Abstract

Deep learning has dramatically improved object recognition, speech recognition, medical image analysis and many other fields. Optical coherence tomography (OCT) has become a standard of care imaging modality for ophthalmology. We asked whether deep learning could be used to segment cornea OCT images. Using a custom-built ultrahigh-resolution OCT system, we scanned 72 healthy eyes and 70 keratoconic eyes. In total, 20,160 images were labeled and used for the training in a supervised learning approach. A custom neural network architecture called CorneaNet was designed and trained. Our results show that CorneaNet is able to segment both healthy and keratoconus images with high accuracy (validation accuracy: 99.56%). Thickness maps of the three main corneal layers (epithelium, Bowman’s layer and stroma) were generated both in healthy subjects and subjects suffering from keratoconus. CorneaNet is more than 50 times faster than our previous algorithm. Our results show that deep learning algorithm scan be used for OCT image segmentation and could be applied in various clinical settings. In particular, CorneaNet could be used for early detection of keratoconus and more generally to study other diseases altering corneal morphology.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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2018 (5)

N. Pircher, F. Schwarzhans, S. Holzer, J. Lammer, D. Schmidl, A. M. Bata, R. M. Werkmeister, G. Seidel, G. Garhöfer, A. Gschließer, L. Schmetterer, and G. Schmidinger, “Distinguishing Keratoconic Eyes and Healthy Eyes Using Ultrahigh-Resolution Optical Coherence Tomography–Based Corneal Epithelium Thickness Mapping,” American Journal of Ophthalmology 189, 47–54 (2018).
[Crossref]

B. Keller, M. Draelos, G. Tang, S. Farsiu, A. N. Kuo, K. Hauser, and J. A. Izatt, “Real-time corneal segmentation and 3d needle tracking in intrasurgical OCT,” Biomedical Optics Express 9, 2716–2732 (2018).
[Crossref] [PubMed]

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head,” Investigative Ophthalmology & Visual Science 59, 63–74 (2018).
[Crossref]

F. G. Venhuizen, B. V. Ginneken, B. Liefers, F. V. Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomedical Optics Express 9, 1545–1569 (2018).
[Crossref] [PubMed]

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence 40, 834–848 (2018).
[Crossref]

2017 (7)

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

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images,” Ophthalmology Retina 1, 322–327 (2017).
[Crossref]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomedical Optics Express 8, 3440–3448 (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,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

T. Zhang, A. Elazab, X. Wang, F. Jia, J. Wu, G. Li, and Q. Hu, “A Novel Technique for Robust and Fast Segmentation of Corneal Layer Interfaces Based on Spectral-Domain Optical Coherence Tomography Imaging,” IEEE Access 5, 10352–10363 (2017).
[Crossref]

R. M. Werkmeister, S. Sapeta, D. Schmidl, G. Garhöfer, G. Schmidinger, V. A. D. Santos, G. C. Aschinger, I. Baumgartner, N. Pircher, F. Schwarzhans, A. Pantalon, H. Dua, and L. Schmetterer, “Ultrahigh-resolution OCT imaging of the human cornea,” Biomedical Optics Express 8, 1221–1239 (2017).
[Crossref] [PubMed]

A. J. Tatham and F. A. Medeiros, “Detecting Structural Progression in Glaucoma with Optical Coherence Tomography,” Ophthalmology 124, S57–S65 (2017).
[Crossref] [PubMed]

2016 (2)

V. Aranha dos Santos, L. Schmetterer, G. J. Triggs, R. A. Leitgeb, M. Gröschl, A. Messner, D. Schmidl, G. Garhofer, G. Aschinger, and R. M. Werkmeister, “Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT,” Biomedical Optics Express 7, 2650 (2016).
[Crossref]

D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen, and A. Elsheikh, “Reconstruction of 3d surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms,” Biomedical Signal Processing and Control 25, 91–98 (2016).
[Crossref]

2015 (3)

