Abstract

Detection of endolymphatic hydrops is important for diagnosing Meniere’s disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

© 2017 Optical Society of America

Full Article  |  PDF Article
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  1. R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
    [Crossref] [PubMed]
  2. K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).
  3. S. Ö. Arık, B. Ibragimov, and L. Xing, “Fully automated quantitative cephalometry using convolutional neural networks,” J. Med. Imaging (Bellingham) 4(1), 014501 (2017).
    [Crossref] [PubMed]
  4. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
    [Crossref] [PubMed]
  5. M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
    [Crossref] [PubMed]
  6. K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).
  7. F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
    [Crossref] [PubMed]
  8. G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” Br. J. Ophthalmol. 80(11), 940–944 (1996).
    [Crossref] [PubMed]
  9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).
  10. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).
  11. 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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.
  12. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. 770–778.
    [Crossref]
  13. N. H. Cho, J. H. Jang, W. Jung, and J. Kim, “In vivo imaging of middle-ear and inner-ear microstructures of a mouse guided by SD-OCT combined with a surgical microscope,” Opt. Express 22(8), 8985–8995 (2014).
    [Crossref] [PubMed]
  14. H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
    [Crossref] [PubMed]
  15. H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
    [Crossref] [PubMed]
  16. H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
    [Crossref] [PubMed]
  17. A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
    [Crossref] [PubMed]
  18. S. S. Gao, A. Xia, T. Yuan, P. D. Raphael, R. L. Shelton, B. E. Applegate, and J. S. Oghalai, “Quantitative imaging of cochlear soft tissues in wild-type and hearing-impaired transgenic mice by spectral domain optical coherence tomography,” Opt. Express 19(16), 15415–15428 (2011).
    [Crossref] [PubMed]
  19. S. D. Rauch, S. N. Merchant, and B. A. Thedinger, “Meniere’s syndrome and endolymphatic hydrops. double-blind temporal bone study,” Ann. Otol. Rhinol. Laryngol. 98(11), 873–883 (1989).
    [Crossref] [PubMed]
  20. A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
    [Crossref] [PubMed]
  21. J. Kim, X. Liu, Z. Jawadi, N. Grillet, and J. Oghalai, “Acute changes in the mouse cochlea after blast injury.,” in Abstracts of the Midwinter Research Meeting of the Association for Research in Otolaryngology 2016. (2016).
  22. F. Fiorino, F. B. Pizzini, A. Beltramello, and F. Barbieri, “MRI performed after intratympanic gadolinium administration in patients with Ménière’s disease: Correlation with symptoms and signs,” Eur. Arch. Otorhinolaryngol. 268(2), 181–187 (2011).
    [Crossref] [PubMed]
  23. H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
    [Crossref] [PubMed]
  24. H. F. Schuknecht, Pathology of the Ear, 2nd ed. (Lea & Febiger, 1993).
  25. S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
    [Crossref] [PubMed]
  26. A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
    [Crossref] [PubMed]
  27. S. F. Klis, J. Buijs, and G. F. Smoorenburg, “Quantification of the relation between electrophysiologic and morphologic changes in experimental endolymphatic hydrops,” Ann. Otol. Rhinol. Laryngol. 99(7), 566–570 (1990).
    [Crossref] [PubMed]
  28. P. Dollar, Z. Tu, H. Tao, and S. Belongie, “Feature mining for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007).
  29. G. S. Liu, J. Kim, B. E. Applegate, and J. S. Oghalai, “Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography,” J. Biomed. Opt. 22(7), 076002 (2017).
    [Crossref] [PubMed]
  30. L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
    [Crossref] [PubMed]
  31. F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
    [Crossref] [PubMed]
  32. S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
    [Crossref] [PubMed]
  33. S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
    [Crossref] [PubMed]
  34. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
    [Crossref]
  35. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Int. Conf. Learn. Represent. 1–14 (2015).
  36. A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
    [Crossref] [PubMed]
  37. D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Int. Conf. Learn. Represent. 1–13 (2014).
  38. Theano Development Team, “Theano: A Python framework for fast computation of mathematical expressions,” arXiv e-prints abs/1605.0, (2016).
  39. F. Chollet, “Keras,” https://github.com/fchollet/keras .
  40. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
  41. M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks BT - Computer Vision – ECCV 2014,” in Computer Vision – ECCV 2014 (2014), Vol. 8689, pp. 818–833.
  42. R. Kotikalapudi, “Keras-vis,” https://github.com/raghakot/keras-vis .
  43. 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(5), 1299–1312 (2016).
    [Crossref] [PubMed]

