Abstract

Optical coherence tomography angiography (OCTA) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes digital resolution degradation, such as the angiograms in wide-field OCTA. In this paper, we propose to address this problem using deep learning. We conducted extensive experiments on converting the centrally cropped 3 × 3 mm2 field of view (FOV) of the 8 × 8 mm2 foveal OCTA images (a sampling density of 22.9 µm) to the native 3 × 3 mm2 en face OCTA images (a sampling density of 12.2 µm). We employed a cycle-consistent adversarial network architecture in this conversion. The quantitative analysis using the perceptual similarity measures shows the generated OCTA images are closer to the native 3 × 3 mm2 scans. Besides, the results show the proposed method could also enhance the signal-to-noise ratio. We further applied our method to enhance diseased cases and calculate vascular biomarkers, which demonstrates its generalization performance and clinical perspective.

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

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2019 (4)

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günaydin, L. A. Bentolila, C. Kural, and A. Ozcan, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nat. Methods 16(1), 103–110 (2019).
[Crossref]

K. de Haan, Z. S. Ballard, Y. Rivenson, Y. Wu, and A. Ozcan, “Resolution enhancement in scanning electron microscopy using deep learning,” Sci. Rep. 9(1), 12050 (2019).
[Crossref]

M.-W. Lee, K.-M. Kim, H.-B. Lim, Y.-J. Jo, and J.-Y. Kim, “Repeatability of vessel density measurements using optical coherence tomography angiography in retinal diseases,” Br. J. Ophthalmol. 103(5), 704–710 (2019).
[Crossref]

A. Borji, “Pros and cons of gan evaluation measures,” Comput. Vis. Image Und. 179, 41–65 (2019).
[Crossref]

2018 (5)

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

X. X. Li, W. Wu, H. Zhou, J. J. Deng, M. Y. Zhao, T. W. Qian, C. Yan, X. Xu, and S. Q. Yu, “A quantitative comparison of five optical coherence tomography angiography systems in clinical performance,” Int. J. Ophthalmol. 11(11), 1784–1795 (2018).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydin, Y. Zhang, Z. Göröcs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5(6), 2354–2364 (2018).
[Crossref]

H. Jiang, Y. Wei, Y. Shi, C. Wright, X. Sun, G. Gregori, F. Zheng, E. Vanner, B. Lam, T. Rundek, and J. Wang, “Altered Macular Microvasculature in Mild Cognitive Impairment and Alzheimer Disease,” J. Neuro-Ophthalmol. 38(3), 292–298 (2018).
[Crossref]

B. E. O’Bryhim, R. S. Apte, N. Kung, D. Coble, and G. P. Van Stavern, “Association of Preclinical Alzheimer Disease With Optical Coherence Tomographic Angiography Findings,” JAMA Ophthalmol. 136(11), 1242–1248 (2018).
[Crossref]

2017 (10)

C.-L. Chen and R. K. Wang, “Optical coherence tomography based angiography [invited],” Biomed. Opt. Express 8(2), 1056–1082 (2017).
[Crossref]

J. P. Campbell, E. Nudleman, J. Yang, O. Tan, R. V. Chan, M. F. Chiang, D. Huang, and G. Liu, “Handheld optical coherence tomography angiography and ultra-wide-field optical coherence tomography in retinopathy of prematurity,” JAMA Ophthalmol. 135(9), 977–981 (2017).
[Crossref]

M. Salas, M. Augustin, L. Ginner, A. Kumar, B. Baumann, R. Leitgeb, W. Drexler, S. Prager, J. Hafner, U. Schmidt-Erfurth, and M. Pircher, “Visualization of micro-capillaries using optical coherence tomography angiography with and without adaptive optics,” Biomed. Opt. Express 8(1), 207–222 (2017).
[Crossref]

T. Klein and R. Huber, “High-speed OCT light sources and systems [Invited],” Biomed. Opt. Express 8(2), 828 (2017).
[Crossref]

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

M. Al-Sheikh, K. G. Falavarjani, H. Akil, and S. R. Sadda, “Impact of image quality on OCT angiography based quantitative measurements,” Int. J. Retin. Vitr. 3(1), 13 (2017).
[Crossref]

