Abstract

Coronary artery disease is the number one health hazard leading to the pathological formations in coronary artery tissues. In severe cases, they can lead to myocardial infarction and sudden death. Optical Coherence Tomography (OCT) is an interferometric imaging modality, which has been recently used in cardiology to characterize coronary artery tissues providing high resolution ranging from 10 to 20 µm. In this study, we investigate different deep learning models for robust tissue characterization to learn the various intracoronary pathological formations caused by Kawasaki disease (KD) from OCT imaging. The experiments are performed on 33 retrospective cases comprising of pullbacks of intracoronary cross-sectional images obtained from different pediatric patients with KD. Our approach evaluates deep features computed from three different pre-trained convolutional networks. Then, a majority voting approach is applied to provide the final classification result. The results demonstrate high values of accuracy, sensitivity, and specificity for each tissue (up to 0.99 ± 0.01). Hence, deep learning models and especially, majority voting method are robust for automatic interpretation of the OCT images.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125, 549–558 (2018).
[Crossref]

2017 (7)

S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8, 579–592 (2017).
[Crossref]

A. Abdolmanafi, L. Duong, N. Dahdah, and F. Cheriet, “Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography,” Biomed. Opt. Express 8, 1203–1220 (2017).
[Crossref]

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Analysis 42, 60–88 (2017).
[Crossref]

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542, 115 (2017).
[Crossref] [PubMed]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8, 2732–2744 (2017).
[Crossref]

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8, 3627–3642 (2017).
[Crossref]

Y. Taguchi, T. Itoh, H. Oda, Y. Uchimura, K. Kaneko, T. Sakamoto, I. Goto, M. Sakuma, M. Ishida, D. Terashita, and et al., “Coronary risk factors associated with OCT macrophage images and their response after cocr everolimus-eluting stent implantation in patients with stable coronary artery disease,” Atherosclerosis 265, 117–123 (2017).
[Crossref] [PubMed]

2016 (5)

F. K. Swirski, C. S. Robbins, and M. Nahrendorf, “Development and function of arterial and cardiac macrophages,” Trends Immunol. 37, 32–40 (2016).
[Crossref]

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref]

J. J. Rico-Jimenez, D. U. Campos-Delgado, M. Villiger, K. Otsuka, B. E. Bouma, and J. A. Jo, “Automatic classification of atherosclerotic plaques imaged with intravascular OCT,” Biomed. Opt. Express 7, 4069–4085 (2016).
[Crossref]

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, and et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” Jama 316, 2402–2410 (2016).
[Crossref] [PubMed]

M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Med. Image Analysis 35, 18–31 (2016).

2015 (5)

F. Ciompi, B. de Hoop, S. J. van Riel, K. Chung, E. T. Scholten, M. Oudkerk, P. A. de Jong, M. Prokop, and B. van Ginneken, “Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2d views and a convolutional neural network out-of-the-box,” Med. Image Analysis 26, 195–202 (2015).
[Crossref]

M. Gargesha, R. Shalev, D. Prabhu, K. Tanaka, A. M. Rollins, M. Costa, H. G. Bezerra, and D. L. Wilson, “Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography,” J. Medical Imaging 2, 016001 (2015).
[Crossref]

K. S. Rathod, S. M. Hamshere, D. A. Jones, and A. Mathur, “Intravascular ultrasound versus optical coherence tomography for coronary artery imaging–apples and oranges,” Interv. Cardiol. Rev. 10, 8–15 (2015).

W. Liu, Y. Zhang, C.-M. Yu, Q.-W. Ji, M. Cai, Y.-X. Zhao, and Y.-J. Zhou, “Current understanding of coronary artery calcification,” J. Geriatric Cardiology: JGC 12, 668 (2015).

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and et al., “Coronary wall structural changes in patients with Kawasaki disease: new insights from optical coherence tomography (OCT),” J. Am. Hear. Assoc. 4, e001939 (2015).

2014 (1)

M. V. Madhavan, M. Tarigopula, G. S. Mintz, A. Maehara, G. W. Stone, and P. Généreux, “Coronary artery calcification: pathogenesis and prognostic implications,” J. Am. Coll. Cardiol. 63, 1703–1714 (2014).
[Crossref] [PubMed]

2013 (1)

2012 (1)

J. M. Orenstein, S. T. Shulman, L. M. Fox, S. C. Baker, M. Takahashi, T. R. Bhatti, P. A. Russo, G. W. Mierau, J. P. de Chadarévian, E. J. Perlman, and et al., “Three linked vasculopathic processes characterize Kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

2010 (3)

K. Fujii, D. Kawasaki, M. Masutani, T. Okumura, T. Akagami, T. Sakoda, T. Tsujino, M. Ohyanagi, and T. Masuyama, “Oct assessment of thin-cap fibroatheroma distribution in native coronary arteries,” JACC: Cardiovasc. Imaging 3, 168–175 (2010).

