Abstract

Photoreceptor ellipsoid zone (EZ) defects visible on optical coherence tomography (OCT) are important imaging biomarkers for the onset and progression of macular diseases. As such, accurate quantification of EZ defects is paramount to monitor disease progression and treatment efficacy over time. We developed and trained a novel deep learning-based method called Deep OCT Atrophy Detection (DOCTAD) to automatically segment EZ defect areas by classifying 3-dimensional A-scan clusters as normal or defective. Furthermore, we introduce a longitudinal transfer learning paradigm in which the algorithm learns from segmentation errors on images obtained at one time point to segment subsequent images with higher accuracy. We evaluated the performance of this method on 134 eyes of 67 subjects enrolled in a clinical trial of a novel macular telangiectasia type 2 (MacTel2) therapeutic agent. Our method compared favorably to other deep learning-based and non-deep learning-based methods in matching expert manual segmentations. To the best of our knowledge, this is the first automatic segmentation method developed for EZ defects on OCT images of MacTel2.

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

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  1. J. D. M. Gass and B. A. Blodi, “Idiopathic juxtafoveolar retinal telangiectasis. Update of classification and follow-up study,” Ophthalmology 100(10), 1536–1546 (1993).
    [Crossref] [PubMed]
  2. P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
    [Crossref] [PubMed]
  3. F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
    [Crossref] [PubMed]
  4. F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
    [Crossref] [PubMed]
  5. P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
    [Crossref] [PubMed]
  6. 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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
    [Crossref] [PubMed]
  7. T. B. DuBose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imaging, published ahead of print (2018).
  8. A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
    [Crossref] [PubMed]
  9. R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
    [Crossref] [PubMed]
  10. T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).
  11. Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
    [Crossref] [PubMed]
  12. Y. Itoh, A. Vasanji, and J. P. Ehlers, “Volumetric ellipsoid zone mapping for enhanced visualisation of outer retinal integrity with optical coherence tomography,” Br. J. Ophthalmol. 100(3), 295–299 (2016).
    [Crossref] [PubMed]
  13. T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
    [PubMed]
  14. D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
    [Crossref] [PubMed]
  15. C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
    [Crossref] [PubMed]
  16. C. X. Cai, J. G. Light, and J. T. Handa, “Quantifying the rate of ellipsoid zone loss in Stargardt disease,” Am. J. Ophthalmol. 186, 1–9 (2018).
    [Crossref] [PubMed]
  17. G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
    [Crossref] [PubMed]
  18. R. F. Spaide and C. A. Curcio, “Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: Literature review and model,” Retina 31(8), 1609–1619 (2011).
    [Crossref] [PubMed]
  19. L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
    [Crossref] [PubMed]
  20. I. Maruko, T. Iida, T. Sekiryu, and T. Fujiwara, “Early morphological changes and functional abnormalities in group 2a idiopathic juxtafoveolar retinal telangiectasis using spectral domain optical coherence tomography and microperimetry,” Br. J. Ophthalmol. 92(11), 1488–1491 (2008).
    [Crossref] [PubMed]
  21. V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
    [Crossref] [PubMed]
  22. G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
    [Crossref] [PubMed]
  23. D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
    [Crossref] [PubMed]
  24. T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
    [PubMed]
  25. F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
    [PubMed]
  26. P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
    [Crossref] [PubMed]
  27. 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]
  28. S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
    [Crossref] [PubMed]
  29. A. W. Francis, J. Wanek, J. I. Lim, and M. Shahidi, “Enface thickness mapping and reflectance imaging of retinal layers in diabetic retinopathy,” PLoS One 10(12), e0145628 (2015).
    [Crossref] [PubMed]
  30. 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]
  31. 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]
  32. 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]
  33. 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).
    [Crossref] [PubMed]
  34. S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008)
  35. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).
  36. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” arXiv preprint https://arxiv.org/abs/1312.6229 (2013).
  37. 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), 3431–3440.
  38. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 39, 91–99 (2015).
  39. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 2818–2826.
    [Crossref]
  40. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 770–778.
  41. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
    [Crossref] [PubMed]
  42. R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology 124(7), 962–969 (2017).
    [Crossref] [PubMed]
  43. V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
    [Crossref] [PubMed]
  44. R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
    [Crossref] [PubMed]
  45. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
    [Crossref] [PubMed]
  46. D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
    [Crossref] [PubMed]
  47. S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
    [Crossref] [PubMed]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention (Springer, 2015), 234–241.
    [Crossref]
  53. M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
    [Crossref] [PubMed]
  54. A. Abdolmanafi, L. Duong, N. Dahdah, and F. Cheriet, “Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography,” Biomed. Opt. Express 8(2), 1203–1220 (2017).
    [Crossref] [PubMed]
  55. S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
    [Crossref] [PubMed]
  56. C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration OCT images,” Ophthalmology Retina 1(4), 322–327 (2017).
    [Crossref]
  57. C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
    [Crossref] [PubMed]
  58. B. Liefers, F. G. Venhuizen, V. Schreur, B. van Ginneken, C. Hoyng, S. Fauser, T. Theelen, and C. I. Sánchez, “Automatic detection of the foveal center in optical coherence tomography,” Biomed. Opt. Express 8(11), 5160–5178 (2017).
    [Crossref] [PubMed]
  59. G. S. Liu, M. H. Zhu, J. Kim, P. Raphael, B. E. Applegate, and J. S. Oghalai, “ELHnet: A convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography,” Biomed. Opt. Express 8(10), 4579–4594 (2017).
    [Crossref] [PubMed]
  60. 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]
  61. F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
    [Crossref] [PubMed]
  62. J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
    [Crossref] [PubMed]
  63. Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzel, “Learning to diagnose with LSTM recurrent neural networks,” arXiv preprint https://arxiv.org/abs/1511.03677 (2015).
  64. Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining (SIAM, 2016), 432–440.
    [Crossref]
  65. T. Pham, T. Tran, D. Phung, and S. Venkatesh, “Predicting healthcare trajectories from medical records: A deep learning approach,” J. Biomed. Inform. 69, 218–229 (2017).
    [Crossref] [PubMed]
  66. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (2015), 448–456.
  67. X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (2010), 249–256.
  68. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv https://arxiv.org/abs/1412.6980 (2014).
  69. S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
    [Crossref]
  70. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
    [Crossref]
  71. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “Tensorflow: A system for large-scale machine learning,” in 12th Symposium on Operating Systems Design and Implementation (Usenix, 2016), 265–283.
  72. L. Torrey and J. Shavlik, “Transfer learning,” Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques 1 (IGI, 2009), p. 242.
  73. O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
    [Crossref] [PubMed]
  74. Ö. Çiçek, 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.
  75. F. Milletari, N. Navab, and S. A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3D Vision (3DV), 2016 Fourth International Conference on, (IEEE, 2016), 565–571.
    [Crossref]
  76. H. Chen, Q. Dou, L. Yu, and P. A. Heng, “Voxresnet: Deep voxelwise residual networks for volumetric brain segmentation,” arXiv preprint https://arxiv.org/abs/1608.05895 (2016).

