Abstract

As a function of the spatial position of the optical coherence tomography (OCT) image, retinal layer thickness is an important diagnostic indicator for many retinal diseases. Reliable segmentation of the retinal layer is necessary for extracting useful clinical information. However, manual segmentation of these layers is time-consuming and prone to bias. Furthermore, due to speckle noise, low image contrast, retinal detachment, and also irregular morphological features make the automatic segmentation task challenging. To alleviate these challenges, in this paper, we propose a new coarse-fine framework combining the full convolutional network (FCN) with a multiphase level set (named FCN-MLS) for automatic segmentation of nine boundaries in retinal spectral OCT images. In the coarse stage, FCN is used to learn the characteristics of specific retinal layer boundaries and achieve classification of four retinal layers. The boundaries are then extracted and the remaining boundaries are initialized based on a priori information about the thickness of the retinal layer. In order to prevent the overlapping of the segmentation interfaces, a regional restriction technique is used in the multi-phase level to evolve the boundaries to achieve fine nine retinal layers segmentation. Experimental results on 1280 B-scans show that the proposed method can segment nine retinal boundaries accurately. Compared with the manual delineation, the overall mean absolute boundary location difference and the overall mean absolute thickness difference were 5.88 ± 2.38μm and 5.81 ± 2.19μm, which showed a good consistency with manual segmentation by the physicians. Our experimental results also outperform state-of-the-art methods.

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

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References

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2018 (1)

2017 (3)

2016 (3)

2015 (2)

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

2014 (2)

P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
[Crossref] [PubMed]

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

2013 (1)

2012 (2)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 60, 1097–1105 (2012).

S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
[Crossref] [PubMed]

2011 (3)

2010 (5)

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process. 19(12), 3243–3254 (2010).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[Crossref] [PubMed]

M. A. Mayer, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients,” Biomed. Opt. Express 1(5), 1358–1383 (2010).
[Crossref] [PubMed]

2006 (1)

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

2005 (1)

2003 (1)

Abrámoff, M. D.

Abràmoff, M. D.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2(8), 2403–2416 (2011).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

Akkin, T.

Antony, B.

Apostolopoulos, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention(Springer2017), pp. 294–301.
[Crossref]

Arshavsky, V. Y.

Bai, Y.

W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers,” in Proceedings of the 33 rd International Conference on Machine Learning(2016).

Bouma, B. E.

Bowes Rickman, C.

Calabresi, P. A.

Carass, A.

Cense, B.

Chakraborthi, D.

Chan, R.

Chatterjee, J.

Chen, Q.

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

Chen, T.

Chiu, S. J.

Ciller, C.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention(Springer2017), pp. 294–301.
[Crossref]

Conjeti, S.

Cunefare, D.

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

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
[Crossref] [PubMed]

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition(2015), pp. 3431–3440.

de Boer, J.

de Boer, J. F.

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

J. F. de Boer, B. Cense, B. H. Park, M. C. Pierce, G. J. Tearney, and B. E. Bouma, “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography,” Opt. Lett. 28(21), 2067–2069 (2003).
[Crossref] [PubMed]

de Jong, J. H.

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

de Sisternes, L.

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

De Zanet, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention(Springer2017), pp. 294–301.
[Crossref]

DeBuc, D. C.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

Dolejsi, M.

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

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

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
[Crossref] [PubMed]

P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
[Crossref] [PubMed]

S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
[Crossref] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
[Crossref] [PubMed]

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express 18(18), 19413–19428 (2010).
[Crossref] [PubMed]

Fox, M. D.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process. 19(12), 3243–3254 (2010).
[Crossref] [PubMed]

Garvin, M. K.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2(8), 2403–2416 (2011).
[Crossref] [PubMed]

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

Grewal, D. S.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
[Crossref] [PubMed]

Gui, C.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process. 19(12), 3243–3254 (2010).
[Crossref] [PubMed]

Guymer, R. H.

Hamarneh, G.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref] [PubMed]

Hauser, M.

Heflin, S. J.

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 60, 1097–1105 (2012).

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10)(2010), pp. 807–814.

Hornegger, J.

Izatt, J. A.

Jansonius, N. M.

Joo, C.

Karri, S. P.

Karri, S. P. K.

Katouzian, A.

Keller, B.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
[Crossref] [PubMed]

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 60, 1097–1105 (2012).

Kuo, A. N.

Kwon, Y. H.

Lang, A.

LaRocca, F.

Lee, K.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2(8), 2403–2416 (2011).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

Lee, W.-H.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

Lemij, H. G.