D. Schmidl, K. J. Witkowska, S. Kaya, C. Baar, H. Faatz, J. Nepp, A. Unterhuber, R. M. Werkmeister, G. Garhofer, and L. Schmetterer, “The Association Between Subjective and Objective Parameters for the Assessment of Dry-Eye Syndrome,” Investigative Ophthalmology & Visual Science 56, 1467–1472 (2015).
[Crossref]

V. Aranha dos Santos, L. Schmetterer, M. Gröschl, G. Garhofer, D. Schmidl, M. Kucera, A. Unterhuber, J.-P. Hermand, and R. M. Werkmeister, “In vivo tear film thickness measurement and tear film dynamics visualization using spectral domain optical coherence tomography,” Optics Express 23, 21043 (2015).
[Crossref] [PubMed]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
[Crossref] [PubMed]

2014 (2)

M. K. Jahromi, R. Kafieh, H. Rabbani, A. M. Dehnavi, A. Peyman, F. Hajizadeh, and M. Ommani, “An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model,” Journal of Medical Signals and Sensors 4, 171–180 (2014).
[PubMed]

C. W. McMonnies, “Corneal endothelial assessment with special references to keratoconus,” Optometry and Vision Science: Official Publication of the American Academy of Optometry 91, e124–e134 (2014).
[Crossref]

2013 (1)

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” Journal of Biomedical Optics 18, 056003 (2013).
[Crossref]

2012 (1)

T. Schmoll, A. Unterhuber, C. Kolbitsch, T. Le, A. Stingl, and R. Leitgeb, “Precise thickness measurements of Bowman’s layer, epithelium, and tear film,” Optometry & Vision Science 89, E795–E802 (2012).
[Crossref]

2011 (1)

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomedical Optics Express 2, 1524–1538 (2011).
[Crossref] [PubMed]

2006 (1)

Y. Li, R. Shekhar, and D. Huang, “Corneal Pachymetry Mapping with High-speed Optical Coherence Tomography,” Ophthalmology 113, 792 (2006).
[Crossref] [PubMed]

2001 (1)

C. Bowd, L. M. Zangwill, C. C. Berry, E. Z. Blumenthal, C. Vasile, C. Sanchez-Galeana, C. F. Bosworth, P. A. Sample, and R. N. Weinreb, “Detecting Early Glaucoma by Assessment of Retinal Nerve Fiber Layer Thickness and Visual Function,” Investigative Ophthalmology & Visual Science 42, 1993–2003 (2001).

1998 (1)

W. Drexler, C. K. Hitzenberger, A. Baumgartner, O. Findl, H. Sattmann, and A. F. Fercher, “Investigation of dispersion effects in ocular media by multiple wavelength partial coherence interferometry,” Experimental Eye Research 66, 25–33 (1998).
[Crossref] [PubMed]

1991 (2)

C. K. Hitzenberger, “Optical measurement of the axial eye length by laser Doppler interferometry,” Investigative ophthalmology & visual science 32, 616–624 (1991).

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and A. Et, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[Crossref] [PubMed]

1988 (1)

A. F. Fercher, K. Mengedoht, and W. Werner, “Eye-length measurement by interferometry with partially coherent light,” Optics Letters 13, 186–188 (1988).
[Crossref] [PubMed]

Abadi, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “Tensorflow: a system for large-scale machine learning,” in OSDI, vol. 16 (2016), pp. 265–283.

Adler, F. H.

F. H. Adler, P. L. Kaufman, L. A. Levin, and A. Alm, Adler’s Physiology of the Eye(ElsevierHealth Sciences, 2011).

Alm, A.

F. H. Adler, P. L. Kaufman, L. A. Levin, and A. Alm, Adler’s Physiology of the Eye(ElsevierHealth Sciences, 2011).

Ang, M.

M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Progress in Retinal and Eye Research (2018).
[Crossref] [PubMed]

Apostolopoulos, S.

S. Apostolopoulos, S. D. Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, (Springer, Cham, 2017), Lecture Notes in Computer Science, pp. 294–301.
[Crossref]

Aranha dos Santos, V.