2018 (4)

C. X. Cai, J. G. Light, and J. T. Handa, “Quantifying the rate of ellipsoid zone loss in Stargardt disease,” Am. J. Ophthalmol. 186, 1–9 (2018).
[Crossref] [PubMed]

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

2017 (18)

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology 124(7), 962–969 (2017).
[Crossref] [PubMed]

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

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (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]

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]

A. Abdolmanafi, L. Duong, N. Dahdah, and F. Cheriet, “Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography,” Biomed. Opt. Express 8(2), 1203–1220 (2017).
[Crossref] [PubMed]

S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
[Crossref] [PubMed]

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration OCT images,” Ophthalmology Retina 1(4), 322–327 (2017).
[Crossref]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

B. Liefers, F. G. Venhuizen, V. Schreur, B. van Ginneken, C. Hoyng, S. Fauser, T. Theelen, and C. I. Sánchez, “Automatic detection of the foveal center in optical coherence tomography,” Biomed. Opt. Express 8(11), 5160–5178 (2017).
[Crossref] [PubMed]

G. S. Liu, M. H. Zhu, J. Kim, P. Raphael, B. E. Applegate, and J. S. Oghalai, “ELHnet: A convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography,” Biomed. Opt. Express 8(10), 4579–4594 (2017).
[Crossref] [PubMed]

T. Pham, T. Tran, D. Phung, and S. Venkatesh, “Predicting healthcare trajectories from medical records: A deep learning approach,” J. Biomed. Inform. 69, 218–229 (2017).
[Crossref] [PubMed]

2016 (10)

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[Crossref] [PubMed]

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Y. Itoh, A. Vasanji, and J. P. Ehlers, “Volumetric ellipsoid zone mapping for enhanced visualisation of outer retinal integrity with optical coherence tomography,” Br. J. Ophthalmol. 100(3), 295–299 (2016).
[Crossref] [PubMed]

P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
[Crossref] [PubMed]

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

2015 (5)

A. W. Francis, J. Wanek, J. I. Lim, and M. Shahidi, “Enface thickness mapping and reflectance imaging of retinal layers in diabetic retinopathy,” PLoS One 10(12), e0145628 (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]

C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
[Crossref] [PubMed]

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 39, 91–99 (2015).

2014 (4)

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]

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[Crossref] [PubMed]

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
[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]

2013 (2)

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).
[Crossref] [PubMed]

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

2012 (4)

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).

2011 (2)

G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
[Crossref] [PubMed]

R. F. Spaide and C. A. Curcio, “Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: Literature review and model,” Retina 31(8), 1609–1619 (2011).
[Crossref] [PubMed]

2010 (3)

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (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]

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

2008 (2)

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008)

I. Maruko, T. Iida, T. Sekiryu, and T. Fujiwara, “Early morphological changes and functional abnormalities in group 2a idiopathic juxtafoveolar retinal telangiectasis using spectral domain optical coherence tomography and microperimetry,” Br. J. Ophthalmol. 92(11), 1488–1491 (2008).
[Crossref] [PubMed]

2006 (2)

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[Crossref] [PubMed]

A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
[Crossref] [PubMed]

1993 (1)

J. D. M. Gass and B. A. Blodi, “Idiopathic juxtafoveolar retinal telangiectasis. Update of classification and follow-up study,” Ophthalmology 100(10), 1536–1546 (1993).
[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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
[Crossref]

Abdolmanafi, A.

Abdulkadir, A.

Ö. Çiçek, 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.

Abràmoff, M. D.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Aguilar, E.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Allingham, M. J.

Amelon, R.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Applegate, B. E.

Araie, M.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Arshavsky, V. Y.

Asaoka, R.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Avery, R. L.

