Abstract

Kawasaki disease (KD) is an acute childhood disease complicated by coronary artery aneurysms, intima thickening, thrombi, stenosis, lamellar calcifications, and disappearance of the media border. Automatic classification of the coronary artery layers (intima, media, and scar features) is important for analyzing optical coherence tomography (OCT) images recorded in pediatric patients. OCT has been known as an intracoronary imaging modality using near-infrared light which has recently been used to image the inner coronary artery tissues of pediatric patients, providing high spatial resolution (ranging from 10 to 20 μm). This study aims to develop a robust and fully automated tissue classification method by using the convolutional neural networks (CNNs) as feature extractor and comparing the predictions of three state-of-the-art classifiers, CNN, random forest (RF), and support vector machine (SVM). The results show the robustness of CNN as the feature extractor and random forest as the classifier with classification rate up to 96%, especially to characterize the second layer of coronary arteries (media), which is a very thin layer and it is challenging to be recognized and specified from other tissues.

© 2017 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Characterization of coronary artery pathological formations from OCT imaging using deep learning

Atefeh Abdolmanafi, Luc Duong, Nagib Dahdah, Ibrahim Ragui Adib, and Farida Cheriet
Biomed. Opt. Express 9(10) 4936-4960 (2018)

Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images

Ping Zhou, Tongjing Zhu, Chunliu He, and Zhiyong Li
J. Opt. Soc. Am. A 34(7) 1152-1159 (2017)

Intravascular optical coherence tomography [Invited]

Brett E. Bouma, Martin Villiger, Kenichiro Otsuka, and Wang-Yuhl Oh
Biomed. Opt. Express 8(5) 2660-2686 (2017)

References

  • View by:
  • |
  • |
  • |

  1. A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
    [Crossref] [PubMed]
  2. E. Regar, J. Ligthart, N. Bruining, and G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
    [Crossref] [PubMed]
  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).
    [Crossref]
  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).
    [Crossref]
  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).
    [Crossref] [PubMed]
  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, and 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).
    [Crossref] [PubMed]
  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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
    [Crossref] [PubMed]
  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).
    [Crossref] [PubMed]
  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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
    [Crossref] [PubMed]
  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).
    [Crossref] [PubMed]
  14. 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] [PubMed]
  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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
    [Crossref] [PubMed]
  16. G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. D’hooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. express 4, 1014–1030 (2013).
    [Crossref] [PubMed]
  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).
    [Crossref] [PubMed]
  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).
    [Crossref] [PubMed]
  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).
    [Crossref]
  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).
    [Crossref] [PubMed]
  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).
    [Crossref] [PubMed]
  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).
    [Crossref]
  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, and 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).
    [Crossref] [PubMed]
  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).
    [Crossref]
  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).
    [Crossref] [PubMed]
  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).
    [Crossref] [PubMed]
  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).
    [Crossref]
  44. M. Kuhn and K. Johnson, Applied Predictive Modeling (Springer, 2013).
    [Crossref]
  45. Z. Wang and X. Xue, “Multi-class support vector machine,” in Support Vector Machines Applications (Springer, 2014), pp. 23–48.
    [Crossref]
  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).

2016 (1)

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).
[Crossref] [PubMed]

2015 (3)

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).
[Crossref] [PubMed]

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).
[Crossref]

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

2014 (2)

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).

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).
[Crossref] [PubMed]

2013 (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).
[Crossref]

G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. D’hooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. express 4, 1014–1030 (2013).
[Crossref] [PubMed]

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).
[Crossref]

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).
[Crossref] [PubMed]

2012 (2)

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).
[Crossref]

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

2011 (2)

E. Regar, J. Ligthart, N. Bruining, and G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
[Crossref] [PubMed]

C.-C. Chang and C.-J. Lin, “Libsvm: a library for support vector machines,” ACM Trans. Intelligent Systems and Technology (TIST) 2, 27 (2011).

