Coronary calcification identification in optical coherence tomography using convolutional neural networks

Oliveira, Dário Augusto Borges; Macedo, Maysa M. G.; Nicz, Pedro Felipe Gomes; Campos, Carlos M.; Lemos, Pedro A.; Gutierrez, Marco Antônio

doi:10.1117/12.2293753

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Few methods have been presented during the last decade for detecting and characterizing atherosclerotic plaque using OCT images 10,[14][15][16] . These methods were primarily based on machine learning algorithms 10,13,14 and most recently on deep learning approaches using convolutional neural networks (CNN) 15,16 . These methods can sufficiently detect a large percentage of the atherosclerotic tissue within the arterial wall.…”

Section: Discussionmentioning

confidence: 99%

“…Going one step further and using machine learning, Athanasiou et al 10 presented a fully-automated OCT plaque characterization method which classified plaque as CA, LT, FT, or MT, with 83% accuracy. More recently, deep learning approaches using convolutional neural networks (CNNs) [15][16][17][18] were presented, achieving an overall accuracy of up to 91.7% 18 .…”

Section: Introductionmentioning

confidence: 99%

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A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography

Athanasiou

Olender

Ben–Assa

et al. 2019

Medical Imaging 2019: Computer-Aided Diagnosis

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Optical coherence tomography (OCT) is a fiber-based intravascular imaging modality that produces high-resolution tomographic images of artery lumen and vessel wall morphology. Manual analysis of the diseased arterial wall is time consuming and sensitive to inter-observer variability; therefore, machine-learning methods have been developed to automatically detect and classify mural composition of atherosclerotic vessels. However, none of the tissue classification methods include in their analysis the outer border of the OCT vessel, they consider the whole arterial wall as pathological, and they do not consider in their analysis the OCT imaging limitations, e.g. shadowed areas. The aim of this study is to present a deep learning method that subdivides the whole arterial wall into six different classes: calcium, lipid tissue, fibrous tissue, mixed tissue, non-pathological tissue or media, and no visible tissue. The method steps include defining wall area (WAR) using previously developed lumen and outer border detection methods, and automatic characterization of the WAR using a convolutional neural network (CNN) algorithm. To validate this approach, 700 images of diseased coronary arteries from 28 patients were manually annotated by two medical experts, while the non-pathological wall and media was automatically detected based on the Euclidian distance of the lumen to the outer border of the WAR. Using the proposed method, an overall classification accuracy 96% is reported, indicating great promise for clinical translation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography

Athanasiou

Olender

Ben–Assa

et al. 2019

Medical Imaging 2019: Computer-Aided Diagnosis

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Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks

Gessert

Lutz

Heyder

et al. 2019

IEEE Trans. Med. Imaging

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Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient which requires automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new dataset consisting of in-vivo patient images labeled by three trained experts. Using this dataset, we employ state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible.

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A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning

Shi¹,

Xin²,

Zheng³

2022

J. Opt. Soc. Am. A

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Automatic detection of thin-cap fibroatheroma (TCFA) is essential to prevent acute coronary syndrome. Hence, in this paper, a method is proposed to detect TCFAs by directly classifying each A-line using multi-view intravascular optical coherence tomography (IVOCT) images. To solve the problem of false positives, a multi-input–output network was developed to implement image-level classification and A-line-based classification at the same time, and a contrastive consistency term was designed to ensure consistency between two tasks. In addition, to learn spatial and global information and obtain the complete extent of TCFAs, an architecture and a regional connectivity constraint term are proposed to classify each A-line of IVOCT images. Experimental results obtained on the 2017 China Computer Vision Conference IVOCT dataset show that the proposed method achieved state-of-art performance with a total score of 88.7 ± 0.88 % , overlap rate of 88.64 ± 0.26 % , precision rate of 84.34 ± 0.86 % , and recall rate of 93.67 ± 2.29 % .

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Coronary calcification identification in optical coherence tomography using convolutional neural networks

Cited by 5 publications

References 13 publications

A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography

A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography

Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks

A-line-based thin-cap fibroatheroma detection with multi-view IVOCT images using multi-task learning and contrastive learning

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