G. Van Soest, E. Regar, S. KoljenoviÄ, G. L. van Leenders, N. Gonzalo, S. van Noorden, T. Okamura, B. E. Bouma, G. J. Tearney, J. W. Oosterhuis, and et al., “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref]

J. J. W. Group et al., “Guidelines for diagnosis and management of cardiovascular sequelae in Kawasaki disease (jcs 2008),” Circ. J. 74, 1989–2020 (2010).
[Crossref]

2009 (1)

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review: clinical and research applications,” JACC: Cardiovasc. Interv. 2, 1035–1046 (2009).

2008 (1)

C. Xu, J. M. Schmitt, S. G. Carlier, and R. Virmani, “Characterization of atherosclerosis plaques by measuring both backscattering and attenuation coefficients in optical coherence tomography,” J. Biomed. Opt. 13, 034003 (2008).
[Crossref]

2004 (2)

J. W. Newburger, M. Takahashi, M. A. Gerber, M. H. Gewitz, L. Y. Tani, J. C. Burns, S. T. Shulman, A. F. Bolger, P. Ferrieri, R. S. Baltimore, and et al., “Diagnosis, treatment, and long-term management of Kawasaki disease,” Circulation 110, 2747–2771 (2004).
[Crossref] [PubMed]

M. Hauser, F. Bengel, A. Kuehn, S. Nekolla, H. Kaemmerer, M. Schwaiger, and J. Hess, “Myocardial blood flow and coronary flow reserve in children with normal epicardial coronary arteries after the onset of Kawasaki disease assessed by positron emission tomography,” Pediatr. Cardiol. 25, 108–112 (2004).
[Crossref]

2002 (2)

I.-K. Jang, B. E. Bouma, D.-H. Kang, S.-J. Park, S.-W. Park, K.-B. Seung, K.-B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, and et al., “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. Coll. Cardiol. 39, 604–609 (2002).
[Crossref] [PubMed]

M. Kawasaki, H. Takatsu, T. Noda, K. Sano, Y. Ito, K. Hayakawa, K. Tsuchiya, M. Arai, K. Nishigaki, G. Takemura, and et al., “In vivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings,” Circulation 105, 2487–2492 (2002).
[Crossref] [PubMed]

1997 (1)

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation 9, 1735–1780 (1997).
[Crossref] [PubMed]

1995 (1)

S.-C. Lo, S.-L. Lou, J.-S. Lin, M. T. Freedman, M. V. Chien, and S. K. Mun, “Artificial convolution neural network techniques and applications for lung nodule detection,” IEEE Transactions on Med. Imaging 14, 711–718 (1995).
[Crossref]

Abdolmanafi, A.

Adriaenssens, T.

Akagami, T.

K. Fujii, D. Kawasaki, M. Masutani, T. Okumura, T. Akagami, T. Sakoda, T. Tsujino, M. Ohyanagi, and T. Masuyama, “Oct assessment of thin-cap fibroatheroma distribution in native coronary arteries,” JACC: Cardiovasc. Imaging 3, 168–175 (2010).

Akasaka, T.

H. Kitabata and T. Akasaka, “Visualization of plaque neovascularization by OCT,” in Optical Coherence Tomography, (InTech, 2013).
[Crossref]

Amir, S. B.

Y. Gan, D. Tsay, S. B. Amir, C. C. Marboe, and C. P. Hendon, “Automated classification of optical coherence tomography images of human atrial tissue,” J. Biomed. Opt. 21, 101407 (2016).
[Crossref]

Anguelov, D.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2015), pp. 1–9.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, and a. rabinovich, “& (2015). going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.

Antony, J.

J. Antony, K. McGuinness, N. E. O’Connor, and K. Moran, “Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks,” in Pattern Recognition (ICPR), 2016 23rd International Conference on, (IEEE, 2016), pp. 1195–1200.

Arai, M.

M. Kawasaki, H. Takatsu, T. Noda, K. Sano, Y. Ito, K. Hayakawa, K. Tsuchiya, M. Arai, K. Nishigaki, G. Takemura, and et al., “In vivo quantitative tissue characterization of human coronary arterial plaques by use of integrated backscatter intravascular ultrasound and comparison with angioscopic findings,” Circulation 105, 2487–2492 (2002).
[Crossref] [PubMed]

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K. S. Rathod, S. M. Hamshere, D. A. Jones, and A. Mathur, “Intravascular ultrasound versus optical coherence tomography for coronary artery imaging–apples and oranges,” Interv. Cardiol. Rev. 10, 8–15 (2015).