2010 (1)

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

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: Cardiovascular Interventions 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] [PubMed]

2007 (1)

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).
[Crossref] [PubMed]

2006 (1)

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).
[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, W. R. Wilson, L. M. Baddour, M. E. Levison, T. J. Pallasch, D. A. Falace, and 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).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

2002 (1)

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

1997 (1)

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

1996 (1)

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).
[Crossref] [PubMed]

1995 (1)

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).
[Crossref] [PubMed]

1959 (1)

D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” J. Physiol. 148, 574–591 (1959).
[Crossref] [PubMed]

Abdolmanafi, A.

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.

Adler, D. D.

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).
[Crossref] [PubMed]

Adriaenssens, T.

Andersen, C.

Andersen, P.

Andersson-Engels, S.

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.

Aretz, H. T.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Arevalo, J.

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.

Ayache, N.

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).

Azarnoush, H.

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).
[Crossref]

Azizpour, H.

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.

Baddour, L. M.

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, and 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).
[Crossref] [PubMed]

Bagci, U.

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, and 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).

Baker, S. C.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Bakloul, M.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Baltimore, R. S.

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, and 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).
[Crossref] [PubMed]

Bar, Y.

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.

Barlis, P.

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).
[Crossref]

Bartz, D.

B. Preim and D. Bartz, Visualization in Medicine: Theory, Algorithms, and Applications (Morgan Kaufmann, 2007).

Bengio, Y.

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).

Berti, S.

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).
[Crossref] [PubMed]

Bezerra, H. G.

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).

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).

Bhatti, T. R.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Biard, A.

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).

Bisaillon, C.-É.

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).
[Crossref]

Bolger, A. F.

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, and 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).
[Crossref] [PubMed]

Boppart, S. A.

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).
[Crossref] [PubMed]

Boulet, B.

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).
[Crossref]

Bouma, B. E.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Bradley, A. P.

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.

Bruining, N.

E. Regar, J. Ligthart, N. Bruining, and G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
[Crossref] [PubMed]

Burns, J. C.

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, and 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).
[Crossref] [PubMed]

Buty, M.

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, and 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).

Cabrera Lozoya, R.

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).

Calucci, D.

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).
[Crossref]

Cardillo, J. A.

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).
[Crossref]

Carlier, S. G.

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] [PubMed]

Carlsson, S.

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.

Carneiro, G.

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.

Castro, J. C.

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).
[Crossref]

Celi, S.

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).
[Crossref] [PubMed]

Chan, H.-P.

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).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

Chang, C.-C.

C.-C. Chang and C.-J. Lin, “Libsvm: a library for support vector machines,” ACM Trans. Intelligent Systems and Technology (TIST) 2, 27 (2011).

Chen, H.

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).
[Crossref]

Chung, K.

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).
[Crossref] [PubMed]

Ciompi, F.

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).
[Crossref] [PubMed]

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.

Collins, D. J.

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).
[Crossref]

Costa, M. A.

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).

Costa, R. A.

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).
[Crossref]

Courville, A.

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).

Criminisi, A.

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).

A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis (Springer Science & Business Media, 2013).
[Crossref]

D’hooge, J.

Dahdah, N.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

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.

Davies, J. E.

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).
[Crossref] [PubMed]

Davy, A.

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).

de Chadarévian, J. P.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

de Hoop, B.

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).
[Crossref] [PubMed]

de Jong, P. A.

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).
[Crossref] [PubMed]

De Souza, A.

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).

Depeursinge, A.

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, and 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).

Déry, J.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Desmet, W.

Di Mario, C.

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).
[Crossref] [PubMed]

Diamant, I.

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.

Dionne, A.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Doran, S. J.

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).
[Crossref]

Duong, L.

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.

Eigen, D.

D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun, “Understanding deep architectures using a recursive convolutional network,” arXiv preprint arXiv:1312.1847 (2013).

Erhan, 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.

Falace, D. A.

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, and 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).
[Crossref] [PubMed]

Farag, A.

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.

Fergus, R.

D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun, “Understanding deep architectures using a recursive convolutional network,” arXiv preprint arXiv:1312.1847 (2013).

M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European Conference on Computer Vision (Springer, 2014), pp. 818–833.

Ferrante, G.

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).
[Crossref]

Ferrieri, P.

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, and 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).
[Crossref] [PubMed]

Foin, N.

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).
[Crossref] [PubMed]

Fournier, A.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Fox, L. M.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Freedman, M. T.