V. Aranha dos Santos, L. Schmetterer, G. J. Triggs, R. A. Leitgeb, M. Gröschl, A. Messner, D. Schmidl, G. Garhofer, G. Aschinger, and R. M. Werkmeister, “Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT,” Biomedical Optics Express 7, 2650 (2016).
[Crossref]

V. Aranha dos Santos, L. Schmetterer, M. Gröschl, G. Garhofer, D. Schmidl, M. Kucera, A. Unterhuber, J.-P. Hermand, and R. M. Werkmeister, “In vivo tear film thickness measurement and tear film dynamics visualization using spectral domain optical coherence tomography,” Optics Express 23, 21043 (2015).
[Crossref] [PubMed]

M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Progress in Retinal and Eye Research (2018).
[Crossref] [PubMed]

Arpit, D.

S. Jastrzębski, Z. Kenton, D. Arpit, N. Ballas, A. Fischer, Y. Bengio, and A. Storkey, “Three Factors Influencing Minima in SGD,” arXiv:1711.04623 [cs, stat] (2017). ArXiv: 1711.04623.

Aschinger, G.

V. Aranha dos Santos, L. Schmetterer, G. J. Triggs, R. A. Leitgeb, M. Gröschl, A. Messner, D. Schmidl, G. Garhofer, G. Aschinger, and R. M. Werkmeister, “Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT,” Biomedical Optics Express 7, 2650 (2016).
[Crossref]

Aschinger, G. C.

R. M. Werkmeister, S. Sapeta, D. Schmidl, G. Garhöfer, G. Schmidinger, V. A. D. Santos, G. C. Aschinger, I. Baumgartner, N. Pircher, F. Schwarzhans, A. Pantalon, H. Dua, and L. Schmetterer, “Ultrahigh-resolution OCT imaging of the human cornea,” Biomedical Optics Express 8, 1221–1239 (2017).
[Crossref] [PubMed]

Asten, F. V.

F. G. Venhuizen, B. V. Ginneken, B. Liefers, F. V. Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomedical Optics Express 9, 1545–1569 (2018).
[Crossref] [PubMed]

Aung, T.

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head,” Investigative Ophthalmology & Visual Science 59, 63–74 (2018).
[Crossref]

Baar, C.

D. Schmidl, K. J. Witkowska, S. Kaya, C. Baar, H. Faatz, J. Nepp, A. Unterhuber, R. M. Werkmeister, G. Garhofer, and L. Schmetterer, “The Association Between Subjective and Objective Parameters for the Assessment of Dry-Eye Syndrome,” Investigative Ophthalmology & Visual Science 56, 1467–1472 (2015).
[Crossref]

Badrinarayanan, V.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” arXiv preprint arXiv:1511.00561 (2015).

Ballas, N.

S. Jastrzębski, Z. Kenton, D. Arpit, N. Ballas, A. Fischer, Y. Bengio, and A. Storkey, “Three Factors Influencing Minima in SGD,” arXiv:1711.04623 [cs, stat] (2017). ArXiv: 1711.04623.

Bao, F.

