Abstract

Segmentation of retinal layers in optical coherence tomography (OCT) is an essential step in OCT image analysis for screening, diagnosis, and assessment of retinal disease progression. Real-time segmentation together with high-speed OCT volume acquisition allows rendering of en face OCT of arbitrary retinal layers, which can be used to increase the yield rate of high-quality scans, provide real-time feedback during image-guided surgeries, and compensate aberrations in adaptive optics (AO) OCT without using wavefront sensors. We demonstrate here unprecedented real-time OCT segmentation of eight retinal layer boundaries achieved by 3 levels of optimization: 1) a modified, low complexity, neural network structure, 2) an innovative scheme of neural network compression with TensorRT, and 3) specialized GPU hardware to accelerate computation. Inferencing with the compressed network U-NetRT took 3.5 ms, improving by 21 times the speed of conventional U-Net inference without reducing the accuracy. The latency of the entire pipeline from data acquisition to inferencing was only 41 ms, enabled by parallelized batch processing. The system and method allow real-time updating of en face OCT and OCTA visualizations of arbitrary retinal layers and plexuses in continuous mode scanning. To the best our knowledge, our work is the first demonstration of an ophthalmic imager with embedded artificial intelligence (AI) providing real-time feedback.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2020 (2)

2019 (8)

T. Zhang, A. M. Kho, and V. J. Srinivasan, “Improving visible light OCT of the human retina with rapid spectral shaping and axial tracking,” Biomed. Opt. Express 10(6), 2918–2931 (2019).
[Crossref]

P. Mecê, V. Mazlin, J. Scholler, P. Xiao, J. Sahel, K. Grieve, M. Fink, and C. Boccara, “Real-time axial retinal motion tracking and correction for consistent high-resolution retinal imaging with Full-Field Time-Domain Optical Coherence Tomography (FFOCT),” Invest Ophthalmol Vis Sci 60, 022 (2019).

M. J. Heiferman and A. A. Fawzi, “Progression of subclinical choroidal neovascularization in age-related macular degeneration,” PLoS One 14(6), e0217805 (2019).
[Crossref]

B. Wang, A. Camino, S. Pi, Y. Guo, J. Wang, D. Huang, T. S. Hwang, and Y. Jia, “Three-dimensional structural and angiographic evaluation of foveal ischemia in diabetic retinopathy: method and validation,” Biomed. Opt. Express 10(7), 3522–3532 (2019).
[Crossref]

Q. You, Y. Guo, J. Wang, X. Wei, A. Camino, P. Zang, C. J. Flaxel, S. T. Bailey, D. Huang, T. S. Hwang, and Y. Jia, “Detection of Clinically Unsuspected Retinal Neovascularization with Wide-Field Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 60, 3278 (2019).
[Crossref]

S. Taylor, J. M. Brown, K. Gupta, J. P. Campbell, S. Ostmo, R. V. P. Chan, J. Dy, D. Erdogmus, S. Ioannidis, S. J. Kim, J. Kalpathy-Cramer, and M. F. Chiang, Imaging and Informatics in Retinopathy of Prematurity Consortium, “Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning,” JAMA Ophthalmol. 137(9), 1022–1028 (2019).
[Crossref]

D. Lu, M. Heisler, S. Lee, G. W. Ding, E. Navajas, M. V. Sarunic, and M. F. Beg, “Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network,” Med. Image Anal. 54, 100–110 (2019).
[Crossref]

A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
[Crossref]

2018 (9)

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,” Biomed. Opt. Express 9(6), 2716–2732 (2018).
[Crossref]

A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
[Crossref]

A. Camino, M. Zhang, L. Liu, J. Wang, Y. Jia, and D. Huang, “Enhanced Quantification of Retinal Perfusion by Improved Discrimination of Blood Flow From Bulk Motion Signal in OCTA,” Trans. Vis. Sci. Tech. 7(6), 20 (2018).
[Crossref]

J. Xue, A. Camino, S. T. Bailey, X. Liu, D. Li, and Y. Jia, “Automatic quantification of choroidal neovascularization lesion area on OCT angiography based on density cell-like P systems with active membranes,” Biomed. Opt. Express 9(7), 3208–3219 (2018).
[Crossref]