C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
[Crossref] [PubMed]

Banaee, T.

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

Baughman, D. M.

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration OCT images,” Ophthalmology Retina 1(4), 322–327 (2017).
[Crossref]

Bejnordi, B. E.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Bena, J.

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

Bengio, Y.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (2010), 249–256.

Berendschot, T. T.

P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
[Crossref] [PubMed]

Beven, L.

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

Bird, A. C.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

Blau, H. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Blodi, B. A.

J. D. M. Gass and B. A. Blodi, “Idiopathic juxtafoveolar retinal telangiectasis. Update of classification and follow-up study,” Ophthalmology 100(10), 1536–1546 (1993).
[Crossref] [PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention (Springer, 2015), 234–241.
[Crossref]

Ö. Çiçek, 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.

Bucher, F.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Cai, C. X.

C. X. Cai, J. G. Light, and J. T. Handa, “Quantifying the rate of ellipsoid zone loss in Stargardt disease,” Am. J. Ophthalmol. 186, 1–9 (2018).
[Crossref] [PubMed]

Calabresi, P. A.

Camino, A.

Carass, A.

Carrasco-Zevallos, O. M.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Carroll, J.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Chakraborty, D.

Chakravarthy, U.

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
[Crossref] [PubMed]

Champ, K.

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

Chan, A.

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Charbel Issa, P.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
[Crossref] [PubMed]

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
[Crossref] [PubMed]

Chatterjee, J.

Cheng, Y.

Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining (SIAM, 2016), 432–440.
[Crossref]

Cheriet, F.

Chew, E. Y.

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[Crossref] [PubMed]

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

Chhablani, J.

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

Chiu, S. J.

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (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]

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, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[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]

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008)

Çiçek, Ö.

Ö. Çiçek, 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.

Ciompi, F.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Clarida, W.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Clemons, T. E.

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

Cohen, S. Y.

A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
[Crossref] [PubMed]

Conjeti, S.

Conti, F. F.

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

Cooper, R. F.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Coram, M.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Cousins, S. W.

Cuadros, J.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Cunefare, D.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (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]

Curcio, C. A.

R. F. Spaide and C. A. Curcio, “Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: Literature review and model,” Retina 31(8), 1609–1619 (2011).
[Crossref] [PubMed]

Dahdah, N.

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), 3431–3440.

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]

Deruyter, N. P.

Diaz-Aguilar, S.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Dice, L. R.

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
[Crossref]

Drexler, W.

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[Crossref] [PubMed]

Dubra, A.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Ducos de Lahitte, G.

A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
[Crossref] [PubMed]

Duker, J. S.

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[Crossref] [PubMed]

Duong, L.

Egan, C.

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

Ehlers, J. P.

Y. Itoh, A. Vasanji, and J. P. Ehlers, “Volumetric ellipsoid zone mapping for enhanced visualisation of outer retinal integrity with optical coherence tomography,” Br. J. Ophthalmol. 100(3), 295–299 (2016).
[Crossref] [PubMed]

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

Erginay, A.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Esteva, A.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Fallon, R.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Fang, L.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (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]

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]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[Crossref] [PubMed]

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (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, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[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]

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008)

Fauser, S.

Fawzi, A. A.

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

Feng, D. D.

Finger, R.

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
[Crossref] [PubMed]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention (Springer, 2015), 234–241.
[Crossref]

Flatter, J. A.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Florea, D.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Folgar, F. A.

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[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]

Folk, J. C.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Francis, A. W.

A. W. Francis, J. Wanek, J. I. Lim, and M. Shahidi, “Enface thickness mapping and reflectance imaging of retinal layers in diabetic retinopathy,” PLoS One 10(12), e0145628 (2015).
[Crossref] [PubMed]

Friedlander, M.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

Fujimoto, J.

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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Fujimoto, J. G.

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[Crossref] [PubMed]

Fujiwara, T.

I. Maruko, T. Iida, T. Sekiryu, and T. Fujiwara, “Early morphological changes and functional abnormalities in group 2a idiopathic juxtafoveolar retinal telangiectasis using spectral domain optical coherence tomography and microperimetry,” Br. J. Ophthalmol. 92(11), 1488–1491 (2008).
[Crossref] [PubMed]

Garcia, P. M.

G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
[Crossref] [PubMed]

Gargeya, R.

R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology 124(7), 962–969 (2017).
[Crossref] [PubMed]

Gass, J. D. M.

J. D. M. Gass and B. A. Blodi, “Idiopathic juxtafoveolar retinal telangiectasis. Update of classification and follow-up study,” Ophthalmology 100(10), 1536–1546 (1993).
[Crossref] [PubMed]

Gattani, V. S.

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

Gaudric, A.

A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
[Crossref] [PubMed]

Ghafoorian, M.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Gillies, M. C.

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

Girshick, R.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 39, 91–99 (2015).

Glorot, X.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (2010), 249–256.

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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Gulshan, V.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Guymer, R. H.

Hahn, P.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Han, D. P.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Handa, J. T.

C. X. Cai, J. G. Light, and J. T. Handa, “Quantifying the rate of ellipsoid zone loss in Stargardt disease,” Am. J. Ophthalmol. 186, 1–9 (2018).
[Crossref] [PubMed]

Haouchine, B.

A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
[Crossref] [PubMed]

Hauser, M.

He, K.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 39, 91–99 (2015).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 770–778.

Hee, M. R.

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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Heeren, T. F.

