Abstract

Fovea serves to be one of the crucial landmarks of the retina. The automatic detection of the foveal center in optical coherence tomography (OCT) images helps in diagnosing retinal diseases. However, challenges arise due to retinal structure damage and the demand for high time performance. In this study, we propose a fast and robust fovea detection framework for OCT and OCT angiography (OCTA) images. We focus on detecting the foveal center based on the foveal avascular zone (FAZ) segmentation. Firstly, the proposed framework uses a lightweight neural network to quickly segment the FAZ. Further, the geometric center of the FAZ is identified as the position of the foveal center. We validate the framework’s performance using two datasets. Dataset A contains two modalities of images from 316 subjects. Dataset B contains OCT data of 700 subjects with healthy eyes, choroidal neovascularization, geographic atrophy, and diabetic retinopathy. The Dice score of the FAZ segmentation is 84.68%, which is higher than that of the existing algorithms. The success rate (< 750 µm) and distance error of fovea detection in OCTA images are 100% and 92.3 ± 90.9 µm, respectively, which are better than that in OCT. For different disease situations, our framework is more robust than the existing algorithms and requires an average time of 0.02 s per eye. This framework has the potential to become an efficient and robust clinical tool for fovea detection in OCT images.

© 2020 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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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
  18. J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2014).
    [Crossref]
  19. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Int. Conf. Med. Image Comput. Comput. Interv. (2015).
  20. M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

2018 (4)

P. K. Yu, Z. Mammo, C. Balaratnasingam, and D. Yu, “Quantitative Study of the Macular Microvasculature in Human Donor Eyes,” Invest. Ophthalmol. Visual Sci. 59(1), 108–116 (2018).
[Crossref]

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

L. Giselle, J. S. A. Romo, R. E. Linderman, B. D. Krawitz, S. Mo, A. Zakik, and J. Carroll, “Within-subject assessment of foveal avascular zone enlargement in different stages of diabetic retinopathy using en face OCT reflectance and OCT angiography,” Biomed. Opt. Express 9(12), 5982–5996 (2018).
[Crossref]

2017 (4)

B. Liefers, F. G. Venhuizen, V. Schreur, B. van Ginneken, C. Hoyng, S. Fauser, T. Theelen, and C. I. Sánchez, “Automatic detection of the foveal center in optical coherence tomography,” Biomed. Opt. Express 8(11), 5160 (2017).
[Crossref]

W. D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

A. Montuoro, S. M. Waldstein, B. S. Gerendas, and U. S. Erfurth, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8(3), 1874–1888 (2017).
[Crossref]

S. Niu, Q. Chen, L. de Sisternes, T. Leng, and D. L. Rubin, “Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps,” Med. Phys. 44(12), 6390–6403 (2017).
[Crossref]

2016 (1)

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

2015 (1)

2014 (1)

J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2014).
[Crossref]

2013 (1)

2012 (1)

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

2011 (1)

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: Past, present and future perspectives,” Br. J. Ophthalmol. 95(2), 171–177 (2011).
[Crossref]

2010 (1)

2005 (1)

J. Conrath, R. Giorgi, D. Raccah, and B. Ridings, “Foveal avascular zone in diabetic retinopathy: Quantitative vs qualitative assessment,” Eye 19(3), 322–326 (2005).
[Crossref]

2002 (1)

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Abramoff, M. D.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015, (2015).

Ahmed, M.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Allingham, M. J.

Anvari, P.

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Bagherinia, H.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

Balaratnasingam, C.

P. K. Yu, Z. Mammo, C. Balaratnasingam, and D. Yu, “Quantitative Study of the Macular Microvasculature in Human Donor Eyes,” Invest. Ophthalmol. Visual Sci. 59(1), 108–116 (2018).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Int. Conf. Med. Image Comput. Comput. Interv. (2015).

Calabresi, P. A.

Carass, A.

Carroll, J.

Chen, Q.

S. Niu, Q. Chen, L. de Sisternes, T. Leng, and D. L. Rubin, “Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps,” Med. Phys. 44(12), 6390–6403 (2017).
[Crossref]

Chiu, S. J.