2017 (8)

S. Ö. Arık, B. Ibragimov, and L. Xing, “Fully automated quantitative cephalometry using convolutional neural networks,” J. Med. Imaging (Bellingham) 4(1), 014501 (2017).
[Crossref] [PubMed]

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

G. S. Liu, J. Kim, B. E. Applegate, and J. S. Oghalai, “Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography,” J. Biomed. Opt. 22(7), 076002 (2017).
[Crossref] [PubMed]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
[Crossref] [PubMed]

S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
[Crossref] [PubMed]

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

2016 (6)

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(5), 1299–1312 (2016).
[Crossref] [PubMed]

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
[Crossref] [PubMed]

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
[Crossref] [PubMed]

2015 (2)

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

2014 (2)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

N. H. Cho, J. H. Jang, W. Jung, and J. Kim, “In vivo imaging of middle-ear and inner-ear microstructures of a mouse guided by SD-OCT combined with a surgical microscope,” Opt. Express 22(8), 8985–8995 (2014).
[Crossref] [PubMed]

2013 (2)

S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
[Crossref] [PubMed]

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

2012 (2)

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

2011 (2)

S. S. Gao, A. Xia, T. Yuan, P. D. Raphael, R. L. Shelton, B. E. Applegate, and J. S. Oghalai, “Quantitative imaging of cochlear soft tissues in wild-type and hearing-impaired transgenic mice by spectral domain optical coherence tomography,” Opt. Express 19(16), 15415–15428 (2011).
[Crossref] [PubMed]

F. Fiorino, F. B. Pizzini, A. Beltramello, and F. Barbieri, “MRI performed after intratympanic gadolinium administration in patients with Ménière’s disease: Correlation with symptoms and signs,” Eur. Arch. Otorhinolaryngol. 268(2), 181–187 (2011).
[Crossref] [PubMed]

2010 (3)

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

1996 (1)

G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” Br. J. Ophthalmol. 80(11), 940–944 (1996).
[Crossref] [PubMed]

1990 (1)

S. F. Klis, J. Buijs, and G. F. Smoorenburg, “Quantification of the relation between electrophysiologic and morphologic changes in experimental endolymphatic hydrops,” Ann. Otol. Rhinol. Laryngol. 99(7), 566–570 (1990).
[Crossref] [PubMed]

1989 (1)

S. D. Rauch, S. N. Merchant, and B. A. Thedinger, “Meniere’s syndrome and endolymphatic hydrops. double-blind temporal bone study,” Ann. Otol. Rhinol. Laryngol. 98(11), 873–883 (1989).
[Crossref] [PubMed]

Abaya, H.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

Anthimopoulos, M.

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
[Crossref] [PubMed]

Applegate, B. E.

G. S. Liu, J. Kim, B. E. Applegate, and J. S. Oghalai, “Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography,” J. Biomed. Opt. 22(7), 076002 (2017).
[Crossref] [PubMed]

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
[Crossref] [PubMed]

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
[Crossref] [PubMed]

S. S. Gao, A. Xia, T. Yuan, P. D. Raphael, R. L. Shelton, B. E. Applegate, and J. S. Oghalai, “Quantitative imaging of cochlear soft tissues in wild-type and hearing-impaired transgenic mice by spectral domain optical coherence tomography,” Opt. Express 19(16), 15415–15428 (2011).
[Crossref] [PubMed]

Arik, S. Ö.

S. Ö. Arık, B. Ibragimov, and L. Xing, “Fully automated quantitative cephalometry using convolutional neural networks,” J. Med. Imaging (Bellingham) 4(1), 014501 (2017).
[Crossref] [PubMed]

Baek, J.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

Barbieri, F.