A. M. Hagag, A. D. Pechauer, L. Liu, J. Wang, M. Zhang, Y. Jia, and D. Huang, “OCT Angiography Changes in the 3 Parafoveal Retinal Plexuses in Response to Hyperoxia,” Ophthalmol. Retina 2(4), 329–336 (2018).
[Crossref]

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
[Crossref]

Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (2018).
[Crossref]

J. Loo, L. Fang, D. Cunefare, G. J. Jaffe, and S. Farsiu, “Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2,” Biomed. Opt. Express 9(6), 2681–2698 (2018).
[Crossref]

E. M. A. Anas, P. Mousavi, and P. Abolmaesumi, “A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy,” Med. Image Anal. 48, 107–116 (2018).
[Crossref]

2017 (12)

C. F. Baumgartner, K. Kamnitsas, J. Matthew, T. P. Fletcher, S. Smith, L. M. Koch, B. Kainz, and D. Rueckert, “SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound,” IEEE Trans. Med. Imaging 36(11), 2204–2215 (2017).
[Crossref]

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]

L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Trans. Vis. Sci. Tech. 6(1), 12 (2017).
[Crossref]

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C.-Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” J. Am. Med. Assoc. 318(22), 2211–2223 (2017).
[Crossref]

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
[Crossref]

R. Gargeya and T. Leng, “Automated Identification of Diabetic Retinopathy Using Deep Learning,” Ophthalmology 124(7), 962–969 (2017).
[Crossref]

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (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,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref]

R. Zhao, A. Camino, J. Wang, A. M. Hagag, Y. Lu, S. T. Bailey, C. J. Flaxel, T. S. Hwang, D. Huang, D. Li, and Y. Jia, “Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography,” Biomed. Opt. Express 8(11), 5049–5064 (2017).
[Crossref]

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
[Crossref]

M. J. Ju, M. Heisler, D. Wahl, Y. Jian, and M. Sarunic, “Multiscale sensorless adaptive optics OCT angiography system for in vivo human retinal imaging,” J. Biomed. Opt. 22(12), 121703 (2017).
[Crossref]

H. R. G. W. Verstraete, M. Heisler, M. J. Ju, D. Wahl, L. Bliek, J. Kalkman, S. Bonora, Y. Jian, M. Verhaegen, and M. V. Sarunic, “Wavefront sensorless adaptive optics OCT with the DONE algorithm for in vivo human retinal imaging [Invited],” Biomed. Opt. Express 8(4), 2261–2275 (2017).
[Crossref]

2016 (5)

M. Cua, S. Lee, D. Miao, M. J. Ju, P. Mackenzie, Y. Jian, and M. Sarunic, “Retinal optical coherence tomography at 1  µm with dynamic focus control and axial motion tracking,” J. Biomed. Opt. 21(2), 026007 (2016).
[Crossref]

P. Prentašić, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 075008 (2016).
[Crossref]

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” J. Am. Med. Assoc. 316(22), 2402–2410 (2016).
[Crossref]

Z. Mammo, M. Heisler, C. Balaratnasingam, S. Lee, D.-Y. Yu, P. Mackenzie, S. Schendel, A. Merkur, A. Kirker, D. Albiani, E. Navajas, M. F. Beg, W. Morgan, and M. V. Sarunic, “Quantitative Optical Coherence Tomography Angiography of Radial Peripapillary Capillaries in Glaucoma, Glaucoma Suspect, and Normal Eyes,” Am. J. Ophthalmol. 170, 41–49 (2016).
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2015 (1)

2014 (1)

J. Xu, K. Wong, Y. Jian, and M. Sarunic, “Real-time acquisition and display of flow contrast using speckle variance optical coherence tomography in a graphics processing unit,” J. Biomed. Opt. 19(2), 026001 (2014).
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2013 (2)

Y. Jian, K. Wong, and M. Sarunic, “Graphics processing unit accelerated optical coherence tomography processing at megahertz axial scan rate and high resolution video rate volumetric rendering,” J. Biomed. Opt. 18, 026002 (2013).
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P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

2010 (1)

2009 (1)

2007 (1)

R. Zawadzki, A. Fuller, D. Wiley, B. Hamann, S. Choi, and J. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
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2006 (1)

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability,” NeuroImage 31(3), 1116–1128 (2006).
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1995 (1)

J. S. Schuman, M. R. Hee, C. A. Puliafito, C. Wong, T. Pedut-Kloizman, C. P. Lin, E. Hertzmark, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Quantification of Nerve Fiber Layer Thickness in Normal and Glaucomatous Eyes Using Optical Coherence Tomography: A Pilot Study,” Arch. Ophthalmol. 113(5), 586–596 (1995).
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Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
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Abolmaesumi, P.

