Abstract

The manual segmentation of individual retinal layers within optical coherence tomography (OCT) images is a time-consuming task and is prone to errors. The investigation into automatic segmentation methods that are both efficient and accurate has seen a variety of methods proposed. In particular, recent machine learning approaches have focused on the use of convolutional neural networks (CNNs). Traditionally applied to sequential data, recurrent neural networks (RNNs) have recently demonstrated success in the area of image analysis, primarily due to their usefulness to extract temporal features from sequences of images or volumetric data. However, their potential use in OCT retinal layer segmentation has not previously been reported, and their direct application for extracting spatial features from individual 2D images has been limited. This paper proposes the use of a recurrent neural network trained as a patch-based image classifier (retinal boundary classifier) with a graph search (RNN-GS) to segment seven retinal layer boundaries in OCT images from healthy children and three retinal layer boundaries in OCT images from patients with age-related macular degeneration (AMD). The optimal architecture configuration to maximize classification performance is explored. The results demonstrate that a RNN is a viable alternative to a CNN for image classification tasks in the case where the images exhibit a clear sequential structure. Compared to a CNN, the RNN showed a slightly superior average generalization classification accuracy. Secondly, in terms of segmentation, the RNN-GS performed competitively against a previously proposed CNN based method (CNN-GS) with respect to both accuracy and consistency. These findings apply to both normal and AMD data. Overall, the RNN-GS method yielded superior mean absolute errors in terms of the boundary position with an average error of 0.53 pixels (normal) and 1.17 pixels (AMD). The methodology and results described in this paper may assist the future investigation of techniques within the area of OCT retinal segmentation and highlight the potential of RNN methods for OCT image analysis.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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  1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
    [Crossref] [PubMed]
  2. J. F. de Boer, R. Leitgeb, and M. Wojtkowski, “Twenty-five years of optical coherence tomography: the paradigm shift in sensitivity and speed provided by Fourier domain OCT,” Biomed. Opt. Express 8(7), 3248–3280 (2017).
    [Crossref] [PubMed]
  3. M. Adhi and J. S. Duker, “Optical coherence tomography - current and future applications,” Curr. Opin. Ophthalmol. 24(3), 213–221 (2013).
    [Crossref] [PubMed]
  4. X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
    [Crossref] [PubMed]
  5. P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
    [Crossref] [PubMed]
  6. P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
    [Crossref] [PubMed]
  7. C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
    [Crossref] [PubMed]
  8. J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
    [Crossref] [PubMed]
  9. D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
    [Crossref] [PubMed]
  10. J. Oliveira, S. Pereira, L. Gonçalves, M. Ferreira, and C. A. Silva, “Multi-surface segmentation of OCT images with AMD using sparse high order potentials,” Biomed. Opt. Express 8(1), 281–297 (2017).
    [Crossref] [PubMed]
  11. D. Cabrera Fernández, H. M. Salinas, and C. A. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express 13(25), 10200–10216 (2005).
    [Crossref] [PubMed]
  12. R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
    [Crossref] [PubMed]
  13. 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]
  14. 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]
  15. S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
    [Crossref] [PubMed]
  16. K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - a Graph-Theoretic Approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
    [Crossref] [PubMed]
  17. 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]
  18. 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]
  19. K. McDonough, I. Kolmanovsky, and I. V. Glybina, “A neural network approach to retinal layer boundary indentification from optical coherence tomography images,” in Proceedings of 2015 IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE, 2015), 1–8.
  20. 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]
  21. J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
    [Crossref] [PubMed]
  22. 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] [PubMed]
  23. 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]
  24. Y. Xu, K. Yan, J. Kim, X. Wang, C. Li, L. Su, S. Yu, X. Xu, and D. D. Feng, “Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy,” Biomed. Opt. Express 8(9), 4061–4076 (2017).
    [Crossref] [PubMed]
  25. 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.
  26. F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
    [Crossref] [PubMed]
  27. M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), 177–184.
  28. X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
    [Crossref]
  29. A. Shah, M. D. Abramoff, and X. Wu, “Simultaneous multiple surface segmentation using deep learning,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), 3–11.