F. Fiorino, F. B. Pizzini, A. Beltramello, and F. Barbieri, “MRI performed after intratympanic gadolinium administration in patients with Ménière’s disease: Correlation with symptoms and signs,” Eur. Arch. Otorhinolaryngol. 268(2), 181–187 (2011).
[Crossref] [PubMed]

Baumgartner, C.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Belongie, S.

P. Dollar, Z. Tu, H. Tao, and S. Belongie, “Feature mining for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007).

Beltramello, A.

F. Fiorino, F. B. Pizzini, A. Beltramello, and F. Barbieri, “MRI performed after intratympanic gadolinium administration in patients with Ménière’s disease: Correlation with symptoms and signs,” Eur. Arch. Otorhinolaryngol. 268(2), 181–187 (2011).
[Crossref] [PubMed]

Berg, A. C.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Bernstein, M.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Betriu, A.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Blau, H. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Boccalini, S.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Budde, R. P. J.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Bueno, A.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Buijs, J.

S. F. Klis, J. Buijs, and G. F. Smoorenburg, “Quantification of the relation between electrophysiologic and morphologic changes in experimental endolymphatic hydrops,” Ann. Otol. Rhinol. Laryngol. 99(7), 566–570 (1990).
[Crossref] [PubMed]

Cardinale, M.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Chakraborty, D.

Chatterjee, J.

Chelu, R. G.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Cho, N. H.

Cho, S. I.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

Christe, A.

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
[Crossref] [PubMed]

Christodoulidis, S.

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
[Crossref] [PubMed]

Coenen, A.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Criminisi, A.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Cunefare, D.

Davila, V.

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

de Mathelin, M.

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

del M. Vila, M.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Del Nido, P.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Deng, J.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Dollar, P.

P. Dollar, Z. Tu, H. Tao, and S. Belongie, “Feature mining for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007).

Ebner, L.

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
[Crossref] [PubMed]

Eigen, D.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).

Ellerbee, A. K.

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

Ellerbee Bowden, A. K.

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

Elliott, A. T.

G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” Br. J. Ophthalmol. 80(11), 940–944 (1996).
[Crossref] [PubMed]

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

Esteva, A.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

Fauser, S.

Fei-Fei, L.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Fergus, R.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).

Fernandez, E.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Fiorino, F.

F. Fiorino, F. B. Pizzini, A. Beltramello, and F. Barbieri, “MRI performed after intratympanic gadolinium administration in patients with Ménière’s disease: Correlation with symptoms and signs,” Eur. Arch. Otorhinolaryngol. 268(2), 181–187 (2011).
[Crossref] [PubMed]

Fukuoka, H.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Galimzianova, A.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Gao, S.

Gao, S. S.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

S. S. Gao, A. Xia, T. Yuan, P. D. Raphael, R. L. Shelton, B. E. Applegate, and J. S. Oghalai, “Quantitative imaging of cochlear soft tissues in wild-type and hearing-impaired transgenic mice by spectral domain optical coherence tomography,” Opt. Express 19(16), 15415–15428 (2011).
[Crossref] [PubMed]

Gardner, G. G.

G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” Br. J. Ophthalmol. 80(11), 940–944 (1996).
[Crossref] [PubMed]

Glocker, B.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Grillet, N.

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

J. Kim, X. Liu, Z. Jawadi, N. Grillet, and J. Oghalai, “Acute changes in the mouse cochlea after blast injury.,” in Abstracts of the Midwinter Research Meeting of the Association for Research in Otolaryngology 2016. (2016).

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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Guymer, R. H.

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. 770–778.
[Crossref]

Hinton, G.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Hoyng, C.

Hsiao, A.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Huang, Z.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Ibragimov, B.

S. Ö. Arık, B. Ibragimov, and L. Xing, “Fully automated quantitative cephalometry using convolutional neural networks,” J. Med. Imaging (Bellingham) 4(1), 014501 (2017).
[Crossref] [PubMed]

Igual, L.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Jacobs, D.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

Jang, J. H.

Jawadi, Z.

J. Kim, X. Liu, Z. Jawadi, N. Grillet, and J. Oghalai, “Acute changes in the mouse cochlea after blast injury.,” in Abstracts of the Midwinter Research Meeting of the Association for Research in Otolaryngology 2016. (2016).

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

Jung, W.

