Page 296 - Computational Retinal Image Analysis

294 CHAPTER 14 OCT fluid detection and quantification

[19] F. Milletari, N. Navab, S.-A. Ahmadi, V-Net: fully convolutional neural networks for
volumetric medical image segmentation, Fourth International Conference on 3D Vision
(3DV), 2016. http://arxiv.org/abs/1606.04797.
[20] O. Ronneberger, P. Fischer, T. Brox, U-Net: convolutional networks for biomedical im-
age segmentation, in: Lect. Notes Comput. Sci., Proc. Int. Conf. Med. Imaging Comput.
& Comput. Assist. Interven. (MICCAI), 9351, 2015, pp. 234–241. vol.
[21] T. Schlegl, S.M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip,
D. Podkowinski, B.S. Gerendas, G. Langs, U. Schmidt-Erfurth, Fully automated detec-
tion and quantification of macular fluid in OCT using deep learning, Ophthalmology 125
(4) (2018) 549–558, https://doi.org/10.1016/J.OPHTHA.2017.10.031.
[22] C.S. Lee, A.J. Tyring, N.P. Deruyter, Y. Wu, A. Rokem, A.Y. Lee, Deep-learning based,
automated segmentation of macular edema in optical coherence tomography, Biomed.
Opt. Express 8 (7) (2017) 3440–3448, https://doi.org/10.1364/BOE.8.003440.
[23] A.G. Roy, S. Conjeti, S.P.K. Karri, D. Sheet, A. Katouzian, C. Wachinger, N. Navab,
ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomogra-
phy using fully convolutional networks, Biomed. Opt. Express 8 (8) (2017) 3627–3642,
https://doi.org/10.1364/BOE.8.003627.
[24] S.J. Chiu, M.J. Allingham, P.S. Mettu, S.W. Cousins, J.A. Izatt, S. Farsiu, Kernel regres-
sion based segmentation of optical coherence tomography images with diabetic macular
edema, Biomed. Opt. Express 6 (4) (2015) 1172–1194.
[25] F.G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser,
C. Hoyng, T. Theelen, C.I. Sánchez, Deep learning approach for the detection and quan-
tification of intraretinal cystoid fluid in multivendor optical coherence tomography,
Biomed. Opt. Express 9 (4) (2018) 1545.
[26] F.G. Venhuizen, B. van Ginneken, B. Liefers, M.J.J.P. van Grinsven, S. Fauser, C. Hoyng,
T. Theelen, C.I. Sánchez, Robust total retina thickness segmentation in optical coherence
tomography images using convolutional neural networks, Biomed. Opt. Express 8 (7)
(2017) 3292–3316, https://doi.org/10.1364/BOE.8.003292.
[27] J. De Fauw, J.R. Ledsam, B. Romera-Paredes, S. Nikolov, N. Tomasev, S. Blackwell,
H. Askham, X. Glorot, B. O’Donoghue, D. Visentin, G. van den Driessche,
B. Lakshminarayanan, C. Meyer, F. Mackinder, S. Bouton, K. Ayoub, R. Chopra, D. King,
A. Karthikesalingam, C.O. Hughes, R. Raine, J. Hughes, D.A. Sim, C. Egan, A. Tufail,
H. Montgomery, D. Hassabis, G. Rees, T. Back, P.T. Khaw, M. Suleyman, J. Cornebise,
P.A. Keane, O. Ronneberger, Clinically applicable deep learning for diagnosis and refer-
ral in retinal disease, Nat. Med. (2018) 1, https://doi.org/10.1038/s41591-018-0107-6.
[28] Ö. Çiçek, A. Abdulkadir, S.S. Lienkamp, T. Brox, O. Ronneberger, 3D U-Net: learning
dense volumetric segmentation from sparse annotation in: Lect. Notes Comput. Sci.,
Proc. Int. Conf. Med. Imaging Comput. & Comput. Assist. Intervent. (MICCAI), vol.
9901, 2016, pp. 424–432, https://doi.org/10.1007/978-3-319-46723-8_49.
[29] C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images, Proc. IEEE Int.
Conf. Comput. Vis. (ICCV), 1998, pp. 839–846.
[30] A. Chambolle, An algorithm for total variation minimization and applications, J. Math.
Imaging Vis. 20 (1–2) (2004) 89–97.
[31] M.K. Garvin, M.D. Abramoff, X. Wu, S.R. Russell, T.L. Burns, M. Sonka, Automated
3-D intraretinal layer segmentation of macular spectral-domain optical coherence to-
mography images, IEEE Trans. Med. Imaging 28 (9) (2009) 1436–1447.
[32] L. Fang, D. Cunefare, C. Wang, R.H. Guymer, S. Li, S. Farsiu, Automatic segmentation
of nine retinal layer boundaries in OCT images of non-exudative AMD patients using