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.

Frosz, M.

Fung, A. Y.

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).

Gao, M.

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, and 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).

Gebhard, C.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Gerber, M. A.

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, and 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).
[Crossref] [PubMed]

Gewitz, M. H.

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, and 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).
[Crossref] [PubMed]

Ghione, M.

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).
[Crossref] [PubMed]

Girard, M. J.

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).
[Crossref] [PubMed]

Girard, P.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Goderie, T.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

González, F. A.

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.

Gonzalo, N.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

Goodsitt, M. M.

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).
[Crossref] [PubMed]

Gotway, M. B.

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).
[Crossref] [PubMed]

Greenspan, H.

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.

Guagliumi, G.

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).

Gurudu, S. R.

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).
[Crossref] [PubMed]

Halpern, E. F.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Hansen, P.

Harris, K. C.

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).

Havaei, M.

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).

Helvie, M. A.

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).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

Heng, P. A.

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).
[Crossref]

Hinton, G. E.

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.

Hochreiter, S.

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

Hosking, M. C.

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).

Houser, S. L.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Huang, D.

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).
[Crossref]

Hubel, D. H.

D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” J. Physiol. 148, 574–591 (1959).
[Crossref] [PubMed]

Hurst, R. T.

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).
[Crossref] [PubMed]

Ibrahim, R.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Jacobs, C.

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.

Jang, I.-K.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Jia, Y.

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.

Jodoin, P.-M.

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).

Johnson, K.

M. Kuhn and K. Johnson, Applied Predictive Modeling (Springer, 2013).
[Crossref]

Kalelkar, M. B.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Kang, D.-H.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Kauffman, C. R.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Kendall, C. B.

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).
[Crossref] [PubMed]

Koljenovic, S.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

Krizhevsky, A.

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.

Kuhn, M.

M. Kuhn and K. Johnson, Applied Predictive Modeling (Springer, 2013).
[Crossref]

Lam, K. L.

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).
[Crossref] [PubMed]

Lamouche, G.

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).
[Crossref]

Lapierre, C.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Larochelle, H.

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).

Leach, M. O.

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).
[Crossref]

LeCun, Y.

D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun, “Understanding deep architectures using a recursive convolutional network,” arXiv preprint arXiv:1312.1847 (2013).

Lee, D. C.

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).

Levison, M. E.

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, and 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).
[Crossref] [PubMed]

Levitz, D.

Leye, M.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Li, S.

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).
[Crossref]

Liang, J.

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).
[Crossref] [PubMed]

Ligthart, J.

E. Regar, J. Ligthart, N. Bruining, and G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
[Crossref] [PubMed]

Lin, C.-J.

C.-C. Chang and C.-J. Lin, “Libsvm: a library for support vector machines,” ACM Trans. Intelligent Systems and Technology (TIST) 2, 27 (2011).

Lin, J.-S.

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.

Liu, W.

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.

Lo, S.-C. B.

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).
[Crossref] [PubMed]

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.

Lopez, M. A. G.

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.

Lu, L.

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, and 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).

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.

Maki, A.

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.

Manouzi, A.

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).

Margeta, J.

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).

Mari, J. M.

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).
[Crossref] [PubMed]

Marks, D. L.

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).
[Crossref] [PubMed]

Melo, L. A.

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).
[Crossref]

Mierau, G. W.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Mollura, D. J.

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, and 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).

Mun, S. K.

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.

Nascimento, J.

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.

Newburger, J. W.

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, and 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).
[Crossref] [PubMed]

Nguyen, F. T.

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).
[Crossref] [PubMed]

Ni, D.

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).
[Crossref]

Nijjer, S.

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).
[Crossref] [PubMed]

Okamura, T.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

Oldenburg, A. L.

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).
[Crossref] [PubMed]

Oliveira, J. L.

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.

Oosterhuis, J. W.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

Orenstein, J. M.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Orton, M. R.

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).
[Crossref]

Oudkerk, M.

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).
[Crossref] [PubMed]

Pal, C.

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).

Pallasch, T. J.

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, and 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).
[Crossref] [PubMed]

Papadakis, G. Z.

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, and 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).