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

S. Men, C.-L. Chen, W. Wei, T.-Y. Lai, S. Song, and R. K. Wang, “Repeatability of vessel density measurement in human skin by oct-based microangiography,” Skin Res. Technol. 23(4), 607–612 (2017).
[Crossref]

M. Alam, D. Thapa, J. I. Lim, D. Cao, and X. Yao, “Quantitative characteristics of sickle cell retinopathy in optical coherence tomography angiography,” Biomed. Opt. Express 8(3), 1741–1753 (2017).
[Crossref]

A. Camino, Y. Jia, G. Liu, J. Wang, and D. Huang, “Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography,” Biomed. Opt. Express 8(6), 3053–3066 (2017).
[Crossref]

2016 (4)

Z. Chu, J. Lin, G. Chen, X. Chen, Q. Zhang, C. L. Chen, L. Roisman, G. Gregori, P. J. Rosenfeld, and R. K. Wang, “Quantitative assessment of the retinal microvasculature using optical coherence tomography angiography,” J. Biomed. Opt. 21(6), 066008 (2016).
[Crossref]

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[Crossref]

A. Camino, M. Zhang, C. Dongye, A. D. Pechauer, T. S. Hwang, T. Steven, B. Lujan, D. J. Wilson, D. Huang, and Y. Jia, “Automated registration and enhanced processing of clinical optical coherence tomography angiography,” Quant Imaging Med. Surg. 6(4), 391–401 (2016).
[Crossref]

S. S. Gao, Y. Jia, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT27–OCT36 (2016).
[Crossref]

2015 (2)

L. Liu, Y. Jia, H. L. Takusagawa, A. D. Pechauer, B. Edmunds, L. Lombardi, E. Davis, and J. C. Morrison, “Optical Coherence Tomography Angiography of the Peripapillary Retina in Glaucoma,” JAMA Ophthalmol. 133(9), 1045–1052 (2015).
[Crossref]

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. 112(18), E2395–E2402 (2015).
[Crossref]

2013 (1)

2012 (1)

2006 (1)

M. J. Doughty and T. Naase, “Further analysis of the human spontaneous eye blink rate by a cluster analysis-based approach to categorize individuals with ‘normal’ versus ‘frequent’ eye blink activity,” Eye Contact Lens. 32(6), 294–299 (2006).
[Crossref]

2004 (1)

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref]

Abràmoff, M. D.

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref]

Akil, H.

M. Al-Sheikh, K. G. Falavarjani, H. Akil, and S. R. Sadda, “Impact of image quality on OCT angiography based quantitative measurements,” Int. J. Retin. Vitr. 3(1), 13 (2017).
[Crossref]

Alam, M.

Al-Sheikh, M.

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

M. Al-Sheikh, K. G. Falavarjani, H. Akil, and S. R. Sadda, “Impact of image quality on OCT angiography based quantitative measurements,” Int. J. Retin. Vitr. 3(1), 13 (2017).
[Crossref]

Ang, M.

J. Hong, B. Tan, N. D. Quang, P. Gupta, E. Lin, D. Wong, M. Ang, E. Lamoureux, L. Schmetterer, and J. Chua, “Intra-session repeatability of quantitative metrics using widefield optical coherence tomography angiography (octa) in elderly subjects,” Acta Ophthalmol. (2019).
[Crossref]

Apte, R. S.

B. E. O’Bryhim, R. S. Apte, N. Kung, D. Coble, and G. P. Van Stavern, “Association of Preclinical Alzheimer Disease With Optical Coherence Tomographic Angiography Findings,” JAMA Ophthalmol. 136(11), 1242–1248 (2018).
[Crossref]

Arathorn, D. W.

Arbel, M.

M. Binkowski, D. J. Sutherland, M. Arbel, and A. Gretton, “Demystifying MMD GANs,” in International Conference on Learning Representations, (2018).

Augustin, M.

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv: Learning (2014).

Baghdasaryan, E.

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

Bailey, S. T.

S. S. Gao, Y. Jia, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT27–OCT36 (2016).
[Crossref]

Y. Jia, S. T. Bailey, T. S. Hwang, S. M. McClintic, S. S. Gao, M. E. Pennesi, C. J. Flaxel, A. K. Lauer, D. J. Wilson, J. Hornegger, J. G. Fujimoto, and D. Huang, “Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye,” Proc. Natl. Acad. Sci. 112(18), E2395–E2402 (2015).
[Crossref]

Balasubramanian, S.