  30. O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention (Springer, 2016), 424–432.
  31. 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).
    [Crossref] [PubMed]
  32. T. Mikolov, M. Karafiát, L. Burget, J. Cernocky, and S. Khudanpur, “Recurrent neural network based language model,” in Proceedings of Interspeech (ISCA, 2011), 1045–1048.
  33. B. Zhang, D. Xiong, and J. Su, “Recurrent Neural Machine Translation,” arXiv preprint arXiv:1607.08725 (2016).
  34. A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
    [Crossref] [PubMed]
  35. A. Graves, “Generating Sequence With Recurrent Neural Networks,” arXiv preprint arXiv:1308.0850 (2013).
  36. A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2013), 6645–6649.
    [Crossref]
  37. A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in Proceedings of the 31st International Conference on Machine Learning , Volume 32 (JMLR.org, 2014), 1764–1772.
  38. F. Visin, K. Kastner, K. Cho, M. Matteucci, A. C. Courville, and Y. Bengio, “ReNet: A recurrent neural network based alternative to convolutional networks,” arXiv preprint arXiv:1505.00393 (2015).
  39. F. Visin, M. Ciccone, A. Romero, K. Kastner, K. Cho, Y. Bengio, M. Matteucci, and A. Courville, “ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation,” arXiv preprint arXiv:1511.07053 (2016).
  40. A. Graves, S. Fernandez, and J. Schmidhuber, “Multi-Dimensional Recurrent Neural Networks,” in Artifical Neural Networks – ICANN 2007, J. M. de Sá, L. A. Alexandre, W. Duch, and D. P. Mandic (Eds.) (Springer, 2007), 549–558.
  41. X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.
  42. J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen, “Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (Curran Associates Inc., 2016), 3044–3052.
  43. H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
    [Crossref]
  44. B. Kong, Y. Zhan, M. Shin, T. Denny, and S. Zhang, “Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 264–272.
    [Crossref]
  45. M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber, “Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation,” in Proceedings of the 28th International Conference on Neural Information Processing Systems, Volume 2 (MIT Press, 2015), 2998–3006.
  46. C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press
  47. Y. Xie, Z. Zhang, M. Sapkota, and L. Yang, “Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 185–193.
  48. J. Koutník, K. Greff, F. Gomez, and J. Schmidhuber, “A Clockwork RNN,” in Proceedings of the 31st International Conference on Machine Learning, Volume 32 (JMLR.org, 2014), 1863–1871.
  49. S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
    [Crossref] [PubMed]
  50. S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
    [Crossref] [PubMed]
  51. S. A. Read, M. J. Collins, and S. J. Vincent, “Light exposure and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(11), 6779–6787 (2015).
    [Crossref] [PubMed]
  52. S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
    [Crossref] [PubMed]
  53. S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
    [Crossref] [PubMed]
  54. M. C. Mozer, “A focused backpropagation algorithm for temporal pattern recognition,” Complex Syst. 3(4), 349–381 (1989).
  55. P. J. Werbos, “Generalization of backpropagation with application to a recurrent gas market model,” Neural Netw. 1(4), 339–356 (1988).
    [Crossref]
  56. P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proc. IEEE 78(10), 1550–1560 (1990).
    [Crossref]
  57. S. Hochreiter and J. Schmidhuber, “Long Short-term Memory,” Neural Comput. 9(8), 1735–1780 (1997).
    [Crossref] [PubMed]
  58. H. Salehinejad, S. Sankar, J. Barfett, E. Colak, and S. Valaee, “Recent Advances in Recurrent Neural Networks,” arXiv preprint arXiv:1801.01078 (2018).
  59. R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training Recurrent Neural Networks,” in Proceedings of the 30th International Conference on Machine Learning, Volume 28 (JMLR.org, 2013), 1310–1318.
  60. K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
    [Crossref]
  61. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).
  62. S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proceedings of the 32nd International Conference on Machine Learning – Volume 37 (JMLR.org, 2015), 448–456.
  63. D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv preprint arXiv:1412.6980 (2017).
  64. K. Janocha and W. M. Czarnecki, “On Loss Functions for Deep Neural Networks in Classification,” arXiv preprint arXiv:1702.05659 (2017).
  65. L. Prechelt, “Early Stopping - But When?” in Neural Networks: Tricks of the Trade, G. B. Orr, O. R. Muller (Eds.) (Springer-Verlag, 1998).
  66. R. Caruana, S. Lawrence, and L. Giles, “Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping,” in Proceedings of the 13th International Conference on Neural Information Processing SystemsT. K. Leen, T. G. Dietterich, and V. Tresp, eds. (MIT Press, 2000), 381–387.
  67. F. Chollet, “Keras” https://github.com/fchollet/keras .
  68. Tensorflow white paper, “Tensorflow: Large-scale machine learning on heterogeneous systems” https://tensorflow.org
  69. E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1(1), 269–271 (1959).
    [Crossref]
  70. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” arXiv preprint arXiv:1505.04597 (2015).

