Page 298 - Computational Retinal Image Analysis
P. 298

296    CHAPTER 14  OCT fluid detection and quantification




                          [46]  P.L. Vidal, J. de Moura, J. Novo, M.G. Penedo, M. Ortega, Intraretinal fluid identifi-
                             cation via enhanced maps using optical coherence tomography images, Biomed. Opt.
                             Express 9 (10) (2018) 4730–4754, https://doi.org/10.1364/BOE.9.004730.
                          [47]  P.  Seeböck, S.M.  Waldstein, S.  Klimscha, H.  Bogunovic, T.  Schlegl, B.S.  Gerendas,
                             R. Donner, U. Schmidt-Erfurth, G. Langs, Unsupervised identification of disease marker
                             candidates in retinal OCT imaging data, IEEE  Trans. Med. Imaging 38 (4) (2019)
                             1037–1047, https://doi.org/10.1109/TMI.2018.2877080.
                          [48]  P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing ro-
                             bust features with denoising autoencoders, Proceedings of the 25th International
                             Conference on Machine learning (ICML), 2008, pp. 1096–1103,  https://doi.
                             org/10.1145/1390156.1390294.
                          [49]  T. Schlegl, P. Seeböck, S.M. Waldstein, U. Schmidt-Erfurth, G. Langs, Unsupervised
                             anomaly  detection  with  generative  adversarial  networks  to  guide  marker  discovery,
                             Proc. Int. Conf. Inform. Process. Med. Imaging (IPMI), vol. 10265, LNCS, Springer,
                             Cham, 2017, pp. 146–147.
                          [50]  I.J.  Goodfellow, J.  Pouget-Abadie, M.  Mirza, B.  Xu, D.  Warde-Farley, S.  Ozair,
                             A. Courville, Y. Bengio, Generative adversarial networks, Proc. Adv. Neural Inform.
                             Process. Syst. (NIPS), 2014.
                          [51]  J.  Wu,  A.-M.  Philip, D.  Podkowinski, B.S.  Gerendas, G.  Langs, C.  Simader,
                             S.M. Waldstein, U. Schmidt-Erfurth, Multivendor spectral-domain optical coherence to-
                             mography dataset, observer annotation performance evaluation, and standardized evalua-
                             tion framework for intraretinal cystoid fluid segmentation, J. Ophthalmol. (2016), https://
                             doi.org/10.1155/2016/3898750.
                          [52]  H.  Bogunović, F.  Venhuizen, S.  Klimscha, S.  Apostolopoulos,  A.  Bab-Hadiashar,
                             U. Bagci, M.F. Beg, L. Bekalo, Q. Chen, C. Ciller, K. Gopinath, A.K. Gostar, K. Jeon,
                             Z. Ji, S.H. Kang, D.D. Koozekanani, D. Lu, D. Morley, K.K. Parhi, H.S. Park, A. Rashno,
                             M. Sarunic, S. Shaikh, J. Sivaswamy, R. Tennakoon, S. Yadav, S.D. Zanet, S.M. Waldstein,
                             B.S. Gerendas, C. Klaver, C.I. Sánchez, U. Schmidt-Erfurth, RETOUCH—the retinal
                             OCT fluid detection and segmentation benchmark and challenge, IEEE Trans. Med.
                             Imaging 38 (8) (2019) 1858–1874, https://doi.org/10.1109/TMI.2019.2901398.
                          [53]  U.  Chakravarthy, D.  Goldenberg, G.  Young, M.  Havilio, O.  Rafaeli, G.  Benyamini,
                             A. Loewenstein, Automated identification of lesion activity in neovascular age-related
                             macular degeneration, Ophthalmology 123 (8) (2016) 1731–1736.
                          [54]  O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy,
                             A. Khosla, M. Bernstein, A.C. Berg, L. Fei-Fei, ImageNet large scale visual recogni-
                             tion challenge, Int. J. Comput. Vis. 115 (3) (2015) 211–252, https://doi.org/10.1007/
                             s11263-015-0816-y.
                          [55]  C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception ar-
                             chitecture for computer vision, Proc. IEEE Int. Conf. Comput. Vis. Pattern Recogn.
                             (CVPR), 2016, pp. 2818–2826, https://doi.org/10.1109/CVPR.2016.308.
                          [56]  M. Treder, J.L. Lauermann, N. Eter, Automated detection of exudative age-related macu-
                             lar degeneration in spectral domain optical coherence tomography using deep learning,
                             Graefe’s Arch. Clin. Exp. Ophthalmol. 256 (2) (2018) 259–265, https://doi.org/10.1007/
                             s00417-017-3850-3.
                          [57]  K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image
                             recognition, http://arxiv.org/abs/1409.1556, 2014.
                          [58]  P. Prahs, V. Radeck, C. Mayer, Y. Cvetkov, N. Cvetkova, H. Helbig, D. Märker, OCT-
                             based deep learning algorithm for the evaluation of treatment indication with  anti- vascular
   293   294   295   296   297   298   299   300   301   302   303