Kadoya, M.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Kamnitsas, K.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Kane, A. D.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Karpathy, A.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Karri, S. P. K.

Keating, D.

G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” Br. J. Ophthalmol. 80(11), 940–944 (1996).
[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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Khosla, A.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Kim, J.

G. S. Liu, J. Kim, B. E. Applegate, and J. S. Oghalai, “Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography,” J. Biomed. Opt. 22(7), 076002 (2017).
[Crossref] [PubMed]

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

N. H. Cho, J. H. Jang, W. Jung, and J. Kim, “In vivo imaging of middle-ear and inner-ear microstructures of a mouse guided by SD-OCT combined with a surgical microscope,” Opt. Express 22(8), 8985–8995 (2014).
[Crossref] [PubMed]

J. Kim, X. Liu, Z. Jawadi, N. Grillet, and J. Oghalai, “Acute changes in the mouse cochlea after blast injury.,” in Abstracts of the Midwinter Research Meeting of the Association for Research in Otolaryngology 2016. (2016).

Klis, S. F.

S. F. Klis, J. Buijs, and G. F. Smoorenburg, “Quantification of the relation between electrophysiologic and morphologic changes in experimental endolymphatic hydrops,” Ann. Otol. Rhinol. Laryngol. 99(7), 566–570 (1990).
[Crossref] [PubMed]

Ko, J.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Krause, J.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Krestin, G. P.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Kuprel, B.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

LeCun, Y.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).

Ledig, C.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Lee, H. Y.

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

Lekadir, K.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Li, S.

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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Liefers, B.

Liu, G. S.

G. S. Liu, J. Kim, B. E. Applegate, and J. S. Oghalai, “Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography,” J. Biomed. Opt. 22(7), 076002 (2017).
[Crossref] [PubMed]

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

Liu, X.

A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
[Crossref] [PubMed]

J. Kim, X. Liu, Z. Jawadi, N. Grillet, and J. Oghalai, “Acute changes in the mouse cochlea after blast injury.,” in Abstracts of the Midwinter Research Meeting of the Association for Research in Otolaryngology 2016. (2016).

Ma, S.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Marescaux, J.

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

Marx, G.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Mathieu, M.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).

Menon, D. K.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Merchant, S. N.

S. D. Rauch, S. N. Merchant, and B. A. Thedinger, “Meniere’s syndrome and endolymphatic hydrops. double-blind temporal bone study,” Ann. Otol. Rhinol. Laryngol. 98(11), 873–883 (1989).
[Crossref] [PubMed]

Miyagawa, M.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Mougiakakou, S.

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
[Crossref] [PubMed]

Mutter, D.

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

Napel, S.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Newcombe, V. F. J.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Nguyen-Huynh, A. T.

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

Nieman, K.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Nori, A.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Novoa, R. A.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Nuttall, A. L.

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

Oghalai, J.

J. Kim, X. Liu, Z. Jawadi, N. Grillet, and J. Oghalai, “Acute changes in the mouse cochlea after blast injury.,” in Abstracts of the Midwinter Research Meeting of the Association for Research in Otolaryngology 2016. (2016).

Oghalai, J. S.

G. S. Liu, J. Kim, B. E. Applegate, and J. S. Oghalai, “Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography,” J. Biomed. Opt. 22(7), 076002 (2017).
[Crossref] [PubMed]

A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
[Crossref] [PubMed]

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
[Crossref] [PubMed]

S. S. Gao, A. Xia, T. Yuan, P. D. Raphael, R. L. Shelton, B. E. Applegate, and J. S. Oghalai, “Quantitative imaging of cochlear soft tissues in wild-type and hearing-impaired transgenic mice by spectral domain optical coherence tomography,” Opt. Express 19(16), 15415–15428 (2011).
[Crossref] [PubMed]

Oguchi, T.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Padoy, N.

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

Park, J.

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
[Crossref] [PubMed]

Pereira, F.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Perrin, D.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Pizzini, F. B.

F. Fiorino, F. B. Pizzini, A. Beltramello, and F. Barbieri, “MRI performed after intratympanic gadolinium administration in patients with Ménière’s disease: Correlation with symptoms and signs,” Eur. Arch. Otorhinolaryngol. 268(2), 181–187 (2011).
[Crossref] [PubMed]

Plontke, S. K.