2018 (2)

2017 (8)

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]

Y. Xu, K. Yan, J. Kim, X. Wang, C. Li, L. Su, S. Yu, X. Xu, and D. D. Feng, “Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy,” Biomed. Opt. Express 8(9), 4061–4076 (2017).
[Crossref] [PubMed]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
[Crossref] [PubMed]

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
[Crossref] [PubMed]

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

J. F. de Boer, R. Leitgeb, and M. Wojtkowski, “Twenty-five years of optical coherence tomography: the paradigm shift in sensitivity and speed provided by Fourier domain OCT,” Biomed. Opt. Express 8(7), 3248–3280 (2017).
[Crossref] [PubMed]

J. Oliveira, S. Pereira, L. Gonçalves, M. Ferreira, and C. A. Silva, “Multi-surface segmentation of OCT images with AMD using sparse high order potentials,” Biomed. Opt. Express 8(1), 281–297 (2017).
[Crossref] [PubMed]

2016 (3)

2015 (5)

S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, and S. J. Vincent, “Light exposure and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(11), 6779–6787 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
[Crossref] [PubMed]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
[Crossref] [PubMed]

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]

2014 (3)

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. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

2013 (2)

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

M. Adhi and J. S. Duker, “Optical coherence tomography - current and future applications,” Curr. Opin. Ophthalmol. 24(3), 213–221 (2013).
[Crossref] [PubMed]

2012 (1)

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

2011 (1)

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

2010 (1)

2009 (2)

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

2006 (1)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - a Graph-Theoretic Approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

2005 (1)

2001 (1)

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
[Crossref] [PubMed]

1997 (1)

S. Hochreiter and J. Schmidhuber, “Long Short-term Memory,” Neural Comput. 9(8), 1735–1780 (1997).
[Crossref] [PubMed]

1995 (1)

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

1990 (1)

P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proc. IEEE 78(10), 1550–1560 (1990).
[Crossref]

1989 (1)

M. C. Mozer, “A focused backpropagation algorithm for temporal pattern recognition,” Complex Syst. 3(4), 349–381 (1989).

1988 (1)

P. J. Werbos, “Generalization of backpropagation with application to a recurrent gas market model,” Neural Netw. 1(4), 339–356 (1988).
[Crossref]

1959 (1)

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1(1), 269–271 (1959).
[Crossref]

Abramoff, M. D.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Abràmoff, M. D.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

Adhi, M.

M. Adhi and J. S. Duker, “Optical coherence tomography - current and future applications,” Curr. Opin. Ophthalmol. 24(3), 213–221 (2013).
[Crossref] [PubMed]

Ahlers, C.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

Alasil, T.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

Alber, M.

J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen, “Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (Curran Associates Inc., 2016), 3044–3052.

Allingham, M. J.

Alonso-Caneiro, D.

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
[Crossref] [PubMed]

Arshavsky, V. Y.

Bahdanau, D.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

Bavinger, J. C.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Bengio, Y.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

Bertolami, R.

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

Bi, H.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Blachley, T. S.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Bougares, F.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

Boyer, K.

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
[Crossref] [PubMed]

Bunke, H.

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

Byeon, W.

M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber, “Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation,” in Proceedings of the 28th International Conference on Neural Information Processing Systems, Volume 2 (MIT Press, 2015), 2998–3006.

Caballero, J.

C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press

Cabrera Fernández, D.

Caruana, R.

R. Caruana, S. Lawrence, and L. Giles, “Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping,” in Proceedings of the 13th International Conference on Neural Information Processing SystemsT. K. Leen, T. G. Dietterich, and V. Tresp, eds. (MIT Press, 2000), 381–387.

Chakraborthi, D.

Chang, K. T.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Chatterjee, J.

Chen, D. Z.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - a Graph-Theoretic Approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen, “Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (Curran Associates Inc., 2016), 3044–3052.

Chen, H.

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

Chen, J.

J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen, “Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (Curran Associates Inc., 2016), 3044–3052.

Chen, Q.

Chen, X.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

Chen, Z.

X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.

Cheng, J.

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

Chiu, S. J.

Cho, K.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

Collins, M. J.

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, and S. J. Vincent, “Light exposure and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(11), 6779–6787 (2015).
[Crossref] [PubMed]

Conjeti, S.