Conrath, J.

J. Conrath, R. Giorgi, D. Raccah, and B. Ridings, “Foveal avascular zone in diabetic retinopathy: Quantitative vs qualitative assessment,” Eye 19(3), 322–326 (2005).
[Crossref]

Cousins, S. W.

Cutrín, P.

M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2014).
[Crossref]

de Sisternes, L.

S. Niu, Q. Chen, L. de Sisternes, T. Leng, and D. L. Rubin, “Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps,” Med. Phys. 44(12), 6390–6403 (2017).
[Crossref]

Díaz, M.

M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

Durbin, M. K.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

Erfurth, U. S.

Erginay, A.

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Falavarjani, K. G.

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Farsiu, S.

Farzaneh, A.

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Fauser, S.

Feras, A.

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Int. Conf. Med. Image Comput. Comput. Interv. (2015).

Gaudric, A.

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Geitzenauer, W.

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: Past, present and future perspectives,” Br. J. Ophthalmol. 95(2), 171–177 (2011).
[Crossref]

Gerendas, B. S.

W. D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

A. Montuoro, S. M. Waldstein, B. S. Gerendas, and U. S. Erfurth, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8(3), 1874–1888 (2017).
[Crossref]

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

Giorgi, R.

J. Conrath, R. Giorgi, D. Raccah, and B. Ridings, “Foveal avascular zone in diabetic retinopathy: Quantitative vs qualitative assessment,” Eye 19(3), 322–326 (2005).
[Crossref]

Giselle, L.

Gómez-Ulla, F.

M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

Gregori, G.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

Hajizadeh, F.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015, (2015).

Haouchine, B.

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Hauser, M.

Hitzenberger, C. K.

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: Past, present and future perspectives,” Br. J. Ophthalmol. 95(2), 171–177 (2011).
[Crossref]

Hoyng, C.

Huang, D.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Hwang, T.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Izatt, J. A.

Jia, Y.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Kafieh, R.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015, (2015).

Krawitz, B. D.

Lang, A.

Langs, G.

W. D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

Leng, T.

S. Niu, Q. Chen, L. de Sisternes, T. Leng, and D. L. Rubin, “Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps,” Med. Phys. 44(12), 6390–6403 (2017).
[Crossref]

Li, D.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Li, X. T.

Liefers, B.

Linderman, R. E.

Long, J.

J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2014).
[Crossref]

Lu, Y.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Lujan, B. J.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

Mammo, Z.

P. K. Yu, Z. Mammo, C. Balaratnasingam, and D. Yu, “Quantitative Study of the Macular Microvasculature in Human Donor Eyes,” Invest. Ophthalmol. Visual Sci. 59(1), 108–116 (2018).
[Crossref]

Massin, P.

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Mehidi, A. B.

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Mettu, P. S.

Mo, S.

Montuoro, A.

A. Montuoro, S. M. Waldstein, B. S. Gerendas, and U. S. Erfurth, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8(3), 1874–1888 (2017).
[Crossref]

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

Naseri, D.

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Nicholas, P.

Niu, S.

S. Niu, Q. Chen, L. de Sisternes, T. Leng, and D. L. Rubin, “Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps,” Med. Phys. 44(12), 6390–6403 (2017).
[Crossref]

Novo, J.

M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

Ortega, M.

M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

Paques, M.

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Pegah, K.

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Penedo, M. G.

M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

Prince, J. L.

Rabbani, H.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015, (2015).

Raccah, D.

J. Conrath, R. Giorgi, D. Raccah, and B. Ridings, “Foveal avascular zone in diabetic retinopathy: Quantitative vs qualitative assessment,” Eye 19(3), 322–326 (2005).
[Crossref]

Ridings, B.

J. Conrath, R. Giorgi, D. Raccah, and B. Ridings, “Foveal avascular zone in diabetic retinopathy: Quantitative vs qualitative assessment,” Eye 19(3), 322–326 (2005).
[Crossref]

Romo, J. S. A.

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Int. Conf. Med. Image Comput. Comput. Interv. (2015).