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

Radeva, P.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Raphael, P. D.

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
[Crossref] [PubMed]

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
[Crossref] [PubMed]

S. S. Gao, A. Xia, T. Yuan, P. D. Raphael, R. L. Shelton, B. E. Applegate, and J. S. Oghalai, “Quantitative imaging of cochlear soft tissues in wild-type and hearing-impaired transgenic mice by spectral domain optical coherence tomography,” Opt. Express 19(16), 15415–15428 (2011).
[Crossref] [PubMed]

Rasband, M. N.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

Rauch, S. D.

S. D. Rauch, S. N. Merchant, and B. A. Thedinger, “Meniere’s syndrome and endolymphatic hydrops. double-blind temporal bone study,” Ann. Otol. Rhinol. Laryngol. 98(11), 873–883 (1989).
[Crossref] [PubMed]

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. 770–778.
[Crossref]

Rodriguez, A.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Roos-Hesselink, J. W.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Rubin, D.

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

Rueckert, D.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Russakovsky, O.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Salgo, I.

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

Salles, F. T.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

Salt, A. N.

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

Sánchez, C. I.

Satheesh, S.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).

Shehata, S.

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

Shelton, R. L.

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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Simpson, J. P.

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

Smoorenburg, G. F.

S. F. Klis, J. Buijs, and G. F. Smoorenburg, “Quantification of the relation between electrophysiologic and morphologic changes in experimental endolymphatic hydrops,” Ann. Otol. Rhinol. Laryngol. 99(7), 566–570 (1990).
[Crossref] [PubMed]

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Su, H.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

Subhash, H. M.

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

Sun, H.

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. 770–778.
[Crossref]

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Swart, L. E.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Swetter, S. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, 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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Takumi, Y.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Tao, H.

P. Dollar, Z. Tu, H. Tao, and S. Belongie, “Feature mining for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007).

Thedinger, B. A.

S. D. Rauch, S. N. Merchant, and B. A. Thedinger, “Meniere’s syndrome and endolymphatic hydrops. double-blind temporal bone study,” Ann. Otol. Rhinol. Laryngol. 98(11), 873–883 (1989).
[Crossref] [PubMed]

Theelen, T.

Thrun, S.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Tsukada, K.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Tu, Z.

P. Dollar, Z. Tu, H. Tao, and S. Belongie, “Feature mining for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007).

Twinanda, A. P.

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

Ueda, H.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Usami, S.

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

van den Hoven, A. T.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

van Ginneken, B.

van Grinsven, M. J. J. P.

van Kranenburg, M.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

Vasanawala, S. S.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Venhuizen, F. G.

Vogel, M. W.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Voogd, T.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Wachtel, J.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

Wanambiro, K. W.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Wang, C.

Wang, R.

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
[Crossref] [PubMed]

Wang, R. K.

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

Wielopolski, P. A.

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

Williamson, T. H.

G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” Br. J. Ophthalmol. 80(11), 940–944 (1996).
[Crossref] [PubMed]

Xia, A.

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
[Crossref] [PubMed]

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

S. Gao, P. D. Raphael, R. Wang, J. Park, A. Xia, B. E. Applegate, and J. S. Oghalai, “In vivo vibrometry inside the apex of the mouse cochlea using spectral domain optical coherence tomography,” Biomed. Opt. Express 4(2), 230–240 (2013).
[Crossref] [PubMed]

S. S. Gao, A. Xia, T. Yuan, P. D. Raphael, R. L. Shelton, B. E. Applegate, and J. S. Oghalai, “Quantitative imaging of cochlear soft tissues in wild-type and hearing-impaired transgenic mice by spectral domain optical coherence tomography,” Opt. Express 19(16), 15415–15428 (2011).
[Crossref] [PubMed]

Xing, L.

S. Ö. Arık, B. Ibragimov, and L. Xing, “Fully automated quantitative cephalometry using convolutional neural networks,” J. Med. Imaging (Bellingham) 4(1), 014501 (2017).
[Crossref] [PubMed]

Yuan, T.