Pazos, V.

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).
[Crossref]

Perlman, E. J.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Petraco, R.

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).
[Crossref] [PubMed]

Petrick, N.

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).
[Crossref] [PubMed]

Potts, J. E.

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).

Prasad, A. S.

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.

Preim, B.

B. Preim and D. Bartz, Visualization in Medicine: Theory, Algorithms, and Applications (Morgan Kaufmann, 2007).

Presbitero, P.

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).
[Crossref]

Prokop, M.

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).
[Crossref] [PubMed]

Qin, J.

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).
[Crossref]

Rabinovich, A.

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.

Ramos-Pollán, R.

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.

Reed, S.

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.

Regar, E.

E. Regar, J. Ligthart, N. Bruining, and G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
[Crossref] [PubMed]

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

Rolfe, J.

D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun, “Understanding deep architectures using a recursive convolutional network,” arXiv preprint arXiv:1312.1847 (2013).

Rollins, A. M.

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).

Roth, H.

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, and 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).

Roth, H. R.

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.

Rotta, A. T.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Rowley, A. H.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Russo, P. A.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Sahiner, B.

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).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

Schlendorf, K. H.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Schmidhuber, J.

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

Schmitt, J. M.

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] [PubMed]

Scholten, E. T.

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).
[Crossref] [PubMed]

Selly, J.-B.

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

Sen, S.

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).
[Crossref] [PubMed]

Sermanet, P.

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.

Serruys, P. W.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

Setio, A. A.

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.

Sharif Razavian, A.

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.

Shin, H.-C.

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).
[Crossref]

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, and 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).

Shin, J. Y.

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).
[Crossref] [PubMed]

Shishkov, M.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Shotton, J.

A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis (Springer Science & Business Media, 2013).
[Crossref]

Shulman, S. T.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

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, and 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).
[Crossref] [PubMed]

Simon, D. I.

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).

Simonyan, K.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

Sinnaeve, P.

Skaf, M.

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).
[Crossref]

Sullivan, J.

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.

Summers, R. M.

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.

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, and 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).

Sutskever, I.

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.

Swartling, J.

Szegedy, C.

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.

Tajbakhsh, N.

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).
[Crossref] [PubMed]

Takahashi, M.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

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, and 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).
[Crossref] [PubMed]

Tani, L. Y.

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, and 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).
[Crossref] [PubMed]

Taubert, K. A.

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, and 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).
[Crossref] [PubMed]

Tearney, G. J.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Thrane, L.

Trevenen, C.

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, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Turkbey, E. B.

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.

Ughi, G. J.

Valanciunaite, J.

Van der Steen, A. F. W

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

van Ginneken, B.

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).
[Crossref] [PubMed]

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.

van Leenders, G. L.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

van Noorden, S.

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

van Riel, S. J.

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).
[Crossref] [PubMed]

van Soest, G.

E. Regar, J. Ligthart, N. Bruining, and G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
[Crossref] [PubMed]

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

Vanhoucke, V.

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.

Vergnole, S.

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).
[Crossref]

Virmani, R.

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] [PubMed]

Wang, T.

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).
[Crossref]

Wang, Z.

Z. Wang and X. Xue, “Multi-class support vector machine,” in Support Vector Machines Applications (Springer, 2014), pp. 23–48.
[Crossref]

Warde-Farley, D.

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).

Wei, D.

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).
[Crossref] [PubMed]

Whitbourn, R.

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).
[Crossref]

Wiesel, T. N.

D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” J. Physiol. 148, 574–591 (1959).
[Crossref] [PubMed]

Wilson, W. R.

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, and 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).
[Crossref] [PubMed]

Wojtkowski, M.

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).
[Crossref]

Wolf, L.

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.

Wu, A.

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, and 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).

Xu, C.

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] [PubMed]

Xu, Z.

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, and 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).

Xue, X.

Z. Wang and X. Xue, “Multi-class support vector machine,” in Support Vector Machines Applications (Springer, 2014), pp. 23–48.
[Crossref]

Yabushita, H.

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

Yang, X.

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).
[Crossref]

Zeiler, M. D.

M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European Conference on Computer Vision (Springer, 2014), pp. 818–833.

