from the previous approaches, Miyagawa et al 29 transferred the knowledge from other tasks based on OCT images to the classification of vascular bifurcation, and this method yielded promising results. Figure 1 shows the flowchart of the methodology.…”

Section: Related Workmentioning

confidence: 99%

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Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks

Wang

Yin

et al. 2020

J. Biomed. Opt.

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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 ¼ 94% for non-zeros padding and F1-score ¼ 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.

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A Review of Deep Learning on Medical Image Analysis

et al. 2020

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Detecting Vascular Bifurcation in IVOCT Images Using Convolutional Neural Networks With Transfer Learning

Cited by 18 publications

References 27 publications

A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images

A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images

Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks

A Review of Deep Learning on Medical Image Analysis

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