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R. M. Werkmeister, S. Sapeta, D. Schmidl, G. Garhöfer, G. Schmidinger, V. A. D. Santos, G. C. Aschinger, I. Baumgartner, N. Pircher, F. Schwarzhans, A. Pantalon, H. Dua, and L. Schmetterer, “Ultrahigh-resolution OCT imaging of the human cornea,” Biomedical Optics Express 8, 1221–1239 (2017).
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V. Aranha dos Santos, L. Schmetterer, G. J. Triggs, R. A. Leitgeb, M. Gröschl, A. Messner, D. Schmidl, G. Garhofer, G. Aschinger, and R. M. Werkmeister, “Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT,” Biomedical Optics Express 7, 2650 (2016).
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D. Schmidl, K. J. Witkowska, S. Kaya, C. Baar, H. Faatz, J. Nepp, A. Unterhuber, R. M. Werkmeister, G. Garhofer, and L. Schmetterer, “The Association Between Subjective and Objective Parameters for the Assessment of Dry-Eye Syndrome,” Investigative Ophthalmology & Visual Science 56, 1467–1472 (2015).
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V. Aranha dos Santos, L. Schmetterer, M. Gröschl, G. Garhofer, D. Schmidl, M. Kucera, A. Unterhuber, J.-P. Hermand, and R. M. Werkmeister, “In vivo tear film thickness measurement and tear film dynamics visualization using spectral domain optical coherence tomography,” Optics Express 23, 21043 (2015).
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M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Progress in Retinal and Eye Research (2018).
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D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen, and A. Elsheikh, “Reconstruction of 3d surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms,” Biomedical Signal Processing and Control 25, 91–98 (2016).
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T. Schmoll, A. Unterhuber, C. Kolbitsch, T. Le, A. Stingl, and R. Leitgeb, “Precise thickness measurements of Bowman’s layer, epithelium, and tear film,” Optometry & Vision Science 89, E795–E802 (2012).
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S. Apostolopoulos, S. D. Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, (Springer, Cham, 2017), Lecture Notes in Computer Science, pp. 294–301.
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B. Keller, M. Draelos, G. Tang, S. Farsiu, A. N. Kuo, K. Hauser, and J. A. Izatt, “Real-time corneal segmentation and 3d needle tracking in intrasurgical OCT,” Biomedical Optics Express 9, 2716–2732 (2018).
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A. J. Tatham and F. A. Medeiros, “Detecting Structural Progression in Glaucoma with Optical Coherence Tomography,” Ophthalmology 124, S57–S65 (2017).
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F. G. Venhuizen, B. V. Ginneken, B. Liefers, F. V. Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomedical Optics Express 9, 1545–1569 (2018).
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S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head,” Investigative Ophthalmology & Visual Science 59, 63–74 (2018).
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C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomedical Optics Express 8, 3440–3448 (2017).
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D. Schmidl, K. J. Witkowska, S. Kaya, C. Baar, H. Faatz, J. Nepp, A. Unterhuber, R. M. Werkmeister, G. Garhofer, and L. Schmetterer, “The Association Between Subjective and Objective Parameters for the Assessment of Dry-Eye Syndrome,” Investigative Ophthalmology & Visual Science 56, 1467–1472 (2015).
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T. Schmoll, A. Unterhuber, C. Kolbitsch, T. Le, A. Stingl, and R. Leitgeb, “Precise thickness measurements of Bowman’s layer, epithelium, and tear film,” Optometry & Vision Science 89, E795–E802 (2012).
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F. G. Venhuizen, B. V. Ginneken, B. Liefers, F. V. Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomedical Optics Express 9, 1545–1569 (2018).
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T. Zhang, A. Elazab, X. Wang, F. Jia, J. Wu, G. Li, and Q. Hu, “A Novel Technique for Robust and Fast Segmentation of Corneal Layer Interfaces Based on Spectral-Domain Optical Coherence Tomography Imaging,” IEEE Access 5, 10352–10363 (2017).
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N. Pircher, F. Schwarzhans, S. Holzer, J. Lammer, D. Schmidl, A. M. Bata, R. M. Werkmeister, G. Seidel, G. Garhöfer, A. Gschließer, L. Schmetterer, and G. Schmidinger, “Distinguishing Keratoconic Eyes and Healthy Eyes Using Ultrahigh-Resolution Optical Coherence Tomography–Based Corneal Epithelium Thickness Mapping,” American Journal of Ophthalmology 189, 47–54 (2018).
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V. Aranha dos Santos, L. Schmetterer, G. J. Triggs, R. A. Leitgeb, M. Gröschl, A. Messner, D. Schmidl, G. Garhofer, G. Aschinger, and R. M. Werkmeister, “Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT,” Biomedical Optics Express 7, 2650 (2016).
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D. Schmidl, K. J. Witkowska, S. Kaya, C. Baar, H. Faatz, J. Nepp, A. Unterhuber, R. M. Werkmeister, G. Garhofer, and L. Schmetterer, “The Association Between Subjective and Objective Parameters for the Assessment of Dry-Eye Syndrome,” Investigative Ophthalmology & Visual Science 56, 1467–1472 (2015).
[Crossref]