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

Leng, T.

Li, C.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process. 19(12), 3243–3254 (2010).
[Crossref] [PubMed]

Li, S.

Li, X. T.

Liu, Q.

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

Long, J.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition(2015), pp. 3431–3440.

Mahmoud, T. H.

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
[Crossref] [PubMed]

Mardin, C. Y.

Mayer, M. A.

McNabb, R. P.

Mujat, M.

Nair, V.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10)(2010), pp. 807–814.

Navab, N.

Nicholas, P.

Niu, S.

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

Novosel, J.

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

Osindero, S.

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

Park, B.

Park, B. H.

Pierce, M. C.

Prince, J. L.

Quellec, G.

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

Ramdas, W. D.

Roy, A. G.

Rubin, D. L.

S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

Rui, Y.

W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers,” in Proceedings of the 33 rd International Conference on Machine Learning(2016).

Sarunic, M. V.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref] [PubMed]

Shah, A.

Sheet, D.

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition(2015), pp. 3431–3440.

Smiddy, W. E.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

Smith, B. R.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref] [PubMed]

Somfai, G. M.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

Sonka, M.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2(8), 2403–2416 (2011).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

Sotirchos, E. S.

Srinivasan, P. P.

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 60, 1097–1105 (2012).

Sznitman, R.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention(Springer2017), pp. 294–301.
[Crossref]

Tang, L.

Tearney, G. J.

Teh, Y.-W.

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

Thepass, G.

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

Tian, J.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

Tornow, R. P.

Toth, C. A.

van Vliet, L. J.

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

Varga, B.

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
[Crossref] [PubMed]

Vermeer, K. A.

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

Vingerling, J. R.

Wachinger, C.

Wang, C.

Wolf, S.

S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention(Springer2017), pp. 294–301.
[Crossref]

Wu, X.

Xiao, T.

W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers,” in Proceedings of the 33 rd International Conference on Machine Learning(2016).

Xu, C.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process. 19(12), 3243–3254 (2010).
[Crossref] [PubMed]

Yang, K.

W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers,” in Proceedings of the 33 rd International Conference on Machine Learning(2016).

Yao, H.

W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers,” in Proceedings of the 33 rd International Conference on Machine Learning(2016).

Yazdanpanah, A.

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref] [PubMed]

Ying, H. S.

Yu, W.

W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers,” in Proceedings of the 33 rd International Conference on Machine Learning(2016).

Zhang, W.

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

Zhou, L.

Ziyuan Wang,

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

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

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 60, 1097–1105 (2012).

Biomed. Opt. Express (11)

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).
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A. Shah, L. Zhou, M. D. Abrámoff, and X. Wu, “Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images,” Biomed. Opt. Express 9(9), 4509–4526 (2018).
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P. P. Srinivasan, S. J. Heflin, J. A. Izatt, V. Y. Arshavsky, and S. Farsiu, “Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology,” Biomed. Opt. Express 5(2), 348–365 (2014).
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S. P. Karri, D. Chakraborthi, and J. Chatterjee, “Learning layer-specific edges for segmenting retinal layers with large deformations,” Biomed. Opt. Express 7(7), 2888–2901 (2016).
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A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4(7), 1133–1152 (2013).
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M. A. Mayer, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients,” Biomed. Opt. Express 1(5), 1358–1383 (2010).
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S. Niu, L. de Sisternes, Q. Chen, T. Leng, and D. L. Rubin, “Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor,” Biomed. Opt. Express 7(2), 581–600 (2016).
[Crossref] [PubMed]

S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
[Crossref] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2(6), 1524–1538 (2011).
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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] [PubMed]

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express 2(8), 2403–2416 (2011).
[Crossref] [PubMed]

Comput. Biol. Med. (1)

S. Niu, Q. Chen, L. de Sisternes, D. L. Rubin, W. Zhang, and Q. Liu, “Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint,” Comput. Biol. Med. 54, 116–128 (2014).
[Crossref] [PubMed]

IEEE Rev. Biomed. Eng. (1)

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

IEEE Trans. Image Process. (1)

C. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process. 19(12), 3243–3254 (2010).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (3)

A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref] [PubMed]

J. Novosel, K. A. Vermeer, J. H. de Jong, Ziyuan Wang, and L. J. van Vliet, “Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas,” IEEE Trans. Med. Imaging 36(6), 1276–1286 (2017).
[Crossref] [PubMed]

G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
[Crossref] [PubMed]