Cousins, S. W.

Cunefare, D.

de Boer, J. F.

de Sisternes, L.

Deak, G.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

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]

Denny, T.

B. Kong, Y. Zhan, M. Shin, T. Denny, and S. Zhang, “Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 264–272.
[Crossref]

Dijkstra, E. W.

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1(1), 269–271 (1959).
[Crossref]

Dou, Q.

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

Duker, J. S.

M. Adhi and J. S. Duker, “Optical coherence tomography - current and future applications,” Curr. Opin. Ophthalmol. 24(3), 213–221 (2013).
[Crossref] [PubMed]

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

Dunbar, G. E.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Fang, L.

Farsiu, S.

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] [PubMed]

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

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

S. J. Chiu, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
[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. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
[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]

Fauser, S.

Feng, D. D.

Fernández, S.

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

Ferreira, M.

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Folgar, F. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

Fujimoto, J. G.

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Gardner, T. W.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Giles, L.

R. Caruana, S. Lawrence, and L. Giles, “Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping,” in Proceedings of the 13th International Conference on Neural Information Processing SystemsT. K. Leen, T. G. Dietterich, and V. Tresp, eds. (MIT Press, 2000), 381–387.

Glybina, I. V.

K. McDonough, I. Kolmanovsky, and I. V. Glybina, “A neural network approach to retinal layer boundary indentification from optical coherence tomography images,” in Proceedings of 2015 IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE, 2015), 1–8.

Gonçalves, L.

Graves, A.

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2013), 6645–6649.
[Crossref]

Gregory, K.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Gulcehre, C.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

Guymer, R. H.

Hajnal, J. V.

C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press

Hamwood, J.

Hee, M. R.

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Heflin, S. J.

Heng, P.

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

Hinton, G.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2013), 6645–6649.
[Crossref]

Hochreiter, S.

S. Hochreiter and J. Schmidhuber, “Long Short-term Memory,” Neural Comput. 9(8), 1735–1780 (1997).
[Crossref] [PubMed]

Hoyng, C.

Huang, D.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Izatt, J. A.

Jackson, G. R.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Jaffe, G. J.

Jivrajka, R. V.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

Kafieh, R.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Karri, S. P.

Karri, S. P. K.

Katouzian, A.

Keane, P. A.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

Kim, J.

Kolmanovsky, I.

K. McDonough, I. Kolmanovsky, and I. V. Glybina, “A neural network approach to retinal layer boundary indentification from optical coherence tomography images,” in Proceedings of 2015 IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE, 2015), 1–8.

Kong, B.

B. Kong, Y. Zhan, M. Shin, T. Denny, and S. Zhang, “Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 264–272.
[Crossref]

Koozekanani, D.

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
[Crossref] [PubMed]

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Kwark, L.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Lawrence, S.

R. Caruana, S. Lawrence, and L. Giles, “Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping,” in Proceedings of the 13th International Conference on Neural Information Processing SystemsT. K. Leen, T. G. Dietterich, and V. Tresp, eds. (MIT Press, 2000), 381–387.

Lee, K.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[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]

Leitgeb, R.

Leng, T.

Li, C.

Li, K.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - a Graph-Theoretic Approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Li, 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]

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

Li, X. T.

Liakopoulos, S.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

Liefers, B.

Lin, C. P.

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Liwicki, M.

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber, “Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation,” in Proceedings of the 28th International Conference on Neural Information Processing Systems, Volume 2 (MIT Press, 2015), 2998–3006.

Loo, J.

Malamos, P.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

McDonough, K.

K. McDonough, I. Kolmanovsky, and I. V. Glybina, “A neural network approach to retinal layer boundary indentification from optical coherence tomography images,” in Proceedings of 2015 IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE, 2015), 1–8.

Mettu, P. S.

Mohamed, A.

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2013), 6645–6649.
[Crossref]

Mozer, M. C.

M. C. Mozer, “A focused backpropagation algorithm for temporal pattern recognition,” Complex Syst. 3(4), 349–381 (1989).

Mylonas, G.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

Navab, N.

Ni, D.

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

Nicholas, P.

Niemeijer, M.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

Niu, S.

O’Connell, R. V.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

Oliveira, J.

Pan, X.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Pereira, S.

Price, A. N.

C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press

Puliafito, C. A.

D. Cabrera Fernández, H. M. Salinas, and C. A. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express 13(25), 10200–10216 (2005).
[Crossref] [PubMed]

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Qin, C.