P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
[Crossref] [PubMed]

Heeren, T. F. C.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

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. 1, 1097–1105 (2012).

Holz, F. G.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
[Crossref] [PubMed]

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
[Crossref] [PubMed]

Hoyng, C.

Hu, J.

Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining (SIAM, 2016), 432–440.
[Crossref]

Huang, D.

Z. Wang, A. Camino, M. Zhang, J. Wang, T. S. Hwang, D. J. Wilson, D. Huang, D. Li, and Y. Jia, “Automated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography,” Biomed. Opt. Express 8(12), 5384–5398 (2017).
[Crossref] [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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Huang, R.

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

Hwang, T. S.

Iida, T.

I. Maruko, T. Iida, T. Sekiryu, and T. Fujiwara, “Early morphological changes and functional abnormalities in group 2a idiopathic juxtafoveolar retinal telangiectasis using spectral domain optical coherence tomography and microperimetry,” Br. J. Ophthalmol. 92(11), 1488–1491 (2008).
[Crossref] [PubMed]

Ioffe, S.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 2818–2826.
[Crossref]

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (2015), 448–456.

Itoh, Y.

Y. Itoh, A. Vasanji, and J. P. Ehlers, “Volumetric ellipsoid zone mapping for enhanced visualisation of outer retinal integrity with optical coherence tomography,” Br. J. Ophthalmol. 100(3), 295–299 (2016).
[Crossref] [PubMed]

Iwase, A.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Izatt, J. A.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (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, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[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]

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008)

Jaffe, G. J.

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

Jaffe, S. J.

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

Jana, S.

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

Jia, Y.

Jonnal, R. S.

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[Crossref] [PubMed]

Karri, S. P. K.

Katouzian, A.

Keller, B.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Kim, J.

Kim, R.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Kitka, D.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

Ko, J.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Ko, T. H.

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[Crossref] [PubMed]

Kocaoglu, O. P.

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[Crossref] [PubMed]

Kooi, T.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).

Kuo, A. N.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Kupitz, E. H.

P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
[Crossref] [PubMed]

Kuprel, B.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Lad, E. M.

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

Landa, G.

G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
[Crossref] [PubMed]

Lang, A.

Langlo, C. S.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Lee, A. Y.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration OCT images,” Ophthalmology Retina 1(4), 322–327 (2017).
[Crossref]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

Lee, C. S.

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration OCT images,” Ophthalmology Retina 1(4), 322–327 (2017).
[Crossref]

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Lee, S. H.

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[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]

Leng, T.

R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology 124(7), 962–969 (2017).
[Crossref] [PubMed]

Leung, I.

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

Li, C.

Li, D.

Li, S.

Li, X. T.

Liefers, B.

Lienkamp, S. S.

Ö. Çiçek, 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.

Light, J. G.

C. X. Cai, J. G. Light, and J. T. Handa, “Quantifying the rate of ellipsoid zone loss in Stargardt disease,” Am. J. Ophthalmol. 186, 1–9 (2018).
[Crossref] [PubMed]

Lim, J. I.

A. W. Francis, J. Wanek, J. I. Lim, and M. Shahidi, “Enface thickness mapping and reflectance imaging of retinal layers in diabetic retinopathy,” PLoS One 10(12), e0145628 (2015).
[Crossref] [PubMed]

Lin, C. P.

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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Litjens, G.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Liu, G. S.

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), 3431–3440.

Lou, Y.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Madams, T.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Marra, K. V.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Maruko, I.

I. Maruko, T. Iida, T. Sekiryu, and T. Fujiwara, “Early morphological changes and functional abnormalities in group 2a idiopathic juxtafoveolar retinal telangiectasis using spectral domain optical coherence tomography and microperimetry,” Br. J. Ophthalmol. 92(11), 1488–1491 (2008).
[Crossref] [PubMed]

Massin, P.

A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
[Crossref] [PubMed]

Mega, J. L.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Mettu, P. S.

Miller, D. T.

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[Crossref] [PubMed]

Mukherjee, D.

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

Murata, H.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Narayanaswamy, A.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Nasir, M.

C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
[Crossref] [PubMed]

Navab, N.

Neitz, M.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Nelson, P. C.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Nicholas, P.

Niemeijer, M.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

Novoa, R. A.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

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]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Oghalai, J. S.

Pan, S. J.

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

Patil, A.

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

Pauleikhoff, D.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

Paunescu, L. A.

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[Crossref] [PubMed]

Peng, L.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Pennesi, M. E.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Peto, T.

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

Pham, T.

T. Pham, T. Tran, D. Phung, and S. Venkatesh, “Predicting healthcare trajectories from medical records: A deep learning approach,” J. Biomed. Inform. 69, 218–229 (2017).
[Crossref] [PubMed]

Phung, D.

T. Pham, T. Tran, D. Phung, and S. Venkatesh, “Predicting healthcare trajectories from medical records: A deep learning approach,” J. Biomed. Inform. 69, 218–229 (2017).
[Crossref] [PubMed]

Pieramici, D. J.

C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
[Crossref] [PubMed]

Prince, J. L.

Puliafito, C. A.

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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Quezada Ruiz, C.

C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
[Crossref] [PubMed]

Rabena, M.

C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
[Crossref] [PubMed]

Raman, R.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Raphael, P.

Ren, S.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 39, 91–99 (2015).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 770–778.

Richhariya, A.

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

Robison, S.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Rokem, A.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

Ronneberger, O.

