Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography

Biomedical Optics Express

Volumn 8, Issue 2, 2017, Pages 1203-1220

  Abdolmanafi, Atefeh ^a   Duong, Luc ^a   Dahdah, Nagib ^b   Cheriet, Farida ^c  

^a ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

^b UNIVERSITY OF MONTREAL   (Canada)

^c ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMINERALIZATION; BLOOD VESSELS; DECISION TREES; DEEP LEARNING; HEART; HISTOLOGY; INFRARED DEVICES; NEURAL NETWORKS; OPTICAL TOMOGRAPHY; PEDIATRICS; SUPPORT VECTOR MACHINES;

AUTOMATIC CLASSIFICATION; CLASSIFICATION RATES; CONVOLUTIONAL NEURAL NETWORK; DEEP FEATURE LEARNING; HIGH SPATIAL RESOLUTION; NEAR INFRARED LIGHT; PEDIATRIC PATIENTS; TISSUE CLASSIFICATION;

TISSUE;

ARTERY INTIMA PROLIFERATION; ARTERY OCCLUSION; ARTERY THROMBOSIS; ARTICLE; CLASSIFICATION; CLINICAL ARTICLE; CORONARY ARTERY; CORONARY ARTERY ANEURYSM; HUMAN; IMAGE ANALYSIS; IMAGE SEGMENTATION; MACHINE LEARNING; MEASUREMENT ACCURACY; MUCOCUTANEOUS LYMPH NODE SYNDROME; NERVE CELL NETWORK; OPTICAL COHERENCE TOMOGRAPHY; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TUNING CURVE; VALIDATION PROCESS;

EID: 85011565816     PISSN: None     EISSN: 21567085     Source Type: Journal    
DOI: 10.1364/BOE.8.001203     Document Type: Article

References (46)