Zisserman, A.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

Zysk, A. M.

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).
[Crossref] [PubMed]

ACM Trans. Intelligent Systems and Technology (TIST) (1)

C.-C. Chang and C.-J. Lin, “Libsvm: a library for support vector machines,” ACM Trans. Intelligent Systems and Technology (TIST) 2, 27 (2011).

Biomed. Opt. express (1)

Cardiovascular Revascularization Medicine (1)

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).
[Crossref] [PubMed]

Circulation (2)

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, and G. J. Tearney, “Characterization of human atherosclerosis by optical coherence tomography,” Circulation 106, 1640–1645 (2002).
[Crossref] [PubMed]

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, and 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).
[Crossref] [PubMed]

Circulation: Cardiovascular Imaging (1)

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).

Herz. (1)

E. Regar, J. Ligthart, N. Bruining, and G. van Soest, “The diagnostic value of intracoronary optical coherence tomography,” Herz. 36, 417–429 (2011).
[Crossref] [PubMed]

IEEE J. Biomed. Health Informatics (1)

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).
[Crossref]

IEEE Trans. Med. Imaging (1)

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).
[Crossref] [PubMed]

IEEE Trans. Medical Imaging (1)

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).
[Crossref] [PubMed]

IEEE Trans. Pattern Analysis and Machine Intelligence (1)

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).
[Crossref]

Int. J. Cardiol. (1)

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).
[Crossref]

J. Am. Heart Assoc. (1)

A. Dionne, R. Ibrahim, C. Gebhard, M. Bakloul, J.-B. Selly, M. Leye, J. Déry, C. Lapierre, P. Girard, A. Fournier, and 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).
[Crossref] [PubMed]

J. Biomed. Opt. (4)

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).
[Crossref] [PubMed]

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] [PubMed]

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, and A. F. W Van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15, 011105 (2010).
[Crossref] [PubMed]

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).
[Crossref]

J. Physiol. (1)

D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” J. Physiol. 148, 574–591 (1959).
[Crossref] [PubMed]

JACC: Cardiovascular Interventions (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: Cardiovascular Interventions 2, 1035–1046 (2009).

Med. Image Anal. (2)

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).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

Med. Phys. (1)

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).
[Crossref] [PubMed]

Neural Computation (1)

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

Opt. Express (1)

PloS one (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, C. Trevenen, A. T. Rotta, M. B. Kalelkar, and A. H. Rowley, “Three linked vasculopathic processes characterize kawasaki disease: a light and transmission electron microscopic study,” PloS one 7, e38998 (2012).
[Crossref] [PubMed]

Prog. Retinal Eye Res. (1)

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).
[Crossref]

Other (20)

B. Preim and D. Bartz, Visualization in Medicine: Theory, Algorithms, and Applications (Morgan Kaufmann, 2007).

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.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European Conference on Computer Vision (Springer, 2014), pp. 818–833.

D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun, “Understanding deep architectures using a recursive convolutional network,” arXiv preprint arXiv:1312.1847 (2013).

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.

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.

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.

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.

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.

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.

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).

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.

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.

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, and 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).

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).

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.

A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis (Springer Science & Business Media, 2013).
[Crossref]

M. Kuhn and K. Johnson, Applied Predictive Modeling (Springer, 2013).
[Crossref]

Z. Wang and X. Xue, “Multi-class support vector machine,” in Support Vector Machines Applications (Springer, 2014), pp. 23–48.
[Crossref]

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (8)