Ö. Çiçek, 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.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention (Springer, 2015), 234–241.
[Crossref]

Rosen, R. B.

G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
[Crossref] [PubMed]

Roy, A. G.

Rubin, G. S.

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

Sadda, S.

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
[Crossref] [PubMed]

Sallo, F. B.

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

Sánchez, C. I.

Scholl, H. P.

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
[Crossref] [PubMed]

Scholl, H. P. N.

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

Schreur, V.

Schuman, J. S.

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Scoles, D.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Seiple, W. H.

G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
[Crossref] [PubMed]

Sekiryu, T.

I. Maruko, T. Iida, T. Sekiryu, and T. Fujiwara, “Early morphological changes and functional abnormalities in group 2a idiopathic juxtafoveolar retinal telangiectasis using spectral domain optical coherence tomography and microperimetry,” Br. J. Ophthalmol. 92(11), 1488–1491 (2008).
[Crossref] [PubMed]

Setio, A. A. A.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Sevilla, M. B.

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[Crossref] [PubMed]

Shahidi, M.

A. W. Francis, J. Wanek, J. I. Lim, and M. Shahidi, “Enface thickness mapping and reflectance imaging of retinal layers in diabetic retinopathy,” PLoS One 10(12), e0145628 (2015).
[Crossref] [PubMed]

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), 3431–3440.

Shen, L.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Shieh, C.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Shlens, J.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 2818–2826.
[Crossref]

Siddique, N.

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

Siddique, T.

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

Simonett, J. M.

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

Singh, R. P.

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

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]

Sotirchos, E. S.

Spaide, R. F.

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
[Crossref] [PubMed]

R. F. Spaide and C. A. Curcio, “Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: Literature review and model,” Retina 31(8), 1609–1619 (2011).
[Crossref] [PubMed]

Srinivasan, P. P.

Staurenghi, G.

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
[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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Stumpe, M. C.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Su, E.

G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
[Crossref] [PubMed]

Su, L.

Sun, J.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 39, 91–99 (2015).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 770–778.

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).

Swanson, E. A.

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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Swetter, S. M.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Szegedy, C.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 2818–2826.
[Crossref]

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (2015), 448–456.

Theelen, T.

Thrun, S.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[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]

Todorich, B.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Toth, C. A.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[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, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[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]

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008)

Tran, T.

T. Pham, T. Tran, D. Phung, and S. Venkatesh, “Predicting healthcare trajectories from medical records: A deep learning approach,” J. Biomed. Inform. 69, 218–229 (2017).
[Crossref] [PubMed]

Troeger, E.

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
[Crossref] [PubMed]

Tyring, A. J.

van der Laak, J. A. W. M.

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

van Ginneken, B.

van Grinsven, M. J. J. P.

Vanhoucke, V.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 2818–2826.
[Crossref]

Vann, R. R.

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[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]

Vasanji, A.

Y. Itoh, A. Vasanji, and J. P. Ehlers, “Volumetric ellipsoid zone mapping for enhanced visualisation of outer retinal integrity with optical coherence tomography,” Br. J. Ophthalmol. 100(3), 295–299 (2016).
[Crossref] [PubMed]

Venhuizen, F. G.

Venkatesh, S.

T. Pham, T. Tran, D. Phung, and S. Venkatesh, “Predicting healthcare trajectories from medical records: A deep learning approach,” J. Biomed. Inform. 69, 218–229 (2017).
[Crossref] [PubMed]

Venugopalan, S.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Viehland, C.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Volpe, N. J.

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[Crossref] [PubMed]

Vupparaboina, K. K.

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

Wachinger, C.

Wai, K.

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

Wanek, J.

A. W. Francis, J. Wanek, J. I. Lim, and M. Shahidi, “Enface thickness mapping and reflectance imaging of retinal layers in diabetic retinopathy,” PLoS One 10(12), e0145628 (2015).
[Crossref] [PubMed]

Wang, C.

Wang, F.

Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining (SIAM, 2016), 432–440.
[Crossref]

Wang, J.

Wang, X.

Wang, Z.

Waterman, G.

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

Webster, D. R.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Weinberg, D. V.

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

Werner, J. S.

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[Crossref] [PubMed]

Widner, K.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Wilke, R.

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
[Crossref] [PubMed]

Wilson, D. J.

Winter, K. P.

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

Wojna, Z.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 2818–2826.
[Crossref]

Wolf-Schnurrbusch, U. E.

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

Wolf-Schnurrbusch, U. E. K.

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

Wu, D.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

Wu, Y.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

Xiao, S.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Xu, X.

Xu, Y.

Yan, K.

Yang, Q.

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

Ying, H. S.

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]

Yuan, E. L.

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[Crossref] [PubMed]

Zawadzki, R. J.

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[Crossref] [PubMed]

Zhang, M.

Zhang, P.

Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining (SIAM, 2016), 432–440.
[Crossref]

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 770–778.

Zhu, M. H.

Adv. Neural Inf. Process. Syst. (2)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst. 1, 1097–1105 (2012).

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 39, 91–99 (2015).