Rosenfeld, P. J.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

Rubin, D. L.

S. Niu, Q. Chen, L. de Sisternes, T. Leng, and D. L. Rubin, “Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps,” Med. Phys. 44(12), 6390–6403 (2017).
[Crossref]

Sánchez, C. I.

Schmidt-Erfurth, U.

W. D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

Schmidt-Erfurth, U. M.

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: Past, present and future perspectives,” Br. J. Ophthalmol. 95(2), 171–177 (2011).
[Crossref]

Schreur, V.

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2014).
[Crossref]

Shenazandi, H.

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Simonett, J. M.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Sonka, M.

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015, (2015).

Sotirchos, E. S.

Theelen, T.

Toth, C. A.

van Ginneken, B.

Venhuizen, F. G.

Vogl, W. D.

W. D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

Waldstein, S. M.

W. D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

A. Montuoro, S. M. Waldstein, B. S. Gerendas, and U. S. Erfurth, “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context,” Biomed. Opt. Express 8(3), 1874–1888 (2017).
[Crossref]

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

Wang, F.

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

Wang, J.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Wu, J.

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

Ying, H. S.

Yu, D.

P. K. Yu, Z. Mammo, C. Balaratnasingam, and D. Yu, “Quantitative Study of the Macular Microvasculature in Human Donor Eyes,” Invest. Ophthalmol. Visual Sci. 59(1), 108–116 (2018).
[Crossref]

Yu, P. K.

P. K. Yu, Z. Mammo, C. Balaratnasingam, and D. Yu, “Quantitative Study of the Macular Microvasculature in Human Donor Eyes,” Invest. Ophthalmol. Visual Sci. 59(1), 108–116 (2018).
[Crossref]

Zakik, A.

Zhang, M.

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

Biomed. Opt. Express (5)

Br. J. Ophthalmol. (1)

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: Past, present and future perspectives,” Br. J. Ophthalmol. 95(2), 171–177 (2011).
[Crossref]

Eur. J. Ophthalmol. (1)

P. Massin, A. Erginay, B. Haouchine, A. B. Mehidi, M. Paques, and A. Gaudric, “Retinal thickness in healthy and diabetic subjects measured using optical coherence tomography mapping software,” Eur. J. Ophthalmol. 12(2), 102–108 (2002).
[Crossref]

Eye (1)

J. Conrath, R. Giorgi, D. Raccah, and B. Ridings, “Foveal avascular zone in diabetic retinopathy: Quantitative vs qualitative assessment,” Eye 19(3), 322–326 (2005).
[Crossref]

IEEE Trans. Med. Imaging (1)

W. D. Vogl, S. M. Waldstein, B. S. Gerendas, U. Schmidt-Erfurth, and G. Langs, “Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images,” IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2014).
[Crossref]

Int. J. Biomed. Imaging (1)

J. Wu, S. M. Waldstein, A. Montuoro, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease,” Int. J. Biomed. Imaging 2016, 1–9 (2016).
[Crossref]

Invest. Ophthalmol. Visual Sci. (2)

P. K. Yu, Z. Mammo, C. Balaratnasingam, and D. Yu, “Quantitative Study of the Macular Microvasculature in Human Donor Eyes,” Invest. Ophthalmol. Visual Sci. 59(1), 108–116 (2018).
[Crossref]

Y. Lu, J. M. Simonett, J. Wang, M. Zhang, T. Hwang, M. Ahmed, D. Huang, D. Li, and Y. Jia, “Evaluation of Automatically Quantified Foveal Avascular Zone Metrics for Diagnosis of Diabetic Retinopathy Using Optical Coherence Tomography Angiography,” Invest. Ophthalmol. Visual Sci. 59(6), 2212 (2018).
[Crossref]

J. Ophthalmic Vision Res. (1)

K. G. Falavarjani, H. Shenazandi, D. Naseri, P. Anvari, K. Pegah, A. Farzaneh, and A. Feras, “Original Article Foveal Avascular Zone and Vessel Density in Healthy Subjects: An Optical Coherence Tomography Angiography Study,” J. Ophthalmic Vision Res. 13(3), 260–265 (2018).
[Crossref]