E. M. A. Anas, P. Mousavi, and P. Abolmaesumi, “A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy,” Med. Image Anal. 48, 107–116 (2018).
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Abràmoff, M. D.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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Albiani, D.

Z. Mammo, M. Heisler, C. Balaratnasingam, S. Lee, D.-Y. Yu, P. Mackenzie, S. Schendel, A. Merkur, A. Kirker, D. Albiani, E. Navajas, M. F. Beg, W. Morgan, and M. V. Sarunic, “Quantitative Optical Coherence Tomography Angiography of Radial Peripapillary Capillaries in Glaucoma, Glaucoma Suspect, and Normal Eyes,” Am. J. Ophthalmol. 170, 41–49 (2016).
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Amelon, R.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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Anas, E. M. A.

E. M. A. Anas, P. Mousavi, and P. Abolmaesumi, “A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy,” Med. Image Anal. 48, 107–116 (2018).
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Askham, H.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
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Aung, T.

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C.-Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” J. Am. Med. Assoc. 318(22), 2211–2223 (2017).
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Ayoub, K.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
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Back, T.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
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Bailey, S. T.

Balaratnasingam, C.

Z. Mammo, M. Heisler, C. Balaratnasingam, S. Lee, D.-Y. Yu, P. Mackenzie, S. Schendel, A. Merkur, A. Kirker, D. Albiani, E. Navajas, M. F. Beg, W. Morgan, and M. V. Sarunic, “Quantitative Optical Coherence Tomography Angiography of Radial Peripapillary Capillaries in Glaucoma, Glaucoma Suspect, and Normal Eyes,” Am. J. Ophthalmol. 170, 41–49 (2016).
[Crossref]

Baskaran, M.

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C.-Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” J. Am. Med. Assoc. 318(22), 2211–2223 (2017).
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Baumgartner, C. F.

C. F. Baumgartner, K. Kamnitsas, J. Matthew, T. P. Fletcher, S. Smith, L. M. Koch, B. Kainz, and D. Rueckert, “SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound,” IEEE Trans. Med. Imaging 36(11), 2204–2215 (2017).
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Beg, M. F.

D. Lu, M. Heisler, S. Lee, G. W. Ding, E. Navajas, M. V. Sarunic, and M. F. Beg, “Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network,” Med. Image Anal. 54, 100–110 (2019).
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P. Prentašić, M. Heisler, Z. Mammo, S. Lee, A. Merkur, E. Navajas, M. F. Beg, M. Šarunic, and S. Lončarić, “Segmentation of the foveal microvasculature using deep learning networks,” J. Biomed. Opt. 21(7), 075008 (2016).
[Crossref]

Z. Mammo, M. Heisler, C. Balaratnasingam, S. Lee, D.-Y. Yu, P. Mackenzie, S. Schendel, A. Merkur, A. Kirker, D. Albiani, E. Navajas, M. F. Beg, W. Morgan, and M. V. Sarunic, “Quantitative Optical Coherence Tomography Angiography of Radial Peripapillary Capillaries in Glaucoma, Glaucoma Suspect, and Normal Eyes,” Am. J. Ophthalmol. 170, 41–49 (2016).
[Crossref]

Bizheva, K.

Blackwell, S.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
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Bliek, L.

Boccara, C.

P. Mecê, V. Mazlin, J. Scholler, P. Xiao, J. Sahel, K. Grieve, M. Fink, and C. Boccara, “Real-time axial retinal motion tracking and correction for consistent high-resolution retinal imaging with Full-Field Time-Domain Optical Coherence Tomography (FFOCT),” Invest Ophthalmol Vis Sci 60, 022 (2019).