Zhang, X.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. 770–778.
[Crossref]

Acta Otolaryngol. (1)

H. Fukuoka, Y. Takumi, K. Tsukada, M. Miyagawa, T. Oguchi, H. Ueda, M. Kadoya, and S. Usami, “Comparison of the diagnostic value of 3 T MRI after intratympanic injection of GBCA, electrocochleography, and the glycerol test in patients with Meniere’s disease,” Acta Otolaryngol. 132(2), 141–145 (2012).
[Crossref] [PubMed]

Adv. Neural Inf. Process. Syst. (1)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst. 2012, 1097–1105 (2012).

Ann. Otol. Rhinol. Laryngol. (2)

S. D. Rauch, S. N. Merchant, and B. A. Thedinger, “Meniere’s syndrome and endolymphatic hydrops. double-blind temporal bone study,” Ann. Otol. Rhinol. Laryngol. 98(11), 873–883 (1989).
[Crossref] [PubMed]

S. F. Klis, J. Buijs, and G. F. Smoorenburg, “Quantification of the relation between electrophysiologic and morphologic changes in experimental endolymphatic hydrops,” Ann. Otol. Rhinol. Laryngol. 99(7), 566–570 (1990).
[Crossref] [PubMed]

Biomed. Opt. Express (4)

Br. J. Ophthalmol. (1)

G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool,” Br. J. Ophthalmol. 80(11), 940–944 (1996).
[Crossref] [PubMed]

Eur. Arch. Otorhinolaryngol. (1)

F. Fiorino, F. B. Pizzini, A. Beltramello, and F. Barbieri, “MRI performed after intratympanic gadolinium administration in patients with Ménière’s disease: Correlation with symptoms and signs,” Eur. Arch. Otorhinolaryngol. 268(2), 181–187 (2011).
[Crossref] [PubMed]

Eur. J. Radiol. (1)

R. G. Chelu, K. W. Wanambiro, A. Hsiao, L. E. Swart, T. Voogd, A. T. van den Hoven, M. van Kranenburg, A. Coenen, S. Boccalini, P. A. Wielopolski, M. W. Vogel, G. P. Krestin, S. S. Vasanawala, R. P. J. Budde, J. W. Roos-Hesselink, and K. Nieman, “Cloud-processed 4D CMR flow imaging for pulmonary flow quantification,” Eur. J. Radiol. 85(10), 1849–1856 (2016).
[Crossref] [PubMed]

IEEE J. Biomed. Health Inform. (1)

K. Lekadir, A. Galimzianova, A. Betriu, M. del M. Vila, L. Igual, D. Rubin, E. Fernandez, P. Radeva, and S. Napel, “A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,” IEEE J. Biomed. Health Inform. 2194, 48–55 (2016).

IEEE Trans. Med. Imaging (3)

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016).
[Crossref] [PubMed]

A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin, and N. Padoy, “EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos,” IEEE Trans. Med. Imaging 36(1), 86–97 (2017).
[Crossref] [PubMed]

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(5), 1299–1312 (2016).
[Crossref] [PubMed]

Int. J. Comput. Vis. (1)

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis. 115(3), 211–252 (2015).
[Crossref]

J. Biomed. Opt. (2)

G. S. Liu, J. Kim, B. E. Applegate, and J. S. Oghalai, “Computer-aided detection and quantification of endolymphatic hydrops within the mouse cochlea in vivo using optical coherence tomography,” J. Biomed. Opt. 22(7), 076002 (2017).
[Crossref] [PubMed]

H. M. Subhash, V. Davila, H. Sun, A. T. Nguyen-Huynh, A. L. Nuttall, and R. K. Wang, “Volumetric in vivo imaging of intracochlear microstructures in mice by high-speed spectral domain optical coherence tomography,” J. Biomed. Opt. 15(3), 036024 (2010).
[Crossref] [PubMed]

J. Mach. Learn. Res. (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

J. Med. Imaging (Bellingham) (2)

F. Pereira, A. Bueno, A. Rodriguez, D. Perrin, G. Marx, M. Cardinale, I. Salgo, and P. Del Nido, “Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms,” J. Med. Imaging (Bellingham) 4(1), 014502 (2017).
[Crossref] [PubMed]