Med. Phys. (1)

S. Niu, Q. Chen, L. de Sisternes, T. Leng, and D. L. Rubin, “Automated detection of foveal center in SD-OCT images using the saliency of retinal thickness maps,” Med. Phys. 44(12), 6390–6403 (2017).
[Crossref]

Ophthalmic Surg. Lasers Imaging (1)

F. Wang, G. Gregori, P. J. Rosenfeld, B. J. Lujan, M. K. Durbin, and H. Bagherinia, “Automated detection of the foveal center improves SD-OCT measurements of central retinal thickness,” Ophthalmic Surg. Lasers Imaging 43(6), S32–S37 (2012).
[Crossref]

Opt. Express (1)

Other (3)

R. Kafieh, H. Rabbani, F. Hajizadeh, M. D. Abramoff, and M. Sonka, “Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes,” J. Ophthalmol.2015, (2015).

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Int. Conf. Med. Image Comput. Comput. Interv. (2015).

M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo, and M. Ortega, “Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images,” PLoS One (2018).

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Figures (10)

Fig. 1.
Fig. 1. The anatomy of the fovea and structure of OCT (a), a normal case of the fovea (b) and the challenges of fovea detection: vanished layer boundaries (c), abnormal retinal thickness (d), irregular foveal shape in B-scan (e).
Fig. 2.
Fig. 2. Overview of fovea detection in OCT and OCTA images.
Fig. 3.
Fig. 3. The architecture of the lightweight U-Net for FAZ segmentation.
Fig. 4.
Fig. 4. Network training patterns of the FAZ segmentation in OCTA images (top) and OCT images (bottom).
Fig. 5.
Fig. 5. An example of fovea detection. (a) FAZ segmentation result. (b) OCT projection map. (c) B-scan with fovea.
Fig. 6.
Fig. 6. The ground truth labeling of the FAZ. (a) 3D visualization of OCTA volume. (b) OCTA maximum projection between ILM and OPL. (c) The ground truth of the FAZ.
Fig. 7.
Fig. 7. The ground truth labeling of the foveal center. (a) B-scan image of a healthy retina. (b)-(c) B-scan images of the retina with different diseases. (d) OCT projection map between OPL and BM layer.
Fig. 8.
Fig. 8. Examples of the FAZ segmentation and foveal center detection in OCTA projection maps (a) and OCT projection maps (b). Green line represents the FAZ segmentation result of our network. Red line represents the ground truth of the FAZ region. Green dot represents our foveal center detection result. Red dot represents the experts’ labeling.
Fig. 9.
Fig. 9. Two relatively poor results of the fovea detection in DR (a) (b) and GA (c). (a) and (c) are the OCT projection maps. (b) is the OCTA projection map. The yellow areas represent the FAZ segmentation results. The green circles represent the fovea detection results and the red circles represent the ground truths.
Fig. 10.
Fig. 10. The fovea detection results in five types of fovea shapes. The green line indicates the FAZ segmentation results. The green and red circles indicate the foveal center detection results and the ground truth, respectively. The method performs well in case of A: normal; B: fibrosis; C: GA; D: absent or minor foveal depression; E: large edema.

Tables (6)

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Table 1. The number of volumes with different pathologies in Dataset A and Dataset B.

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Table 2. The accuracy and distance error of our framework for foveal center detection.

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Table 3. Quantitative comparison of different FAZ segmentation methods.

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Table 4. The accuracy and distance error of three methods in different disease cases for foveal center detection.

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Table 5. The distance between our results and manual marking (two experts) for 700 cubes.

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Table 6. The run time of different methods for foveal center detection.

Equations (7)

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

F = | | T ( 1 B ) | | α
P = ( ( x , y ) F x F ( x , y ) ( x , y ) F F ( x , y ) , ( x , y ) F y F ( x , y ) ( x , y ) F F ( x , y ) )
D I C E = 2 T P 2 T P + F P + F N
J A C = T P T P + F P + F N
A C C = T P + T N T P + T N + F P + F N
P R E = T P T P + F P
R E C = T P T P + F N

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