Fig. 1
Fig. 1 Flowchart of the tissue classification algorithm. The process of training, feature extraction, and classification using pre-trained CNN just as feature generator is shown in step 1 and fine-tuning the network to use it as the classifier as well as feature extractor to train Random Forest and SVM is demonstrated in step 2. Step 3 show our final decision to select the optimal classification algorithm based on measured classification accuracy, sensitivity, and specificity at each step of the work and for each classifier.
Fig. 2
Fig. 2 Pre-processing steps in order from left to right: original image, converting to planar representation, and extracting the region of interest by removing all the background.
Fig. 3
Fig. 3 Peak detection and image quantization. Red circles show the peaks in the image profile; yellow, blue, and green are used to display intima, intima-media, and media borders, respectively.
Fig. 4
Fig. 4 Initial segmentation of one frame for four different patients. From left to right: Planar representation of the original image, manual segmentation, initial segmentation. Yellow, blue, and green dots show intima, intima-media, and media borders, respectively.
Fig. 5
Fig. 5 Tissue classification accuracy for all 26 sequences of intravascular OCT images at each step of fine-tuning the network from fc8 to the first convolutional layer to find the optimal depth of fine-tuning.
Fig. 6
Fig. 6 Performance of CNN, Random Forest, and SVM based on classification accuracy for each patient. Fine-tuning is performed from fc8 to the third convolutional layer for CNN. Features are extracted from fc7 (the last fully connected layer just before the classification layer) of the pre-trained and fine-tuned network to train Random Forest and SVM.
Fig. 7
Fig. 7 Performance of Random Forest, and SVM based on classification accuracy for each patient. CNN is used as feature extractor for our dataset. Features are extracted from fc7 (the last fully connected layer just before the classification layer) of the pre-trained network to train Random Forest and SVM. The performance of RF and SVM compared against the best performance of the CNN as the classifier in our experiments when the network is fine-tuned from fc8 to the third convolutional layer.
Fig. 8
Fig. 8 Classification results for one frame of five different patients. From left to right for each patient: original image converted to planar representation, initial segmentation, intima (red), and media (green)

Tables (7)

Tables Icon

Table 1 AlexNet architecture consists of five convolutional layers, and three fully connected layers.

Tables Icon

Table 2 Learning rates at each step of fine-tuning the AlexNet model in our experiments. μ and γ are fixed at 0.9 and 0.95 respectively at all the steps of fine-tuning. We started fine-tuning from the last fully connected layer by setting the learning rate to 0.1 for this layer and zero for other layers. We continue changing the network slightly. We started decreasing the learning rates during fine-tuning from fc6. So, the weights of the last layers which are more dataset specific change faster than the rest of the network.

Tables Icon

Table 3 Measured values of accuracy, sensitivity, and specificity to find the optimal depth of fine-tuning based on the performance of the network to classify intima and media at each step of fine-tuning. Values are reported as means ± standard deviation for 26 sequences.

Tables Icon

Table 4 Measured values of accuracy, sensitivity, and specificity to evaluate the performance of CNN, Random Forest, and SVM to classify intima and media. Values are reported as mean ± standard deviation for 26 sequences. In this experiment, fine-tuning is performed from fc8 to the third convolutional layer for CNN. Features are extracted from fc7 (the last fully connected layer just before the classification layer) to train Random Forest and SVM.

Tables Icon

Table 5 Measured values of accuracy, sensitivity, and specificity. Values are reported as mean ± standard deviation for 26 sequences. In this experiment, CNN is used as feature extractor for our dataset. Features are extracted from fc7 (the last fully connected layer just before the classification layer) to train Random Forest and SVM. The performances of Random Forest and SVM are compared against the best performance of the CNN as classifier in our experiments when the network is fine-tuned from fc8 to the third convolutional layer.

Tables Icon

Table 6 Measured values of accuracy, sensitivity, and specificity to evaluate the performance of CNN, Random Forest, and SVM to classify intima and media for the next step of the work when our algorithm is trained on different patients. In this experiment, fine-tuning is performed from fc8 to the third convolutional layer for CNN. Features are extracted from fc7 (the last fully connected layer just before the classification layer) of the fine-tuned network to train Random Forest and SVM.

Tables Icon

Table 7 Measured values of accuracy, sensitivity, and specificity for the next step of the work when our algorithm is trained on different patients. In this experiment, CNN is used as feature extractor for our dataset. Features are extracted from fc7 (the last fully connected layer just before the classification layer) to train Random Forest and SVM. The performances of Random Forest and SVM are compared against the best performance of the CNN as the classifier in our experiments when the network is fine-tuned from fc8 to the third convolutional layer.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

L = ( 1 / | X | ) j | X | ln ( p ( y j | X j | ) )
V i + 1 = μ V i γ i α L / W
W i + 1 = W i + V i + 1

Metrics