S. Ö. Arık, B. Ibragimov, and L. Xing, “Fully automated quantitative cephalometry using convolutional neural networks,” J. Med. Imaging (Bellingham) 4(1), 014501 (2017).
[Crossref] [PubMed]

J. Neurosci. (1)

H. Y. Lee, P. D. Raphael, A. Xia, J. Kim, N. Grillet, B. E. Applegate, A. K. Ellerbee Bowden, and J. S. Oghalai, “Two-Dimensional Cochlear Micromechanics Measured In Vivo Demonstrate Radial Tuning within the Mouse Organ of Corti,” J. Neurosci. 36(31), 8160–8173 (2016).
[Crossref] [PubMed]

Nat. Commun. (1)

A. Xia, X. Liu, P. D. Raphael, B. E. Applegate, and J. S. Oghalai, “Hair cell force generation does not amplify or tune vibrations within the chicken basilar papilla,” Nat. Commun. 7, 13133 (2016).
[Crossref] [PubMed]

Nature (1)

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Opt. Express (2)

Otolaryngol. Clin. North Am. (2)

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

A. N. Salt and S. K. Plontke, “Endolymphatic Hydrops: Pathophysiology and Experimental Models,” Otolaryngol. Clin. North Am. 43(5), 971–983 (2010).
[Crossref] [PubMed]

PLoS One (1)

S. I. Cho, S. S. Gao, A. Xia, R. Wang, F. T. Salles, P. D. Raphael, H. Abaya, J. Wachtel, J. Baek, D. Jacobs, M. N. Rasband, and J. S. Oghalai, “Mechanisms of Hearing Loss after Blast Injury to the Ear,” PLoS One 8(7), e67618 (2013).
[Crossref] [PubMed]

Proc. Natl. Acad. Sci. U.S.A. (1)

H. Y. Lee, P. D. Raphael, J. Park, A. K. Ellerbee, B. E. Applegate, and J. S. Oghalai, “Noninvasive in vivo imaging reveals differences between tectorial membrane and basilar membrane traveling waves in the mouse cochlea,” Proc. Natl. Acad. Sci. U.S.A. 112(10), 3128–3133 (2015).
[Crossref] [PubMed]

Other (13)

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks,” (2013).

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 Computer Society Conference on Computer Vision and Pattern Recognition (2015), 07–12–June, pp. 1–9.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016), pp. 770–778.
[Crossref]

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert, and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,” Inf. Process. Med. Imaging (2016).

P. Dollar, Z. Tu, H. Tao, and S. Belongie, “Feature mining for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007).

J. Kim, X. Liu, Z. Jawadi, N. Grillet, and J. Oghalai, “Acute changes in the mouse cochlea after blast injury.,” in Abstracts of the Midwinter Research Meeting of the Association for Research in Otolaryngology 2016. (2016).

H. F. Schuknecht, Pathology of the Ear, 2nd ed. (Lea & Febiger, 1993).

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Int. Conf. Learn. Represent. 1–14 (2015).

D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Int. Conf. Learn. Represent. 1–13 (2014).

Theano Development Team, “Theano: A Python framework for fast computation of mathematical expressions,” arXiv e-prints abs/1605.0, (2016).

F. Chollet, “Keras,” https://github.com/fchollet/keras .

M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks BT - Computer Vision – ECCV 2014,” in Computer Vision – ECCV 2014 (2014), Vol. 8689, pp. 818–833.

R. Kotikalapudi, “Keras-vis,” https://github.com/raghakot/keras-vis .

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Figures (11)