1. A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, N. Dahdah, “Coronary wall structural changes in patients with kawasaki disease: new insights from optical coherence tomography (oct),” J. Am. Heart Assoc. 4, e001939 (2015).
2. E. Regar, J. Ligthart, N. Bruining, G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
3. B. Preim and D. Bartz, Visualization in Medicine: Theory, Algorithms, and Applications (Morgan Kaufmann, 2007).
4. G. Ferrante, P. Presbitero, R. Whitbourn, and P. Barlis, “Current applications of optical coherence tomography for coronary intervention,” Int. J. Cardiol. 165, 7–16 (2013).
5. 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: Cardiovascular Interventions 2, 1035–1046 (2009).
6. R. A. Costa, M. Skaf, L. A. Melo, D. Calucci, J. A. Cardillo, J. C. Castro, D. Huang, and M. Wojtkowski, “Retinal assessment using optical coherence tomography,” Prog. Retinal Eye Res. 25, 325–353 (2006).
7. A. M. Zysk, F. T. Nguyen, A. L. Oldenburg, D. L. Marks, and S. A. Boppart, “Optical coherence tomography: a review of clinical development from bench to bedside,” J. Biomed. Opt. 12, 051403 (2007).
8. 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, W. R. Wilson, L. M. Baddour, M. E. Levison, T. J. Pallasch, D. A. Falace, K. A. Taubert “Diagnosis, treatment, and long-term management of kawasaki disease a statement for health professionals from the committee on rheumatic fever, endocarditis and kawasaki disease, council on cardiovascular disease in the young, american heart association,” Circulation 110, 2747–2771 (2004).
9. 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, C. Trevenen, A. T. Rotta, M. B. Kalelkar, A. H. Rowley “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
10. K. C. Harris, A. Manouzi, A. Y. Fung, A. De Souza, H. G. Bezerra, J. E. Potts, and M. C. Hosking, “Feasibility of optical coherence tomography in children with kawasaki disease and pediatric heart transplant recipients,” Circulation: Cardiovascular Imaging 7, 671–678 (2014).
11. S. Celi and S. Berti, “In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading,” Med. Image Anal. 18, 1157–1168 (2014).
12. H. Yabushita, B. E. Bouma, S. L. Houser, H. T. Aretz, I.-K. Jang, K. H. Schlendorf, C. R. Kauffman, M. Shishkov, D.- H. Kang, E. F. Halpern, G. J. Tearney “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
13. D. Levitz, L. Thrane, M. Frosz, P. Andersen, C. Andersen, S. Andersson-Engels, J. Valanciunaite, J. Swartling, and P. Hansen, “Determination of optical scattering properties of highly-scattering media in optical coherence tomography images,” Opt. Express 12, 249–259 (2004).
14. C. Xu, J. M. Schmitt, S. G. Carlier, and R. Virmani, “Characterization of atherosclerosis plaques by measuring both backscattering and attenuation coeffcients in optical coherence tomography,” J. Biomed. Opt. 13, 034003 (2008).
15. G. Van Soest, T. Goderie, E. Regar, S. Koljenović, G. L. van Leenders, N. Gonzalo, S. van Noorden, T. Okamura, B. E. Bouma, G. J. Tearney, J. W. Oosterhuis, P. W. Serruys, A. F. W Van der Steen “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
16. G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. Dâ Ăźhooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. express 4, 1014–1030 (2013).
17. 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.
18. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).
19. M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European Conference on Computer Vision (Springer, 2014), pp. 818–833.
20. D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun, “Understanding deep architectures using a recursive convolutional network,” arXiv preprint arXiv:1312.1847 (2013).
21. S.-C. B. Lo, J.-S. Lin, M. T. Freedman, and S. K. Mun, “Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network,” in “Medical Imaging 1993,” (International Society for Optics and Photonics, 1993), pp. 859–869.
22. H.-P. Chan, S.-C. B. Lo, B. Sahiner, K. L. Lam, and M. A. Helvie, “Computer-aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network,” Med. Phys. 22, 1555–1567 (1995).
23. B. Sahiner, H.-P. Chan, N. Petrick, D. Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images,” IEEE Trans. Medical Imaging 15, 598–610 (1996).
24. H.-C. Shin, M. R. Orton, D. J. Collins, S. J. Doran, and M. O. Leach, “Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4d patient data,” IEEE Trans. Pattern Analysis and Machine Intelligence 35, 1930–1943 (2013).
25. H. R. Roth, A. Farag, L. Lu, E. B. Turkbey, and R. M. Summers, “Deep convolutional networks for pancreas segmentation in ct imaging,” in “SPIE Medical Imaging,” (International Society for Optics and Photonics, 2015), pp. 94131G.
26. 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 Anal. 26, 195–202 (2015).
27. 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 Anal. (2016).
28. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation 9, 1735–1780 (1997).
29. H. Azizpour, A. Sharif Razavian, J. Sullivan, A. Maki, and S. Carlsson, “From generic to specific deep representations for visual recognition,” in “Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops,” (2015), pp. 36–45.
30. B. van Ginneken, A. A. Setio, C. Jacobs, and F. Ciompi, “Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans,” in “2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI),” (IEEE, 2015), pp. 286–289.
31. Y. Bar, I. Diamant, L. Wolf, and H. Greenspan, “Deep learning with non-medical training used for chest pathology identification,” in “SPIE Medical Imaging,” (International Society for Optics and Photonics, 2015), pp. 94140V.
32. J. Arevalo, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. A. G. Lopez, “Convolutional neural networks for mammography mass lesion classification,” in “2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),” (IEEE, 2015), pp. 797–800.
33. H. Chen, D. Ni, J. Qin, S. Li, X. Yang, T. Wang, and P. A. Heng, “Standard plane localization in fetal ultrasound via domain transferred deep neural networks,” IEEE J. Biomed. Health Informatics 19, 1627–1636 (2015).
34. G. Carneiro, J. Nascimento, and A. P. Bradley, “Unregistered multiview mammogram analysis with pre-trained deep learning models,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), pp. 652–660.
35. M. Gao, U. Bagci, L. Lu, A. Wu, M. Buty, H.-C. Shin, H. Roth, G. Z. Papadakis, A. Depeursinge, R. M. Summers, Z. Xu, D. J. Mollura “Holistic classification of ct attenuation patterns for interstitial lung diseases via deep convolutional neural networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization pp. 1–6 (2016).
36. J. Margeta, A. Criminisi, R. Cabrera Lozoya, D. C. Lee, and N. Ayache, “Fine-tuned convolutional neural nets for cardiac mri acquisition plane recognition,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization pp. 1–11 (2015).
37. N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang, “Convolutional neural networks for medical image analysis: Full training or fine tuning?” IEEE Trans. Med. Imaging 35, 1299–1312 (2016).
38. A. Abdolmanafi, A. S. Prasad, L. Duong, and N. Dahdah, “Classification of coronary artery tissues using optical coherence tomography imaging in kawasaki disease,” in “SPIE Medical Imaging,” (International Society for Optics and Photonics, 2016), pp. 97862U.
39. H. Azarnoush, S. Vergnole, V. Pazos, C.-É. Bisaillon, B. Boulet, and G. Lamouche, “Intravascular optical coherence tomography to characterize tissue deformation during angioplasty: preliminary experiments with artery phantoms,” J. Biomed. Opt. 17, 096015 (2012).
40. N. Foin, J. M. Mari, S. Nijjer, S. Sen, R. Petraco, M. Ghione, C. Di Mario, J. E. Davies, and M. J. Girard, “Intracoronary imaging using attenuation-compensated optical coherence tomography allows better visualisation of coronary artery diseases,” Cardiovascular Revascularization Medicine 14, 139–143 (2013).
41. D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” J. Physiol. 148, 574–591 (1959).
42. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (Springer, 2012), pp. 1097–1105.
43. A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis (Springer Science & Business Media, 2013).
44. M. Kuhn and K. Johnson, Applied Predictive Modeling (Springer, 2013).
45. Z. Wang and X. Xue, “Multi-class support vector machine,” in Support Vector Machines Applications (Springer, 2014), pp. 23–48.
46. C.-C. Chang and C.-J. Lin, “Libsvm: a library for support vector machines,” ACM Trans. Intelligent Systems and Technology (TIST) 2, 27 (2011).