Am. J. Ophthalmol. (1)

C. X. Cai, J. G. Light, and J. T. Handa, “Quantifying the rate of ellipsoid zone loss in Stargardt disease,” Am. J. Ophthalmol. 186, 1–9 (2018).
[Crossref] [PubMed]

Arch. Ophthalmol. (1)

A. Gaudric, G. Ducos de Lahitte, S. Y. Cohen, P. Massin, and B. Haouchine, “Optical coherence tomography in group 2a idiopathic juxtafoveolar retinal telangiectasis,” Arch. Ophthalmol. 124(10), 1410–1419 (2006).
[Crossref] [PubMed]

Biomed. Opt. Express (13)

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

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).
[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]

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]

C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based, automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
[Crossref] [PubMed]

B. Liefers, F. G. Venhuizen, V. Schreur, B. van Ginneken, C. Hoyng, S. Fauser, T. Theelen, and C. I. Sánchez, “Automatic detection of the foveal center in optical coherence tomography,” Biomed. Opt. Express 8(11), 5160–5178 (2017).
[Crossref] [PubMed]

G. S. Liu, M. H. Zhu, J. Kim, P. Raphael, B. E. Applegate, and J. S. Oghalai, “ELHnet: A convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography,” Biomed. Opt. Express 8(10), 4579–4594 (2017).
[Crossref] [PubMed]

A. Abdolmanafi, L. Duong, N. Dahdah, and F. Cheriet, “Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography,” Biomed. Opt. Express 8(2), 1203–1220 (2017).
[Crossref] [PubMed]

S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
[Crossref] [PubMed]

Br. J. Ophthalmol. (2)

I. Maruko, T. Iida, T. Sekiryu, and T. Fujiwara, “Early morphological changes and functional abnormalities in group 2a idiopathic juxtafoveolar retinal telangiectasis using spectral domain optical coherence tomography and microperimetry,” Br. J. Ophthalmol. 92(11), 1488–1491 (2008).
[Crossref] [PubMed]

Y. Itoh, A. Vasanji, and J. P. Ehlers, “Volumetric ellipsoid zone mapping for enhanced visualisation of outer retinal integrity with optical coherence tomography,” Br. J. Ophthalmol. 100(3), 295–299 (2016).
[Crossref] [PubMed]

Comput. Biol. Med. (1)

V. S. Gattani, K. K. Vupparaboina, A. Patil, J. Chhablani, A. Richhariya, and S. Jana, “Semi-automated quantification of retinal IS/OS damage in en-face OCT image,” Comput. Biol. Med. 69, 52–60 (2016).
[Crossref] [PubMed]

Ecology (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
[Crossref]

IEEE Trans. Knowl. Data Eng. (1)

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010).
[Crossref]

Invest. Ophthalmol. Vis. Sci. (6)

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016).
[Crossref] [PubMed]

D. Mukherjee, E. M. Lad, R. R. Vann, S. J. Jaffe, T. E. Clemons, M. Friedlander, E. Y. Chew, G. J. Jaffe, and S. Farsiu, “Correlation between macular integrity assessment and optical coherence tomography imaging of ellipsoid zone in macular telangiectasia type 2,” Invest. Ophthalmol. Vis. Sci. 58(6), BIO291 (2017).
[Crossref] [PubMed]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of amd pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53(1), 53–61 (2012).
[Crossref] [PubMed]

R. S. Jonnal, O. P. Kocaoglu, R. J. Zawadzki, S. H. Lee, J. S. Werner, and D. T. Miller, “The cellular origins of the outer retinal bands in optical coherence tomography images,” Invest. Ophthalmol. Vis. Sci. 55(12), 7904–7918 (2014).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, “The IS/OS junction layer in the natural history of type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(12), 7889–7895 (2012).
[Crossref] [PubMed]

F. B. Sallo, T. Peto, C. Egan, U. E. K. Wolf-Schnurrbusch, T. E. Clemons, M. C. Gillies, D. Pauleikhoff, G. S. Rubin, E. Y. Chew, and A. C. Bird, ““En face” OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia,” Invest. Ophthalmol. Vis. Sci. 53(10), 6145–6152 (2012).
[Crossref] [PubMed]

J. Biomed. Inform. (1)

T. Pham, T. Tran, D. Phung, and S. Venkatesh, “Predicting healthcare trajectories from medical records: A deep learning approach,” J. Biomed. Inform. 69, 218–229 (2017).
[Crossref] [PubMed]

JAMA (1)

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

JCI Insight (1)

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), 97585 (2017).
[Crossref] [PubMed]

Med. Image Anal. (1)

G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Med. Image Anal. 42, 60–88 (2017).
[Crossref] [PubMed]

Nature (1)

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature 542(7639), 115–118 (2017).
[Crossref] [PubMed]

Ophthalmology (7)

R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology 124(7), 962–969 (2017).
[Crossref] [PubMed]

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[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]

F. A. Folgar, E. L. Yuan, M. B. Sevilla, S. J. Chiu, S. Farsiu, E. Y. Chew, and C. A. Toth, “Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration,” Ophthalmology 123(1), 39–50 (2016).
[Crossref] [PubMed]

J. D. M. Gass and B. A. Blodi, “Idiopathic juxtafoveolar retinal telangiectasis. Update of classification and follow-up study,” Ophthalmology 100(10), 1536–1546 (1993).
[Crossref] [PubMed]

G. Staurenghi, S. Sadda, U. Chakravarthy, and R. F. Spaide, “Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus,” Ophthalmology 121(8), 1572–1578 (2014).
[Crossref] [PubMed]

L. A. Paunescu, T. H. Ko, J. S. Duker, A. Chan, W. Drexler, J. S. Schuman, and J. G. Fujimoto, “Idiopathic juxtafoveal retinal telangiectasis: New findings by ultrahigh-resolution optical coherence tomography,” Ophthalmology 113(1), 48–57 (2006).
[Crossref] [PubMed]