C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press

Qin, J.

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

Rabbani, H.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Read, S. A.

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, and S. J. Vincent, “Light exposure and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(11), 6779–6787 (2015).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
[Crossref] [PubMed]

Reichel, E.

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

Ritter, M.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

Roberts, C.

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
[Crossref] [PubMed]

Roy, A. G.

Rubin, D. L.

Rueckert, D.

C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press

Sacu, S.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

Sadda, S. R.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Salinas, H. M.

Sánchez, C. I.

Schlemper, J.

C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press

Schmidhuber, J.

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

S. Hochreiter and J. Schmidhuber, “Long Short-term Memory,” Neural Comput. 9(8), 1735–1780 (1997).
[Crossref] [PubMed]

M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber, “Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation,” in Proceedings of the 28th International Conference on Neural Information Processing Systems, Volume 2 (MIT Press, 2015), 2998–3006.

Schmidt-Erfurth, U.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

Schuman, J. S.

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Schütze, C.

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

Schwenk, H.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

Sheet, D.

Shi, X.

X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.

Shin, M.

B. Kong, Y. Zhan, M. Shin, T. Denny, and S. Zhang, “Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 264–272.
[Crossref]

Silva, C. A.

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]

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.

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - a Graph-Theoretic Approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Srinivasan, P. P.

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Stem, M. S.

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

Stinson, W. G.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Stollenga, M. F.

M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber, “Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation,” in Proceedings of the 28th International Conference on Neural Information Processing Systems, Volume 2 (MIT Press, 2015), 2998–3006.

Su, L.

Sui, X.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Swanson, E. A.

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Theelen, T.

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]

Toth, C. A.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
[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]

van Ginneken, B.

van Grinsven, M. J. J. P.

van Merrienboer, B.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

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]

Venhuizen, F. G.

Vincent, S. J.

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, and S. J. Vincent, “Light exposure and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(11), 6779–6787 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
[Crossref] [PubMed]

Wachinger, C.

Walsh, A. C.

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

Wang, C.

Wang, H.

X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.

Wang, X.

Wei, B.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Werbos, P. J.

P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proc. IEEE 78(10), 1550–1560 (1990).
[Crossref]

P. J. Werbos, “Generalization of backpropagation with application to a recurrent gas market model,” Neural Netw. 1(4), 339–356 (1988).
[Crossref]

Wojtkowski, M.

Wong, W.

X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.

Woo, W.

X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.

Wu, J.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Wu, X.

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - a Graph-Theoretic Approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

Xu, X.

Xu, Y.

Yan, K.

Yang, L.

J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen, “Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (Curran Associates Inc., 2016), 3044–3052.

Yeung, D.

X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.

Yin, Y.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Yu, S.

Yuan, E.

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

Zhan, Y.

B. Kong, Y. Zhan, M. Shin, T. Denny, and S. Zhang, “Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 264–272.
[Crossref]

Zhang, L.

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

Zhang, S.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

B. Kong, Y. Zhan, M. Shin, T. Denny, and S. Zhang, “Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 264–272.
[Crossref]

Zhang, Y.

J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen, “Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (Curran Associates Inc., 2016), 3044–3052.

Zheng, Y.

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Biomed. Opt. Express (12)

J. F. de Boer, R. Leitgeb, and M. Wojtkowski, “Twenty-five years of optical coherence tomography: the paradigm shift in sensitivity and speed provided by Fourier domain OCT,” Biomed. Opt. Express 8(7), 3248–3280 (2017).
[Crossref] [PubMed]

J. Oliveira, S. Pereira, L. Gonçalves, M. Ferreira, and C. A. Silva, “Multi-surface segmentation of OCT images with AMD using sparse high order potentials,” Biomed. Opt. Express 8(1), 281–297 (2017).
[Crossref] [PubMed]

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, M. J. Allingham, P. S. Mettu, S. W. Cousins, J. A. Izatt, and S. Farsiu, “Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema,” Biomed. Opt. Express 6(4), 1172–1194 (2015).
[Crossref] [PubMed]

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

J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref] [PubMed]

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] [PubMed]

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]

Y. Xu, K. Yan, J. Kim, X. Wang, C. Li, L. Su, S. Yu, X. Xu, and D. D. Feng, “Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy,” Biomed. Opt. Express 8(9), 4061–4076 (2017).
[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]

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

Complex Syst. (1)