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Figures (18)

Fig. 1
Fig. 1 Pre-processing steps: (a) Original image, (b) ROI detection using active contour, (c) Applying smallest connected components approach to remove the catheter and unwanted blood cells.
Fig. 2
Fig. 2 AlexNet architecture consists of five convolutional layers, and three fully connected layers.
Fig. 3
Fig. 3 VGG-19 architecture consists of sixteen convolutional layers, and three fully connected layers.
Fig. 4
Fig. 4 Last layers of the Inception-v3 architecture.
Fig. 5
Fig. 5 OOB error rate is calculated to find the optimal number of trees to train Random Forest model. The performance of Random Forest is evaluated by calculating OOB errors while it is trained on each set of features extracted from each network separately. The OOB error rate is calculated for 1000 of trees.
Fig. 6
Fig. 6 Confusion matrix of intracoronary tissue classification using fine-tuned AlexNet.
Fig. 7
Fig. 7 Confusion matrix of intracoronary tissue classification using fine-tuned VGG-19.
Fig. 8
Fig. 8 Confusion matrix of intracoronary tissue classification using fine-tuned Inception-v3.
Fig. 9
Fig. 9 Confusion matrix of intracoronary tissue classification: Random Forest is trained using the deep features extracted from AlexNet.
Fig. 10
Fig. 10 Confusion matrix of intracoronary tissue classification: Random Forest is trained using the deep features extracted from VGG-19.
Fig. 11
Fig. 11 Confusion matrix of intracoronary tissue classification: Random Forest is trained using the deep features extracted from Inception-v3.
Fig. 12
Fig. 12 Confusion matrix of intracoronary tissue classification using majority voting approach.
Fig. 13
Fig. 13 Confusion matrix of intracoronary tissue classification using RF: Combination of features extracted from pre-trained AlexNet, and VGG-19 are used to train Random Forest.
Fig. 14
Fig. 14 Accuracy is reported as the mean ± standard deviation of the measured accuracies for all the tissues in each experiment.
Fig. 15
Fig. 15 Sensitivity is reported as the mean ± standard deviation of the measured sensitivities for all tissues in each experiment.
Fig. 16
Fig. 16 Specificity is reported as the mean ± standard deviation of the measured specificities for all tissues in each experiment.
Fig. 17
Fig. 17 From left to right: The first image shows the original OCT image in planar representation, manual segmentation for each tissue is illustrated in the second image, and the third image is the classification result, which is shown for intima in (a), media in (b), fibrosis in (c), neovascularization in (d), macrophage in (e), and calcification in (f).
Fig. 18
Fig. 18 Leave-one-out cross-validation using pre-trained networks as feature extractor and majority voting for final classification results. The experiments were performed 33 times and each time, one patient was left out for test set and the classifier was trained on the OCT images of the remaining patients. Measured accuracies obtained from all the patients are sorted from lower to higher accuracy.

Tables (11)

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Table 1 Information of the dataset used for this study.

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Table 2 Measured sensitivity, specificity, and accuracy of tissue classification using fine-tuned AlexNet.

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Table 3 Measured sensitivity, specificity, and accuracy of tissue classification using fine-tuned VGG-19.

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Table 4 Measured sensitivity, specificity, and accuracy of tissue classification using fine-tuned Inception-v3.

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Table 5 Measured sensitivity, specificity, and accuracy of tissue classification using RF. Features are extracted from AlexNet.

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Table 6 Measured sensitivity, specificity, and accuracy of tissue classification using RF. Features are extracted from VGG-19.

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Table 7 Measured sensitivity, specificity, and accuracy of tissue classification using RF. Features are extracted from Inception-v3.

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Table 8 Measured sensitivity, specificity, and accuracy of tissue classification using majority voting approach.

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Table 9 Measured sensitivity, specificity, and accuracy of tissue classification: Combination of features extracted from pre-trained AlexNet, and VGG-19 are used to train Random Forest.

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Table 10 Accuracy, sensitivity, and specificity obtained from each experiment. The accuracy, sensitivity, and specificity are reported as the mean ± standard deviation of the values of accuracy, sensitivity, and specificity obtained for all the tissues performing each experiment.

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Table 11 Measured sensitivity, specificity, and accuracy of tissue classification: Using the final model (feature extraction using CNNs, classification using RF, and final classification result by majority voting), we perform the experiment in 10 iterations to evaluate the performance of the model using various randomization of the training, validation, and test sets. The accuracy, sensitivity, and specificity are reported as the mean ± std for all the iterations.

Equations (5)

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L = ( 1 / | X | ) j | X | l n ( p ( y j | X j ) )
V i + 1 = μ V i γ i α L / W
W i + 1 = W i + V i + 1
C ( x ) = a r g m a x i j w j I ( C j ( x ) = i )
C ( x ) = m o d e { C 1 ( x ) , C 2 ( x ) , C 3 ( x ) }

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