Ophthalmology Retina (1)

C. S. Lee, D. M. Baughman, and A. Y. Lee, “Deep learning is effective for classifying normal versus age-related macular degeneration OCT images,” Ophthalmology Retina 1(4), 322–327 (2017).
[Crossref]

Opt. Express (1)

PLoS One (3)

P. Charbel Issa, E. Troeger, R. Finger, F. G. Holz, R. Wilke, and H. P. Scholl, “Structure-function correlation of the human central retina,” PLoS One 5(9), e12864 (2010).
[Crossref] [PubMed]

A. W. Francis, J. Wanek, J. I. Lim, and M. Shahidi, “Enface thickness mapping and reflectance imaging of retinal layers in diabetic retinopathy,” PLoS One 10(12), e0145628 (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]

Proc. SPIE (1)

S. Farsiu, S. J. Chiu, J. A. Izatt, and C. A. Toth, “Fast detection and segmentation of drusen in retinal optical coherence tomography images,” Proc. SPIE 6844, 68440D (2008)

Prog. Retin. Eye Res. (1)

P. Charbel Issa, M. C. Gillies, E. Y. Chew, A. C. Bird, T. F. C. Heeren, T. Peto, F. G. Holz, and H. P. N. Scholl, “Macular telangiectasia type 2,” Prog. Retin. Eye Res. 34, 49–77 (2013).
[Crossref] [PubMed]

Retin. Cases Brief Rep. (1)

C. Quezada Ruiz, D. J. Pieramici, M. Nasir, M. Rabena, and R. L. Avery, “Severe acute vision loss, dyschromatopsia, and changes in the ellipsoid zone on SD-OCT associated with intravitreal ocriplasmin injection,” Retin. Cases Brief Rep. 9(2), 145–148 (2015).
[Crossref] [PubMed]

Retina (7)

R. F. Spaide and C. A. Curcio, “Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: Literature review and model,” Retina 31(8), 1609–1619 (2011).
[Crossref] [PubMed]

T. F. C. Heeren, D. Kitka, D. Florea, T. E. Clemons, E. Y. Chew, A. C. Bird, D. Pauleikhoff, P. Charbel Issa, F. G. Holz, and T. Peto, “Longitudinal correlation of ellipsoid zone loss and functional loss in macular telangiectasia type 2,” Retina 38(Suppl 1), S20–S26 (2018).
[PubMed]

D. Scoles, J. A. Flatter, R. F. Cooper, C. S. Langlo, S. Robison, M. Neitz, D. V. Weinberg, M. E. Pennesi, D. P. Han, A. Dubra, and J. Carroll, “Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography,” Retina 36(1), 91–103 (2016).
[Crossref] [PubMed]

P. Charbel Issa, T. F. Heeren, E. H. Kupitz, F. G. Holz, and T. T. Berendschot, “Very early disease manifestations of macular telangiectasia type 2,” Retina 36(3), 524–534 (2016).
[Crossref] [PubMed]

T. Peto, T. F. C. Heeren, T. E. Clemons, F. B. Sallo, I. Leung, E. Y. Chew, and A. C. Bird, “Correlation of clinical and structural progression with visual acuity loss in macular telangiectasia type 2: Mactel project report no. 6–the mactel research group,” Retina 38(Suppl 1), S8–S13 (2018).
[PubMed]

F. B. Sallo, I. Leung, T. E. Clemons, T. Peto, E. Y. Chew, D. Pauleikhoff, A. C. Bird, and M. C. R. Group, “Correlation of structural and functional outcome measures in a phase one trial of ciliary neurotrophic factor in type 2 idiopathic macular telangiectasia,” Retina 38(Suppl 1), S27–S32 (2018).
[PubMed]

G. Landa, E. Su, P. M. Garcia, W. H. Seiple, and R. B. Rosen, “Inner segment-outer segment junctional layer integrity and corresponding retinal sensitivity in dry and wet forms of age-related macular degeneration,” Retina 31(2), 364–370 (2011).
[Crossref] [PubMed]

Sci. Rep. (3)

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref] [PubMed]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

J. M. Simonett, R. Huang, N. Siddique, S. Farsiu, T. Siddique, N. J. Volpe, and A. A. Fawzi, “Macular sub-layer thinning and association with pulmonary function tests in Amyotrophic Lateral Sclerosis,” Sci. Rep. 6(1), 29187 (2016).
[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. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Other (17)

T. B. DuBose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imaging, published ahead of print (2018).

T. Banaee, R. P. Singh, K. Champ, F. F. Conti, K. Wai, J. Bena, L. Beven, and J. P. Ehlers, “Ellipsoid zone mapping parameters in retinal venous occlusive disease with associated macular edema,” Ophthalmology Retina, in press (2018).

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 2818–2826.
[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), 770–778.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” arXiv preprint https://arxiv.org/abs/1312.6229 (2013).

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), 3431–3440.

Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzel, “Learning to diagnose with LSTM recurrent neural networks,” arXiv preprint https://arxiv.org/abs/1511.03677 (2015).

Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining (SIAM, 2016), 432–440.
[Crossref]

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-assisted Intervention (Springer, 2015), 234–241.
[Crossref]

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (2015), 448–456.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (2010), 249–256.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv https://arxiv.org/abs/1412.6980 (2014).