Fig. 1
Fig. 1 (a) Schematic of the cochlea. (b) Representative OCT cross-sections of the cochlea in a healthy mouse (normal) and a blast-exposed mouse with endolymphatic hydrops (hydrops). The red arrow indicates the deformity of Reissner’s membrane, the hallmark of endolymphatic hydrops, in the blast-exposed mouse. Scale bars 100 µm. (c) Plastic-embedded section of the upper basal turn in the cochlea of a mouse (adapted from [25]). Scale bar 50 µm. RM, Reissner’s membrane; SM, scala media; SV, scala vestibuli.
Fig. 2
Fig. 2 Process of data augmentation. The original image (left) is rotated by an angle between −20 and 20 degrees, shrunk by a factor between 1 and 1.33, and cropped to a 64x64 pixel region around a randomly chosen center.
Fig. 3
Fig. 3 ELHnet architecture. (a) The layers of ELHnet are illustrated schematically. Schematic design adapted from reference [36]. (b) The first five activations of the output of each layer are visualized, given the example image on the left as the input, resulting in a prediction of no endolymphatic hydrops (green number) from the final (fully connected) output layer (fc8). The final output layer (fc8) outputs a score between 0 and 1, which can be interpreted as the model’s predicted probability that the input image shows endolymphatic hydrops. The penultimate layer (fc7) tends to output values of either 1 or −1 because of the hyperbolic tangent activation function used for that layer (Table 2), which compresses positive and negative values of large magnitude to 1 and −1, respectively.
Fig. 4
Fig. 4 Steps for classifying images using ELHnet. The uncropped, original image is shown, and a region of interest (ROI) is selected by the user applying ELHnet. The user manually clicks on the center of the desired ROI, whose size is 64x64 pixels. Next, the ROI is input to the ELHnet model for classification. The classification result is shown as a red or green box around the ROI. Green box indicates the predicted class is non-hydrops, and red box indicates the predicted class is endolymphatic hydrops. In this example, the red color of the box indicates a classification of endolymphatic hydrops.
Fig. 5
Fig. 5 Learning curves for the loss function and training and validation accuracies during training of ELHnet.
Fig. 6
Fig. 6 Validation results for ELHnet classification of the validation data set. Classification was performed using augmented images from the validation data set, i.e. zoomed, rotated, and cropped patches of 64x64 pixels from the original images of 128x128 pixels. All validation images were correctly classified except for seven augmented images of endolymphatic hydrops, which were misclassified as false negatives, as shown. A representative sample of ten true negative and ten true positive augmented images are also shown. Note: a classification result of endolymphatic hydrops was considered “positive” and a classification result of non-hydrops was considered “negative”. Therefore, “true negative” indicates a classification of non-hydrops in a control mouse, “false negative” indicates a classification of non-hydrops in a mouse with endolymphatic hydrops, etc.
Fig. 7
Fig. 7 Test results for ELHnet classification of the test data set. The test data set comprised of previously unseen OCT images, each taken in a new cochlea. Cochleae with ground truth labels of non-endolymphatic hydrops are labeled as control; cochleae with ground truth labels of endolymphatic hydrops are labeled as such. ELHnet correctly classified all 19 control images and 15 of the 18 endolymphatic hydrops images. For clarity, only the region of interest that was used for classification by ELHnet is shown. Note: a classification result of endolymphatic hydrops was considered “positive” and a classification result of non-hydrops was considered “negative”. Therefore, “true negative” indicates a classification of non-hydrops in a control mouse, “false negative” indicates a classification of non-hydrops in a mouse with endolymphatic hydrops, etc.
Fig. 8
Fig. 8 Receiver operating characteric curves for ELHnet based on its classification scores for (a) the validation data set and (b) the test data set.
Fig. 9
Fig. 9 Test results for the computer-aided detection (CAD) approach [29] classification of the test data set. This is the same test data set used to evaluate the test performance of ELHnet. The CAD approach correctly classified 10 of the 16 analyzed control images and 14 of the 15 analyzed endolymphatic hydrops images. The CAD approach was unable to analyze (and therefore classify) 3 control images and 3 hydrops images.
Fig. 10
Fig. 10 Receiver operating characteric curve for the computer-aided detection approach [29] to classifying endolymphatic hydrops in the test data set.
Fig. 11
Fig. 11 Saliency maps for (a) control and endolymphatic hydrops training images correctly classified by ELHnet, and (b) the three test images of endolymphatic hydrops misclassified by ELHnet. Saliency maps are overlaid in pseudocolor over the original images in grayscale.

Tables (4)

Tables Icon

Table 1 Number of mice and images in the training, validation, and test data sets.

Tables Icon

Table 2 ELHnet architecture.

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Table 3 Learning rates that were explored during training.

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Table 4 Validation and test performance of ELHnet.

Equations (1)

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L(W)=  1 N i=1 N [ y (i) log( y ^ (i) )+( 1 y (i) )log( 1 y ^ (i) ) ]

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