V. Aranha dos Santos, L. Schmetterer, M. Gröschl, G. Garhofer, D. Schmidl, M. Kucera, A. Unterhuber, J.-P. Hermand, and R. M. Werkmeister, “In vivo tear film thickness measurement and tear film dynamics visualization using spectral domain optical coherence tomography,” Optics Express 23, 21043 (2015).
[Crossref] [PubMed]

M. Ang, M. Baskaran, R. M. Werkmeister, J. Chua, D. Schmidl, V. Aranha dos Santos, G. Garhöfer, J. S. Mehta, and L. Schmetterer, “Anterior segment optical coherence tomography,” Progress in Retinal and Eye Research (2018).
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D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” Journal of Biomedical Optics 18, 056003 (2013).
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D. Schmidl, K. J. Witkowska, S. Kaya, C. Baar, H. Faatz, J. Nepp, A. Unterhuber, R. M. Werkmeister, G. Garhofer, and L. Schmetterer, “The Association Between Subjective and Objective Parameters for the Assessment of Dry-Eye Syndrome,” Investigative Ophthalmology & Visual Science 56, 1467–1472 (2015).
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S. Apostolopoulos, S. D. Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, (Springer, Cham, 2017), Lecture Notes in Computer Science, pp. 294–301.
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T. Zhang, A. Elazab, X. Wang, F. Jia, J. Wu, G. Li, and Q. Hu, “A Novel Technique for Robust and Fast Segmentation of Corneal Layer Interfaces Based on Spectral-Domain Optical Coherence Tomography Imaging,” IEEE Access 5, 10352–10363 (2017).
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Wu, Y.

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomedical Optics Express 8, 3440–3448 (2017).
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence 40, 834–848 (2018).
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Zanet, S. D.

S. Apostolopoulos, S. D. Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, (Springer, Cham, 2017), Lecture Notes in Computer Science, pp. 294–301.
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C. Bowd, L. M. Zangwill, C. C. Berry, E. Z. Blumenthal, C. Vasile, C. Sanchez-Galeana, C. F. Bosworth, P. A. Sample, and R. N. Weinreb, “Detecting Early Glaucoma by Assessment of Retinal Nerve Fiber Layer Thickness and Visual Function,” Investigative Ophthalmology & Visual Science 42, 1993–2003 (2001).

Zhang, T.

T. Zhang, A. Elazab, X. Wang, F. Jia, J. Wu, G. Li, and Q. Hu, “A Novel Technique for Robust and Fast Segmentation of Corneal Layer Interfaces Based on Spectral-Domain Optical Coherence Tomography Imaging,” IEEE Access 5, 10352–10363 (2017).
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Zheng, Y.

D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen, and A. Elsheikh, “Reconstruction of 3d surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms,” Biomedical Signal Processing and Control 25, 91–98 (2016).
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D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” Journal of Biomedical Optics 18, 056003 (2013).
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American Journal of Ophthalmology (1)

N. Pircher, F. Schwarzhans, S. Holzer, J. Lammer, D. Schmidl, A. M. Bata, R. M. Werkmeister, G. Seidel, G. Garhöfer, A. Gschließer, L. Schmetterer, and G. Schmidinger, “Distinguishing Keratoconic Eyes and Healthy Eyes Using Ultrahigh-Resolution Optical Coherence Tomography–Based Corneal Epithelium Thickness Mapping,” American Journal of Ophthalmology 189, 47–54 (2018).
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Biomedical Optics Express (8)