Bonora, S.

Bouton, S.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
[Crossref]

Bressler, N. M.

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C.-Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” J. Am. Med. Assoc. 318(22), 2211–2223 (2017).
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P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
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Brown, J. M.

S. Taylor, J. M. Brown, K. Gupta, J. P. Campbell, S. Ostmo, R. V. P. Chan, J. Dy, D. Erdogmus, S. Ioannidis, S. J. Kim, J. Kalpathy-Cramer, and M. F. Chiang, Imaging and Informatics in Retinopathy of Prematurity Consortium, “Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning,” JAMA Ophthalmol. 137(9), 1022–1028 (2019).
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Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, (Springer International Publishing, 2015), 234–241.

Burlina, P. M.

P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks,” JAMA Ophthalmol. 135(11), 1170–1176 (2017).
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Camino, A.

A. Camino, R. Ng, J. Huang, Y. Guo, S. Ni, Y. Jia, D. Huang, and Y. Jian, “Depth-resolved optimization of a real-time sensorless adaptive optics optical coherence tomography,” Opt. Lett. 45(9), 2612–2615 (2020).
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A. Camino, Y. Jia, J. Yu, J. Wang, L. Liu, and D. Huang, “Automated detection of shadow artifacts in optical coherence tomography angiography,” Biomed. Opt. Express 10(3), 1514–1531 (2019).
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Q. You, Y. Guo, J. Wang, X. Wei, A. Camino, P. Zang, C. J. Flaxel, S. T. Bailey, D. Huang, T. S. Hwang, and Y. Jia, “Detection of Clinically Unsuspected Retinal Neovascularization with Wide-Field Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 60, 3278 (2019).
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B. Wang, A. Camino, S. Pi, Y. Guo, J. Wang, D. Huang, T. S. Hwang, and Y. Jia, “Three-dimensional structural and angiographic evaluation of foveal ischemia in diabetic retinopathy: method and validation,” Biomed. Opt. Express 10(7), 3522–3532 (2019).
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Z. Wang, A. Camino, A. M. Hagag, J. Wang, R. G. Weleber, P. Yang, M. E. Pennesi, D. Huang, D. Li, and Y. Jia, “Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning,” J. Biophotonics 11(5), e201700313 (2018).
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A. Camino, Z. Wang, J. Wang, M. E. Pennesi, P. Yang, D. Huang, D. Li, and Y. Jia, “Deep learning for the segmentation of preserved photoreceptors on en face optical coherence tomography in two inherited retinal diseases,” Biomed. Opt. Express 9(7), 3092–3105 (2018).
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J. Xue, A. Camino, S. T. Bailey, X. Liu, D. Li, and Y. Jia, “Automatic quantification of choroidal neovascularization lesion area on OCT angiography based on density cell-like P systems with active membranes,” Biomed. Opt. Express 9(7), 3208–3219 (2018).
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A. Camino, M. Zhang, L. Liu, J. Wang, Y. Jia, and D. Huang, “Enhanced Quantification of Retinal Perfusion by Improved Discrimination of Blood Flow From Bulk Motion Signal in OCTA,” Trans. Vis. Sci. Tech. 7(6), 20 (2018).
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R. Zhao, A. Camino, J. Wang, A. M. Hagag, Y. Lu, S. T. Bailey, C. J. Flaxel, T. S. Hwang, D. Huang, D. Li, and Y. Jia, “Automated drusen detection in dry age-related macular degeneration by multiple-depth, en face optical coherence tomography,” Biomed. Opt. Express 8(11), 5049–5064 (2017).
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Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
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Campbell, J. P.

S. Taylor, J. M. Brown, K. Gupta, J. P. Campbell, S. Ostmo, R. V. P. Chan, J. Dy, D. Erdogmus, S. Ioannidis, S. J. Kim, J. Kalpathy-Cramer, and M. F. Chiang, Imaging and Informatics in Retinopathy of Prematurity Consortium, “Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning,” JAMA Ophthalmol. 137(9), 1022–1028 (2019).
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Ceklic, L.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. D. Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints,” IEEE Trans. Med. Imaging 32(3), 531–543 (2013).
[Crossref]

Chan, R. V. P.