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

J. Lei, M. K. Durbin, Y. Shi, A. Uji, S. Balasubramanian, E. Baghdasaryan, M. Al-Sheikh, and S. R. Sadda, “Repeatability and reproducibility of superficial macular retinal vessel density measurements using optical coherence tomography angiography en face images,” JAMA Ophthalmol. 135(10), 1092–1098 (2017).
[Crossref]

Ballard, Z. S.

K. de Haan, Z. S. Ballard, Y. Rivenson, Y. Wu, and A. Ozcan, “Resolution enhancement in scanning electron microscopy using deep learning,” Sci. Rep. 9(1), 12050 (2019).
[Crossref]

Baumann, B.

Baxter, S. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Bengio, Y.

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D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Yang, J.

J. P. Campbell, E. Nudleman, J. Yang, O. Tan, R. V. Chan, M. F. Chiang, D. Huang, and G. Liu, “Handheld optical coherence tomography angiography and ultra-wide-field optical coherence tomography in retinopathy of prematurity,” JAMA Ophthalmol. 135(9), 977–981 (2017).
[Crossref]

Yang, Q.

Yao, X.

Yu, S. Q.

X. X. Li, W. Wu, H. Zhou, J. J. Deng, M. Y. Zhao, T. W. Qian, C. Yan, X. Xu, and S. Q. Yu, “A quantitative comparison of five optical coherence tomography angiography systems in clinical performance,” Int. J. Ophthalmol. 11(11), 1784–1795 (2018).
[Crossref]

Zha, Z.-J.

Y. Li, X. Chen, F. Wu, and Z.-J. Zha, “Linestofacephoto: Face photo generation from lines with conditional self-attention generative adversarial networks,” in Proceedings of the 27th ACM International Conference on Multimedia, (ACM, 2019), pp. 2323–2331.

Zhang, C. L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhang, E. D.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhang, K.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhang, M.

A. Camino, M. Zhang, S. S. Gao, T. S. Hwang, U. Sharma, D. J. Wilson, D. Huang, and Y. Jia, “Evaluation of artifact reduction in optical coherence tomography angiography with real-time tracking and motion correction technology,” Biomed. Opt. Express 7(10), 3905–3915 (2016).
[Crossref]

A. Camino, M. Zhang, C. Dongye, A. D. Pechauer, T. S. Hwang, T. Steven, B. Lujan, D. J. Wilson, D. Huang, and Y. Jia, “Automated registration and enhanced processing of clinical optical coherence tomography angiography,” Quant Imaging Med. Surg. 6(4), 391–401 (2016).
[Crossref]

S. S. Gao, Y. Jia, M. Zhang, J. P. Su, G. Liu, T. S. Hwang, S. T. Bailey, and D. Huang, “Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT27–OCT36 (2016).
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D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
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Zhang, Y.

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Zhao, M. Y.

X. X. Li, W. Wu, H. Zhou, J. J. Deng, M. Y. Zhao, T. W. Qian, C. Yan, X. Xu, and S. Q. Yu, “A quantitative comparison of five optical coherence tomography angiography systems in clinical performance,” Int. J. Ophthalmol. 11(11), 1784–1795 (2018).
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Zhao, Y.

Zheng, F.

H. Jiang, Y. Wei, Y. Shi, C. Wright, X. Sun, G. Gregori, F. Zheng, E. Vanner, B. Lam, T. Rundek, and J. Wang, “Altered Macular Microvasculature in Mild Cognitive Impairment and Alzheimer Disease,” J. Neuro-Ophthalmol. 38(3), 292–298 (2018).
[Crossref]

Zheng, L.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

Zhou, H.

X. X. Li, W. Wu, H. Zhou, J. J. Deng, M. Y. Zhao, T. W. Qian, C. Yan, X. Xu, and S. Q. Yu, “A quantitative comparison of five optical coherence tomography angiography systems in clinical performance,” Int. J. Ophthalmol. 11(11), 1784–1795 (2018).
[Crossref]

Zhu, J.