M. C. Mozer, “A focused backpropagation algorithm for temporal pattern recognition,” Complex Syst. 3(4), 349–381 (1989).

Curr. Opin. Ophthalmol. (1)

M. Adhi and J. S. Duker, “Optical coherence tomography - current and future applications,” Curr. Opin. Ophthalmol. 24(3), 213–221 (2013).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

X. Chen, M. Niemeijer, L. Zhang, K. Lee, M. D. Abràmoff, and M. Sonka, “3D Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search–Graph-Cut,” IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012).
[Crossref] [PubMed]

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal Surface Segmentation in Volumetric Images - a Graph-Theoretic Approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist system for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009).
[Crossref] [PubMed]

Invest. Ophthalmol. Vis. Sci. (4)

J. C. Bavinger, G. E. Dunbar, M. S. Stem, T. S. Blachley, L. Kwark, S. Farsiu, G. R. Jackson, and T. W. Gardner, “The Effects of Diabetic Retinopathy and Pan-Retinal Photocoagulation on Photoreceptor Cell Function as Assessed by Dark Adaptometry,” Invest. Ophthalmol. Vis. Sci. 57(1), 208–217 (2016).
[Crossref] [PubMed]

P. A. Keane, S. Liakopoulos, R. V. Jivrajka, K. T. Chang, T. Alasil, A. C. Walsh, and S. R. Sadda, “Evaluation of Optical Coherence Tomography Retinal Thickness Parameters for Use in Clinical Trials for Neovascular Age-Related Macular Degeneration,” Invest. Ophthalmol. Vis. Sci. 50(7), 3378–3385 (2009).
[Crossref] [PubMed]

S. A. Read, D. Alonso-Caneiro, S. J. Vincent, and M. J. Collins, “Longitudinal changes in choroidal thickness and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(5), 3103–3112 (2015).
[Crossref] [PubMed]

S. A. Read, M. J. Collins, and S. J. Vincent, “Light exposure and eye growth in childhood,” Invest. Ophthalmol. Vis. Sci. 56(11), 6779–6787 (2015).
[Crossref] [PubMed]

J. Mach. Learn. Res. (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Journal of Neurocomputing (1)

X. Sui, Y. Zheng, B. Wei, H. Bi, J. Wu, X. Pan, Y. Yin, and S. Zhang, “Choroid segmentation from optical coherence tomography with graph edge weights learned from deep convolutional neural networks,” Journal of Neurocomputing 237, 332–341 (2017).
[Crossref]

Med. Image Anal. (1)

R. Kafieh, H. Rabbani, M. D. Abramoff, and M. Sonka, “Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map,” Med. Image Anal. 17(8), 907–928 (2013).
[Crossref] [PubMed]

Neural Comput. (1)

S. Hochreiter and J. Schmidhuber, “Long Short-term Memory,” Neural Comput. 9(8), 1735–1780 (1997).
[Crossref] [PubMed]

Neural Netw. (1)

P. J. Werbos, “Generalization of backpropagation with application to a recurrent gas market model,” Neural Netw. 1(4), 339–356 (1988).
[Crossref]

Numer. Math. (1)

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numer. Math. 1(1), 269–271 (1959).
[Crossref]

Ophthalmology (2)

S. Farsiu, S. J. Chiu, R. V. O’Connell, F. A. Folgar, E. Yuan, J. A. Izatt, and C. A. Toth, “Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography,” Ophthalmology 121(1), 162–172 (2014).
[Crossref] [PubMed]

C. A. Puliafito, M. R. Hee, C. P. Lin, E. Reichel, J. S. Schuman, J. S. Duker, J. A. Izatt, E. A. Swanson, and J. G. Fujimoto, “Imaging of macular diseases with optical coherence tomography,” Ophthalmology 102(2), 217–229 (1995).
[Crossref] [PubMed]

Opt. Express (2)

PLoS One (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]

S. A. Read, D. Alonso-Caneiro, and S. J. Vincent, “Longitudinal changes in macular retinal layer thickness in pediatric populations: Myopic vs non-myopic eyes,” PLoS One 12(6), e0180462 (2017).
[Crossref] [PubMed]

Proc. IEEE (1)

P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proc. IEEE 78(10), 1550–1560 (1990).
[Crossref]

Retina (2)

S. A. Read, M. J. Collins, S. J. Vincent, and D. Alonso-Caneiro, “Macular retinal layer thickness in childhood,” Retina 35(6), 1223–1233 (2015).
[Crossref] [PubMed]