R. M. Werkmeister, S. Sapeta, D. Schmidl, G. Garhöfer, G. Schmidinger, V. A. D. Santos, G. C. Aschinger, I. Baumgartner, N. Pircher, F. Schwarzhans, A. Pantalon, H. Dua, and L. Schmetterer, “Ultrahigh-resolution OCT imaging of the human cornea,” Biomedical Optics Express 8, 1221–1239 (2017).
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F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomedical Optics Express 2, 1524–1538 (2011).
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B. Keller, M. Draelos, G. Tang, S. Farsiu, A. N. Kuo, K. Hauser, and J. A. Izatt, “Real-time corneal segmentation and 3d needle tracking in intrasurgical OCT,” Biomedical Optics Express 9, 2716–2732 (2018).
[Crossref] [PubMed]

V. Aranha dos Santos, L. Schmetterer, G. J. Triggs, R. A. Leitgeb, M. Gröschl, A. Messner, D. Schmidl, G. Garhofer, G. Aschinger, and R. M. Werkmeister, “Super-resolved thickness maps of thin film phantoms and in vivo visualization of tear film lipid layer using OCT,” Biomedical Optics Express 7, 2650 (2016).
[Crossref]

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

F. G. Venhuizen, B. V. Ginneken, B. Liefers, F. V. Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomedical Optics Express 9, 1545–1569 (2018).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomedical Optics Express 8, 3440–3448 (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,” Biomedical Optics Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

Biomedical Signal Processing and Control (1)

D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen, and A. Elsheikh, “Reconstruction of 3d surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms,” Biomedical Signal Processing and Control 25, 91–98 (2016).
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Experimental Eye Research (1)

W. Drexler, C. K. Hitzenberger, A. Baumgartner, O. Findl, H. Sattmann, and A. F. Fercher, “Investigation of dispersion effects in ocular media by multiple wavelength partial coherence interferometry,” Experimental Eye Research 66, 25–33 (1998).
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IEEE Access (1)

T. Zhang, A. Elazab, X. Wang, F. Jia, J. Wu, G. Li, and Q. Hu, “A Novel Technique for Robust and Fast Segmentation of Corneal Layer Interfaces Based on Spectral-Domain Optical Coherence Tomography Imaging,” IEEE Access 5, 10352–10363 (2017).
[Crossref]

IEEE transactions on pattern analysis and machine intelligence (1)

L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence 40, 834–848 (2018).
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Investigative Ophthalmology & Visual Science (3)

D. Schmidl, K. J. Witkowska, S. Kaya, C. Baar, H. Faatz, J. Nepp, A. Unterhuber, R. M. Werkmeister, G. Garhofer, and L. Schmetterer, “The Association Between Subjective and Objective Parameters for the Assessment of Dry-Eye Syndrome,” Investigative Ophthalmology & Visual Science 56, 1467–1472 (2015).
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C. Bowd, L. M. Zangwill, C. C. Berry, E. Z. Blumenthal, C. Vasile, C. Sanchez-Galeana, C. F. Bosworth, P. A. Sample, and R. N. Weinreb, “Detecting Early Glaucoma by Assessment of Retinal Nerve Fiber Layer Thickness and Visual Function,” Investigative Ophthalmology & Visual Science 42, 1993–2003 (2001).

S. K. Devalla, K. S. Chin, J.-M. Mari, T. A. Tun, N. G. Strouthidis, T. Aung, A. H. Thiéry, and M. J. A. Girard, “A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head,” Investigative Ophthalmology & Visual Science 59, 63–74 (2018).
[Crossref]

Journal of Biomedical Optics (1)

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” Journal of Biomedical Optics 18, 056003 (2013).
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Journal of Medical Signals and Sensors (1)

M. K. Jahromi, R. Kafieh, H. Rabbani, A. M. Dehnavi, A. Peyman, F. Hajizadeh, and M. Ommani, “An Automatic Algorithm for Segmentation of the Boundaries of Corneal Layers in Optical Coherence Tomography Images using Gaussian Mixture Model,” Journal of Medical Signals and Sensors 4, 171–180 (2014).
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Nature (1)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).
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Ophthalmology (2)