Ö. Çiçek, 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.

F. Milletari, N. Navab, and S. A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 3D Vision (3DV), 2016 Fourth International Conference on, (IEEE, 2016), 565–571.
[Crossref]

H. Chen, Q. Dou, L. Yu, and P. A. Heng, “Voxresnet: Deep voxelwise residual networks for volumetric brain segmentation,” arXiv preprint https://arxiv.org/abs/1608.05895 (2016).

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “Tensorflow: A system for large-scale machine learning,” in 12th Symposium on Operating Systems Design and Implementation (Usenix, 2016), 265–283.

L. Torrey and J. Shavlik, “Transfer learning,” Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques 1 (IGI, 2009), p. 242.

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Figures (11)

Fig. 1
Fig. 1 (a) Retinal OCT volume. (b) Gold standard (manual) segmentation of the ILM (orange), inner EZ (magenta), inner RPE (cyan), and BrM (yellow) layer boundaries by expert Readers. The EZ thickness is defined by the inner EZ and inner RPE layer boundaries. (c) En face EZ thickness map. (d) Gold standard (manual) binary map of EZ defects. EZ thicknesses of less than 12µm were classified as EZ defects.
Fig. 2
Fig. 2 B-scan from the position marked by the red line on Fig. 1(c-d) showing the gold standard (manual) segmentation of the inner EZ (magenta) and inner RPE (cyan) layer boundaries by expert Readers and the EZ defects identified (white).
Fig. 3
Fig. 3 Clusters of dimensions 256 × 16 × 5 pixels were extracted from the OCT volumes.
Fig. 4
Fig. 4 DOCTAD CNN architecture showing the number of features (top) and filter sizes (bottom) of the convolutional and fully-connected layers, pooling sizes of the pooling layers, and the output dimensions of each layer as indicated by the layer number.
Fig. 5
Fig. 5 During prediction, clusters of every A-scan were extracted from the given OCT volume and passed as inputs to the trained CNN to generate an en face probability map which was thresholded to obtain the predicted binary map of EZ defects.
Fig. 6
Fig. 6 (a – c) Overlay of gold standard (manual) and predicted binary maps of EZ defects by DOCTRAP, CNN-GS, and our new DOCTAD method showing TP (green), FP (blue) and FN (red). (d) B-scan from the position marked by the yellow line on (a-c). (e) Gold standard (manual) boundary segmentations and EZ defect areas (white). (f – h) Boundary segmentations and predicted EZ defect areas by DOCTRAP, CNN-GS, and DOCTAD showing TP (green), FP (blue) and FN (red). DOCTRAP and CNN-GS correctly identified some EZ defects despite errors in the boundary segmentations.
Fig. 7
Fig. 7 (a – c) Overlay of gold standard (manual) and predicted binary maps of EZ defects by DOCTRAP, CNN-GS, and our new DOCTAD method showing TP (green), FP (blue) and FN (red). (d) B-scan from the position marked by the yellow line on (a-c). (e) Gold standard (manual) boundary segmentations and EZ defect areas (white). (f – h) Boundary segmentations and predicted EZ defect areas by DOCTRAP, CNN-GS, and DOCTAD showing TP (green), FP (blue) and FN (red). DOCTRAP correctly identified some EZ defects despite errors in the boundary segmentations whereas CNN-GS correctly identified more EZ defects with more accurate boundary segmentations.
Fig. 8
Fig. 8 Predicted binary maps of EZ defects on the 6-month volumes by DOCTAD before and after fine-tuning on the subject’s baseline volume showing TP (green), FP (blue), FN (red) and B-scans corresponding to the position marked by the yellow lines. Fine-tuning improved the EZ defects segmentations.
Fig. 9
Fig. 9 Predicted binary maps of EZ defects by DOCTAD showing TP (green), FP (blue), FN (red) and B-scans corresponding to the position marked by the yellow lines. The false positives (blue) occurred in borderline-defective areas.
Fig. 10
Fig. 10 Predicted binary maps of EZ defects by DOCTAD showing TP (green), FP (blue), FN (red) and B-scans corresponding to the position marked by the yellow lines. The false negatives (red) occurred in regions obscured by intra-retinal pigment.
Fig. 11
Fig. 11 (a) B-scan with EZ defects that was missed in the gold standard (manual) segmentation (yellow arrow). (b) Gold standard (manual) boundary segmentations and EZ defects areas (white). (c) Predicted EZ defects areas by DOCTAD showing TP (green), FP (blue) and FN (red). The missed EZ defects (yellow arrow) were correctly identified but considered false positives (blue).

Tables (3)

Tables Icon

Table 1 Performance metrics (mean ± standard deviation, median) of DOCTRAP [27, 28], CNN-GS [48], and our new DOCTAD method on 134 baseline volumes using 6-fold cross validation.

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Table 2 Performance metrics (mean ± standard deviation, median) of DOCTAD on 134 6-month volumes before and after fine-tuning both on the initial training set and the subject’s baseline volume using 6-fold cross validation. Statistically significant differences (p-value < 0.05) are shown in bold.

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Table 3 Performance breakdown on 134 6-month volumes after fine-tuning on the subject’s baseline volume.

Equations (4)

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L= 1 N i=1 N [ y i log( p i )+(1 y i )log(1 p i ) ]
DSC= 2TP 2TP+FP+FN ,
E t =k| FP+FN |,
E n =k| FPFN |,

Metrics