P. Malamos, C. Ahlers, G. Mylonas, C. Schütze, G. Deak, M. Ritter, S. Sacu, and U. Schmidt-Erfurth, “Evaluation of segmentation procedures using spectral domain optical coherence tomography in exudative age-related macular degeneration,” Retina 31(3), 453–463 (2011).
[Crossref] [PubMed]

Science (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Other (32)

A. Graves, “Generating Sequence With Recurrent Neural Networks,” arXiv preprint arXiv:1308.0850 (2013).

A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 2013), 6645–6649.
[Crossref]

A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” in Proceedings of the 31st International Conference on Machine Learning , Volume 32 (JMLR.org, 2014), 1764–1772.

F. Visin, K. Kastner, K. Cho, M. Matteucci, A. C. Courville, and Y. Bengio, “ReNet: A recurrent neural network based alternative to convolutional networks,” arXiv preprint arXiv:1505.00393 (2015).

F. Visin, M. Ciccone, A. Romero, K. Kastner, K. Cho, Y. Bengio, M. Matteucci, and A. Courville, “ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation,” arXiv preprint arXiv:1511.07053 (2016).

A. Graves, S. Fernandez, and J. Schmidhuber, “Multi-Dimensional Recurrent Neural Networks,” in Artifical Neural Networks – ICANN 2007, J. M. de Sá, L. A. Alexandre, W. Duch, and D. P. Mandic (Eds.) (Springer, 2007), 549–558.

X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems (MIT Press, 2015), 802–810.

J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen, “Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation,” in Proceedings of the 30th International Conference on Neural Information Processing Systems (Curran Associates Inc., 2016), 3044–3052.

H. Chen, Q. Dou, D. Ni, J. Cheng, J. Qin, S. Li, and P. Heng, “Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks,” in Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2015), 507–514.
[Crossref]

B. Kong, Y. Zhan, M. Shin, T. Denny, and S. Zhang, “Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network,” in Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 264–272.
[Crossref]

M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber, “Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation,” in Proceedings of the 28th International Conference on Neural Information Processing Systems, Volume 2 (MIT Press, 2015), 2998–3006.

C. Qin, J. V. Hajnal, D. Rueckert, J. Schlemper, J. Caballero, and A. N. Price, “Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction,” IEEE Trans. Med. Imaging. in press

Y. Xie, Z. Zhang, M. Sapkota, and L. Yang, “Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Springer, 2016), 185–193.

J. Koutník, K. Greff, F. Gomez, and J. Schmidhuber, “A Clockwork RNN,” in Proceedings of the 31st International Conference on Machine Learning, Volume 32 (JMLR.org, 2014), 1863–1871.

M. Chen, J. Wang, I. Oguz, B. L. VanderBeek, and J. C. Gee, “Automated segmentation of the choroid in edi-oct images with retinal pathology using convolution neural networks,” in Fetal, Infant and Ophthalmic Medical Image Analysis (Springer, 2017), 177–184.

K. McDonough, I. Kolmanovsky, and I. V. Glybina, “A neural network approach to retinal layer boundary indentification from optical coherence tomography images,” in Proceedings of 2015 IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE, 2015), 1–8.

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.

A. Shah, M. D. Abramoff, and X. Wu, “Simultaneous multiple surface segmentation using deep learning,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (Springer, 2017), 3–11.

O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention (Springer, 2016), 424–432.

T. Mikolov, M. Karafiát, L. Burget, J. Cernocky, and S. Khudanpur, “Recurrent neural network based language model,” in Proceedings of Interspeech (ISCA, 2011), 1045–1048.

B. Zhang, D. Xiong, and J. Su, “Recurrent Neural Machine Translation,” arXiv preprint arXiv:1607.08725 (2016).

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proceedings of the 32nd International Conference on Machine Learning – Volume 37 (JMLR.org, 2015), 448–456.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv preprint arXiv:1412.6980 (2017).

K. Janocha and W. M. Czarnecki, “On Loss Functions for Deep Neural Networks in Classification,” arXiv preprint arXiv:1702.05659 (2017).

L. Prechelt, “Early Stopping - But When?” in Neural Networks: Tricks of the Trade, G. B. Orr, O. R. Muller (Eds.) (Springer-Verlag, 1998).

R. Caruana, S. Lawrence, and L. Giles, “Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping,” in Proceedings of the 13th International Conference on Neural Information Processing SystemsT. K. Leen, T. G. Dietterich, and V. Tresp, eds. (MIT Press, 2000), 381–387.