D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
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D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu, A. Tafreshi, M. A. Lewis, H. Xia, and K. Zhang, “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell 172(5), 1122–1131.e9 (2018).
[Crossref]

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Figures (9)

Fig. 1.
Fig. 1. (a) Relationship between the sampling density (rate) and the A-line speed of the OCT systems. (b) The comparison of $8\times 8$ mm$^2$ and $3\times 3$ mm$^2$ FOV OCTA images.
Fig. 2.
Fig. 2. Proposed workflow of using low-cost OCT systems and deep learning to get high digital resolution OCTA images.
Fig. 3.
Fig. 3. Overview of the framework for the digital resolution enhancement task, there are two generators $G_{AB}$ and $G_{BA}$ and two discriminators $D_{A}$ and $D_{B}$. A denotes the low transverse sampling image domain, B denotes the high-definition image domain, to further regularize the mappings, the network employed three losses: cycle consistency loss, adversarial loss, and identity loss.
Fig. 4.
Fig. 4. Digital resolution enhancement results of the SVP, the zoom-in views of three regions of interest (ROIs) labeled as a, b, c (the red boxes) are demonstrated at the right side of the figure. The tag numbers 1, 2, and 3 refer to the original low transverse sampling image, the GAN-generated image, and the HD OCTA, respectively.
Fig. 5.
Fig. 5. Digital resolution enhancement results of the DCP. (a1-c1) The zoom-in views of interest (ROIs) labeled in the red boxes of original low transverse sampling image. (a2-c2) Demonstrated the corresponding zoom-in region of the GAN-generated image. (a3-c3) Magnified corresponding area at the red boxes of HD OCTA.
Fig. 6.
Fig. 6. Quantifying the changes of retinal vessel caliber. Left: 5 randomly selected positions of the vessels. Right: their intensity profiles. The black, red, and blue ones are from the original, generated, and HD OCTA images, respectively.
Fig. 7.
Fig. 7. Quantify the changes of vessel caliber. Top: the variation of vessel calibers in 25 random positions from the 5 testing data sets. The black, red, and blue lines are from the original, generated, and HD OCTA images, respectively. Bottom: The discrepancies between the original and HD (black) and the generated and HD (red).
Fig. 8.
Fig. 8. Application of the proposed method in unseen diseased cases. The case 1 is from the right eye of a male (age 51), who has branch retinal vein occlusion. The case 2 is from the right eye of a 70-years-old subject (gender unknown), who has diabetic retinopathy. From left to right are the $8\times 8$ mm $^2$ scans for these two cases, the cropped $3\times 3$ mm $^2$ images referring to original, and the outputted images for the deep network referring to generated. The red boxes give the FOVs for the cropping.
Fig. 9.
Fig. 9. Representative OMAG images to calculate the quantitative indexes of the SVP. (a) Original en face OCTA image. (b) Vessel area map is used for VAD, VDI, and VCI quantification. (c) Vessel skeleton map is used for VSD and VDI quantification. (d) Vessel perimeter map is used for VPI and VCI quantification.

Tables (3)

Tables Icon

Table 1. Quantitative comparison of the perceptual similarity measures

Tables Icon

Table 2. Quantitative comparison of the SNR

Tables Icon

Table 3. Comparison of the vascular biomarkers of the SVP.

Equations (7)

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

L ( G A B , G B A , D A , D B ) = L G A N ( G A B , D B , A , B ) + L G A N ( G B A , D A , B , A ) + β L c y c ( G A B , G B A ) + γ L i d e ( G A B , G B A )
min G A B max D B L G A N ( G A B , D B , A , B ) = E i b P d a t a ( b ) [ log D B ( b ) ] + E i b P d a t a ( a ) [ log ( 1 D B ( G A B ( a ) ) ) ]
L c y c ( G A B , G B A ) = E i a P d a t a [ G B A ( G A B ( i a ) ) i a 1 ] + E i b P d a t a ( b ) [ G A B ( G B A ( i b ) ) i b 1 ] .
G B A ( G A B ( i A ) ) i A  and  G A B ( G B A ( i B ) ) i B .
L i d e ( G A B , G B A ) = E i b P d a t a ( b ) [ G A B ( b ) i b 1 ] + E i a P d a t a ( a ) [ G B A ( i a ) i a 1 ] .
S = | W O , G W H D | W H D × 100 % ,
S N R = D ¯ p a r a f o v e a l D ¯ F A Z σ D F A Z ,

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