S. Taylor, J. M. Brown, K. Gupta, J. P. Campbell, S. Ostmo, R. V. P. Chan, J. Dy, D. Erdogmus, S. Ioannidis, S. J. Kim, J. Kalpathy-Cramer, and M. F. Chiang, Imaging and Informatics in Retinopathy of Prematurity Consortium, “Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning,” JAMA Ophthalmol. 137(9), 1022–1028 (2019).
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Chen, Q.

L. de Sisternes, G. Jonna, M. A. Greven, Q. Chen, T. Leng, and D. L. Rubin, “Individual Drusen Segmentation and Repeatability and Reproducibility of Their Automated Quantification in Optical Coherence Tomography Images,” Trans. Vis. Sci. Tech. 6(1), 12 (2017).
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Cheng, C.-Y.

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C.-Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” J. Am. Med. Assoc. 318(22), 2211–2223 (2017).
[Crossref]

Cheung, C. Y.-L.

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C.-Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” J. Am. Med. Assoc. 318(22), 2211–2223 (2017).
[Crossref]

Cheung, G. C. M.

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, E. Y. M. Wong, C. Sabanayagam, M. Baskaran, F. Ibrahim, N. C. Tan, E. A. Finkelstein, E. L. Lamoureux, I. Y. Wong, N. M. Bressler, S. Sivaprasad, R. Varma, J. B. Jonas, M. G. He, C.-Y. Cheng, G. C. M. Cheung, T. Aung, W. Hsu, M. L. Lee, and T. Y. Wong, “Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes,” J. Am. Med. Assoc. 318(22), 2211–2223 (2017).
[Crossref]

Chiang, M. F.

S. Taylor, J. M. Brown, K. Gupta, J. P. Campbell, S. Ostmo, R. V. P. Chan, J. Dy, D. Erdogmus, S. Ioannidis, S. J. Kim, J. Kalpathy-Cramer, and M. F. Chiang, Imaging and Informatics in Retinopathy of Prematurity Consortium, “Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning,” JAMA Ophthalmol. 137(9), 1022–1028 (2019).
[Crossref]

Chiu, S. J.

Choi, S.

R. Zawadzki, A. Fuller, D. Wiley, B. Hamann, S. Choi, and J. Werner, “Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets,” J. Biomed. Opt. 12(4), 041206 (2007).
[Crossref]

Chopra, R.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
[Crossref]

Clarida, W.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
[Crossref]

Clausi, D. A.

Conjeti, S.

Coram, M.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” J. Am. Med. Assoc. 316(22), 2402–2410 (2016).
[Crossref]

Cornebise, J.

J. De Fauw, J. R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell, H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche, B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King, A. Karthikesalingam, C. O. Hughes, R. Raine, J. Hughes, D. A. Sim, C. Egan, A. Tufail, H. Montgomery, D. Hassabis, G. Rees, T. Back, P. T. Khaw, M. Suleyman, J. Cornebise, P. A. Keane, and O. Ronneberger, “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nat. Med. 24(9), 1342–1350 (2018).
[Crossref]

Cua, M.

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Wong, K.

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Supplementary Material (3)

NameDescription
» Visualization 1       In vivo imaging session with real-time retinal layer segmentation enabled
» Visualization 2       In vivo imaging session with real-time retinal layer segmentation enabled
» Visualization 3       In vivo imaging session with real-time retinal layer segmentation enabled

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Figures (10)