F. Chollet, “Keras” https://github.com/fchollet/keras .

Tensorflow white paper, “Tensorflow: Large-scale machine learning on heterogeneous systems” https://tensorflow.org

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” arXiv preprint arXiv:1505.04597 (2015).

H. Salehinejad, S. Sankar, J. Barfett, E. Colak, and S. Valaee, “Recent Advances in Recurrent Neural Networks,” arXiv preprint arXiv:1801.01078 (2018).

R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training Recurrent Neural Networks,” in Proceedings of the 30th International Conference on Machine Learning, Volume 28 (JMLR.org, 2013), 1310–1318.

K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, 2014), 1724–1734.
[Crossref]

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Figures (5)

Fig. 1
Fig. 1 Overview of the RNN-GS method for segmentation of retinal layers, where red boxes represent training steps and blue are evaluation steps. For training (labelled data A) and evaluation (labelled data B) there was no overlap between participants.
Fig. 2
Fig. 2 Example of a model with three stacked RNN layers showing how the activation volume is manipulated as it passes through the network. Each grey volume corresponds to the volume (receptive field width x receptive field height x channels) processed at a particular step within the first sequence operated on by the RNN. The direction of this operation is indicated by the solid arrows. The dotted outline volumes belong to each step of the following sequence and the dashed arrows indicate the order the sequences are processed in. 1) Horizontal unidirectional RNN with a 2x2 receptive field and 8 filters. 2) Horizontal bidirectional RNN with a 1x1 receptive field and 16 filters (8 / pass). 3) Vertical bidirectional RNN with a 2x2 receptive field and 24 filters (12 / pass).
Fig. 3
Fig. 3 Data set 1 (normal OCT images) mean absolute error profiles of each boundary for each of the tested methods.
Fig. 4
Fig. 4 Example RNN-GS segmentation plots of two different participants from data set 1 (normal OCT images) with the locations of the true (solid lines) and predicted (dotted lines) boundaries marked showing the close level of agreement between them.
Fig. 5
Fig. 5 Example RNN-GS segmentation plots of two different participants from data set 2 (AMD OCT images) with the locations of the true (solid lines) and predicted (dotted lines) boundaries marked. The top image shows close agreement between the predictions and truths while the bottom image shows an example of a failure case for the BM boundary with a relatively high level of disagreement between the true and predicted boundaries.

Tables (8)

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Table 1 Effect of patch size and direction on validation classification accuracy (%). The mean (standard deviation) of the accuracy for three training runs (GRU, 32 filters / pass, 2x2 receptive field, 25% dropout).

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Table 2 Effect of receptive field size on validation classification accuracy (%). The mean (standard deviation) of the accuracy for three training runs are reported. (GRU, 32 filters, 64x32 patch size, single-layer vertical unidirectional RNN, 25% dropout).

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Table 3 Effect of number of filters on validation classification accuracy (%). The mean (standard deviation) of the accuracy for three training runs are reported. (GRU, 2x2 receptive field, 64x32 patch size, single-layer vertical unidirectional RNN, 25% dropout).

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Table 4 Effect of stacked layers and order on validation classification accuracy (%). The mean (standard deviation) of the accuracy for three training runs are reported. (GRU, 2x2 receptive field, 32 filters / pass, 64x32 patch size, 25% dropout each layer).

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Table 5 Effect of fully-connected output layer size on validation classification accuracy (%). The mean (standard deviation) of the accuracy for three training runs are reported. (GRU, 2x2 receptive field, 32 filters, 64x32 patch size, single-layer vertical unidirectional RNN, 25% dropout for RNN layer, 50% dropout for fully-connected layer)

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Table 6 The selected RNN architecture. 4 bidirectional layers are utilized with two operating vertically and two horizontally. Each layer contains 16 filters per pass for a total of 32 filters each.

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Table 7 Data set 1 (normal OCT images) position error (in pixels) of each layer boundary for each of the tested methods. The results are reported in mean values and (per A-scan standard deviation). The best results (smallest error) for each boundary are highlighted in bold text.

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Table 8 Data set 2 (AMD OCT images) position error (in pixels) of each layer boundary for each of the tested methods. The results are reported in mean values and (per A-scan standard deviation). The best results (smallest error) for each boundary are highlighted in bold text.

Equations (1)

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w sd =2( P s + P d )+ w min ,

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