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B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21(7), 076015 (2016).
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J. Novosel, G. Thepass, H. G. Lemij, J. F. de Boer, K. A. Vermeer, and L. J. van Vliet, “Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography,” Med. Image Anal. 26(1), 146–158 (2015).
[Crossref] [PubMed]

Neural Comput. (1)

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput. 18(7), 1527–1554 (2006).
[Crossref] [PubMed]

Opt. Express (2)

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PLoS One (1)

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PLoS One 10(8), e0133908 (2015).
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K. McDonough, I. Kolmanovsky, and I. V. Glybina, “A neural network approach to retinal layer boundary identification from optical coherence tomography images,” in 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)(IEEE2015), pp. 1–8.
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G. E. Hinton, and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” science 313, 504–507 (2006).
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S. Apostolopoulos, S. De Zanet, C. Ciller, S. Wolf, and R. Sznitman, “Pathological OCT retinal layer segmentation using branch residual u-shape networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention(Springer2017), pp. 294–301.
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A. Ben-Cohen, D. Mark, I. Kovler, D. Zur, A. Barak, M. Iglicki, and R. Soferman, “Retinal layers segmentation using fully convolutional network in OCT images,” RSIP Vision (2017).

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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition(2015), pp. 3431–3440.

W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao, and Y. Rui, “Visualizing and comparing AlexNet and VGG using deconvolutional layers,” in Proceedings of the 33 rd International Conference on Machine Learning(2016).

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10)(2010), pp. 807–814.

C. Li, C. Xu, C. Gui, and M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (IEEE2005), pp. 430–436.

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Figures (10)

Fig. 1
Fig. 1 B-scans of OCT retina. (a) A B-scan of OCT normal retina. (b) A B-scan of OCT retina with CSC.
Fig. 2
Fig. 2 Flowchart of the proposed method.
Fig. 3
Fig. 3 Schematic diagram of level set function initialization and its projection in a two-dimensional plane.
Fig. 4
Fig. 4 Schematic diagram of the proposed level set function initialization and its projection in a two-dimensional plane. (The blue area represents the positive horizontal level of the level set function and the red area represents the negative level of the level set function.)
Fig. 5
Fig. 5 Principle of regional restriction.
Fig. 6
Fig. 6 Automatic surface detection. (a)layer classification through FCN; (b) Initialization interface; (c) Remainder layer initialization based on retinal layer thickness.
Fig. 7
Fig. 7 The first row (a), (b), (c) are the result of the FCN network full classification; and the second row (d), (e), (f) are the result of the interface we need to be classified by the FCN network.
Fig. 8
Fig. 8 (a), (b), (c), (d),(e) are the comparisons among the method proposed by Chiu et al. [6], OCT-Explore [34,35], Roy et al. [24], our proposed method and manual segmentation result.
Fig. 9
Fig. 9 (a), (b), (c), (d), (e), (f), (g), (h), (i) are the boundary positions maps of a cube respectively; and (j) is the total thickness map.
Fig. 10
Fig. 10 The 3-D surfaces visualization of a cube.

Tables (4)

Tables Icon

Table 1 Mean absolute boundary positioning differences (mean ± SD) calculated from the abnormal images with CSC in micrometers.

Tables Icon

Table 2 Mean absolute boundary positioning differences (mean ± SD) calculated from the normal images in micrometers.

Tables Icon

Table 3 Mean absolute thickness differences (mean ± SD) calculated from the abnormal images with CSC in micrometers.

Tables Icon

Table 4 Mean absolute thickness differences (mean ± SD) calculated from the normal eye’s images in micrometers.

Equations (11)

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

L log = i , j ω ( x i , j ) y l ( x i , j ) log p l ( x i , j )
ε ( ϕ ) = μ R ( ϕ ) + α L ( ϕ ) + β S ( ϕ )
L ( ϕ ) = Δ Ω g δ ( ϕ ) | ϕ | d x
S ( ϕ ) = Δ Ω gH ( ϕ ) dx
g = Δ 1 1 + | G g * I | 2
δ ( η ) = { 1 2 ε [ 1 + cos ( π η ε ) ] , | η | ε 0 , | η | > ε
H ( ϕ ) = { 1 2 ( 1 + η ε + 1 π sin ( π η ε ) ) , | η | ε 1 , 0 , η > ε η < ε
m a b d = 1 N i = 1 N 1 512 | C b 1 1 C b 2 i |
s d = 1 N i = 1 N ( 1 512 | C b 1 i C b 2 i | m a b d )
m a h d = 1 N i = 1 N ( 1 512 | l j 1 i l j 2 i | )
I j 1 8 = C b j C b j 1

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