Fig. 1.
Fig. 1. Summary of deep learning segmentation workflow. After generating the ground truth and pre-processing the cross-sectional B-scans (including augmentation of B-scans available), a neural network was trained to segment seven retinal layers. An optimization process cropped the network size and a calibration that took into account the dynamic range of activations was used to reduce the precision of network weights in order to perform inference of averaged groups of 10 OCT B-scans simultaneously with acquisition. After layer boundaries had been defined, en face projections of the volumetric data could be produced.
Fig. 2.
Fig. 2. Representation of network architecture of the modified U-Net trained to segment retinal layers. The number of feature channels is specified for each stage. This network was further compressed, as explained in Section 2.5, in order to be used for real-time inference simultaneously with data acquisition.
Fig. 3.
Fig. 3. Pre-processing framework of B-scans prior to training of the neural network. Ten adjacent B-scans are first averaged to increase the signal-to-noise ratio. Data was augmented by introducing random brightness adjustments in order to account for all different signal intensities. Contrast was then adjusted for original and augmented data saturating the bottom and top 1% of pixel intensities. Then, the data and ground truth obtained by graph-cut (GC) segmentation were fed to a U-Net type neural network for training.
Fig. 4.
Fig. 4. Detection and correction steps of segmentation inaccuracies. (a) Original B-scan. (b) Segmentation with missing layer boundary for INL (blue). (c) Detected layer boundaries with a gap. (d) Layer boundaries after linear interpolation.
Fig. 5.
Fig. 5. Representative scans visualizing the agreement of the U-NetRT inference with the classification made by the originally trained U-Net architecture, as well as the ground truth.
Fig. 6.
Fig. 6. Profiler for B-scan segmentation. (a) After data was acquired in batches of 10 frames (blue segments), it was transferred from host to GPU memory in synchronization with the acquisition of the next batch (orange segments). Then, OCT signal processing, image pre-processing, inference, and post-processing was performed during the acquisition of the following batch (cyan segments). (b) Processing encompasses OCT data processing, OCT image pre-processing, inference with U-NetRT and post-processing, yielding an approximate total latency of 41 ms.
Fig. 7.
Fig. 7. Representative results of the real-time deep-learning inference processing using our U-NetRT architecture. Four steps are shown in order of occurrence: acquisition of a B-scan, contrast adjustment, layer classification of pixels, and extraction of the layer boundaries.
Fig. 8.
Fig. 8. Screen capture of the OCTViewer software representing en face visualizations of arbitrary retinal layers simultaneously with data acquisition (see Visualization 1), with a total latency of 41 ms lapsed after acquisition of the batch of frames by the spectrometer. The horizontal blue line represents the position of the B-scan visualized on the upper-left panel.
Fig. 9.
Fig. 9. Screen capture of the OCTViewer software representing segmentation performance in the area between optic disc and macula. Four en face OCT projections of the RNFL, GCIPL, INL-OPL-ONL and EZ-RPE slabs are visualized. The horizontal blue line represents the B-scan in the upper left corner and the red line represents the one in the lower left corner. Although this field of view was not used in training, segmentation was correct except in the area inside the optic disc.
Fig. 10.
Fig. 10. Representative examples of neural network segmentation errors. Single-frame B-scans (upper row) without frame averaging did not guarantee the signal-to-noise ratio required to delineate relatively dark boundaries such as the inner nuclear layer with the inner and outer plexiform layers, yielding ‘broken’ layer boundary graphs. Although averaging of ten B-scans over five adjacent B-scan positions increased the signal-to-noise ratio, the frames found at the proximity of positions of large bulk motion artifacts presented distinct layers overlapping at the same axial depths, precluding with the performance of the neural network.

Tables (3)

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Table 1. Dice coefficients representing the performance of the original U-Net and the modified U-Net on the test set for the eight layer labels produced. ILM – Inner limiting membrane. NFL – Nerve fiber layer. GCL – Ganglion cell layer. IPL – Inner plexiform layer. INL – Inner nuclear layer. OPL – Outer plexiform layer. PR – Photoreceptors. RPE – Retinal pigment epithelium.

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Table 2. Comparison of dice coefficients between two networks trained on different data when tested on images with a high brightness/contrast coefficient. Network 1 was trained on images with default brightness, while the Network 2 was trained on images with random brightness and contrast applied.

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Table 3. Comparison of inferencing time (mean ± standard deviation) for the two network architectures (modified U-Net and U-NetRT after architecture optimization) using TensorFlow and TensorRT with network parameters of different precision modes (floating point 32 and 16, and integer 8).

Equations (4)

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L o v e r a l l = 0.5 L d i c e + L l o g
L d i c e = 1 2 Σ x Ω p l ( x ) g l ( x ) Σ x Ω p l 2 ( x ) + Σ x Ω g ( x )
L l o g = Σ x Ω w ( x ) g l ( x ) l o g ( p l ( x ) )
ω ( x ) = 1 + 10 I ( l ( x ) 0 ) + 5 I ( l ( x ) = L )

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