Y. Li, R. Shekhar, and D. Huang, “Corneal Pachymetry Mapping with High-speed Optical Coherence Tomography,” Ophthalmology 113, 792 (2006).
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A. J. Tatham and F. A. Medeiros, “Detecting Structural Progression in Glaucoma with Optical Coherence Tomography,” Ophthalmology 124, S57–S65 (2017).
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Ophthalmology Retina (1)

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images,” Ophthalmology Retina 1, 322–327 (2017).
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Figures (5)

Fig. 1
Fig. 1 Diagram of a typical U-net architecture. After passing through all the blocks, the input image is transformed into a label image. The building blocks A, B and C are defined in terms of basic building blocks (convolution, pooling, concatenation, up-sampling and fully-connected (dense) layer). The last layer of the network is a fully-connected layer with four channels followed by a soft-max activation (the probability for each class). The number of channels of this layer corresponds to the number of classes of the labels (i.e. 4). The activation and batch normalization layers are omitted for clarity.
Fig. 2
Fig. 2 Training loss as a function of the epoch for the five tested models for one fold (logarithmic scale). During the learning process of each model, the loss, representing the error, decreases. CUnet1 and CorneaNet learn faster and achieve finally lower loss than other models.
Fig. 3
Fig. 3 CorneaNet automatically segments cornea OCT images with high accuracy. Segmentation of healthy and keratoconic corneas using the Matlab algorithm and CorneaNet. Keratoconic corneas exhibit usually at least one layer with non-uniform thickness. Scale bar: 250 μm
Fig. 4
Fig. 4 Using CorneaNet, the thicknesses of the epithelium, stroma and Bowman’s layer were computed in a healthy and a keratoconus case. The healthy case shows close to uniform thicknesses for all three layers, while for the keratoconus case, in a specific region of the cornea, the epithelium and stroma are thinner and the Bowman’s layer is thicker. (a-c) Thickness calculation in one tomogram. (a) UHR-OCT tomogram of a keratoconus patient, (b) corresponding labels map computed using CorneaNet. (c) Thicknesses of the three corneal layers computed using the label maps. (d-f) Thickness maps in a healthy subject case. (g-i) Thickness maps in a keratoconus case. The thickness scale bar is shared by the maps horizontally. Scale bar: 1 mm.
Fig. 5
Fig. 5 Comparison between (a) full corneal thickness map obtained using UHR-OCT and CorneaNet; and (b) an Oculus Pentacam total power image. Both measurements were done in the same eye suffering from keratoconus.

Tables (8)

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Table 1 Characteristics of the architectures of the studied models.

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Table 2 Results of the different models on validation data obtained using six-fold cross-validation (average ± standard deviation). The support is the fraction of the images covered by each class in the dataset. The sensitivity is the fraction of the true labels that were correctly identified for each class. The precision is the fraction of the predicted labels that are correct for each class. The accuracy is the fraction of correctly identified pixels. All metrics are defined in section 2.2. The background class corresponds to air or aqueous humor.

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Table 3 Time and memory characteristics of the studied models. The image memory is the memory required to store all the feature images data of hidden layers in the GPU memory. B is the batch size we used for the training.1 The values are obtained with an input image size of 1024 × 384 pixels.

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Table 4 Comparison of the durations of various tasks for several algorithms .

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Table 5 Statistical analysis of the dataset. The thicknesses of different layers in both groups are shown. The reported value is the mean ± standard deviation.

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Table 6 Architecture of CUNet 1 and CorneaNet

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Table 7 Architecture of CUNet3

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Table 8 Architecture of CUNet5

Equations (7)

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H ( p ) x p ( x ) log  p ( x ) .
D ( p | | q ) x p ( x ) log  p ( x ) q ( x ) .
H ( p , q ) x p ( x ) log  q ( x ) .
D ( p | | q ) = H ( p , q ) H ( p ) .
L ( θ ) = n = 1 N l ( θ , x n )
G = x n B l ( θ , x n ) θ
θ k + 1 = θ k η   G

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