378
F. Shi et al.
The method still has two limitations. First, it needs to be tested in a bigger
dataset to further prove its prediction accuracy. Secondly, to enhance its performance, the image preprocessing methods, including registration and segmentation,
needs improvement.
References
1. N. Kwak, N. Okamoto, J.M. Wood, P.A. Campochiaro, VEGF is major stimulator in model of
choroidal neovascularization. Invest. Ophthalmol. Vis. Sci. 41(10), 3158–3164 (2000)
2. A. Kubicka-Trz˛ aska, J. Wila´ nska, B. Romanowska-Dixon, M. Sanak, Circulating antiretinal
antibodies predict the outcome of anti-VEGF therapy in patients with exudative age-related
macular degeneration. Acta Ophthalmol. 90(1), 21–24 (2012)
3. 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, J.G. Fujimoto, Optical coherence tomography. Science 254,
1178–1181 (1991)
4. W. Drexler, J.G. Fujimoto, State-of-the-art retinal optical coherence tomography. Prog. Retinal
Eye Res. 27(1), 45–88 (2008)
5. G.J. Jaffe, J. Caprioli, Optical coherence tomography to detect and manage retinal disease and
glaucoma. Am. J. Ophthalmol. 137(1), 156–169 (2004)
6. M.R. Hee, C.R. Baumal, C.A. Puliafito, J.S. Duker, E. Reichel, J.R. Wilkins, J.G. Coker, J.S.
Schuman, E.A. Swanson, J.G. Fujimoto, Optical coherence tomography of age-related macular
degeneration and choroidal neovascularization. Ophthalmology 103(8), 1260–1270 (1996)
7. P.J. Rosenfeld, A.E. Fung, G.A. Lalwani, Visual acuity outcomes following a variable-dosing
regimen for ranibizumab (LucentisTM) in neovascular AMD: the PrONTO study. Invest. Ophthalmol. Vis. Sci. 47(13), 2958 (2006)
8. H. Bogunovic, M.D. Abràmoff, L. Zhang, M. Sonka, Prediction of treatment response from
retinal OCT in patients with exudative age-related macular degeneration, in Medical Imaging
and Computer-Assisted Interventions Workshop (2014)
9. W.D. Vogl, S.M. Waldstein, B.S. Gerendas, U. Schmidterfurth, G. Langs, Predicting macular
edema recurrence from spatio-temporal signatures in optical coherence tomography images.
IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017)
10. S. Zhu, F. Shi, D. Xiang, W. Zhu, H. Chen, X. Chen, Choroid neovascularization growth
prediction with treatment based on reaction-diffusion model in 3-D OCT images. IEEE J.
Biomed. Health Inform. 21(6), 1667–1674 (2017)
11. X. Guo, Three-dimensional moment invariants under rigid transformation. Lect. Notes Comput.
Sci. 719, 518–522 (1993)
12. F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, H. Chen, Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous
pigment epithelial detachments. IEEE Trans. Med. Imaging 34(2), 441–452 (2015)
13. X. Chen, M. Niemeijer, L. Zhang, K. Lee, M.D. Abràmoff, M. Sonka, Three-dimensional
segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graphsearch-graph-cut. IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012)
14. Q. Fang, ISO2Mesh: a 3D surface and volumetric mesh generator for MATLAB/octave
[Online]. Available: http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?Home (2010)
15. Q. Fang, D.A. Boas, Tetrahedral mesh generation from volumetric binary and grayscale images,
in Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro (IEEE Press, 2009), pp. 1142–1145
16. A.E. Islam, N. Goel, S. Mahapatra, M.A. Alam, Reaction-diffusion model. Springer Series
Adv. Microelectron. 139, 181–207 (2016)
17. X. Chen, R.M. Summers, J. Yao, Kidney tumor growth prediction by coupling reactiondiffusion and biomechanical model. IEEE Trans. Biomed. Eng. 60(1), 169–173 (2013)
F. Shi et al.
The method still has two limitations. First, it needs to be tested in a bigger
dataset to further prove its prediction accuracy. Secondly, to enhance its performance, the image preprocessing methods, including registration and segmentation,
needs improvement.
References
1. N. Kwak, N. Okamoto, J.M. Wood, P.A. Campochiaro, VEGF is major stimulator in model of
choroidal neovascularization. Invest. Ophthalmol. Vis. Sci. 41(10), 3158–3164 (2000)
2. A. Kubicka-Trz˛ aska, J. Wila´ nska, B. Romanowska-Dixon, M. Sanak, Circulating antiretinal
antibodies predict the outcome of anti-VEGF therapy in patients with exudative age-related
macular degeneration. Acta Ophthalmol. 90(1), 21–24 (2012)
3. 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, J.G. Fujimoto, Optical coherence tomography. Science 254,
1178–1181 (1991)
4. W. Drexler, J.G. Fujimoto, State-of-the-art retinal optical coherence tomography. Prog. Retinal
Eye Res. 27(1), 45–88 (2008)
5. G.J. Jaffe, J. Caprioli, Optical coherence tomography to detect and manage retinal disease and
glaucoma. Am. J. Ophthalmol. 137(1), 156–169 (2004)
6. M.R. Hee, C.R. Baumal, C.A. Puliafito, J.S. Duker, E. Reichel, J.R. Wilkins, J.G. Coker, J.S.
Schuman, E.A. Swanson, J.G. Fujimoto, Optical coherence tomography of age-related macular
degeneration and choroidal neovascularization. Ophthalmology 103(8), 1260–1270 (1996)
7. P.J. Rosenfeld, A.E. Fung, G.A. Lalwani, Visual acuity outcomes following a variable-dosing
regimen for ranibizumab (LucentisTM) in neovascular AMD: the PrONTO study. Invest. Ophthalmol. Vis. Sci. 47(13), 2958 (2006)
8. H. Bogunovic, M.D. Abràmoff, L. Zhang, M. Sonka, Prediction of treatment response from
retinal OCT in patients with exudative age-related macular degeneration, in Medical Imaging
and Computer-Assisted Interventions Workshop (2014)
9. W.D. Vogl, S.M. Waldstein, B.S. Gerendas, U. Schmidterfurth, G. Langs, Predicting macular
edema recurrence from spatio-temporal signatures in optical coherence tomography images.
IEEE Trans. Med. Imaging 36(9), 1773–1783 (2017)
10. S. Zhu, F. Shi, D. Xiang, W. Zhu, H. Chen, X. Chen, Choroid neovascularization growth
prediction with treatment based on reaction-diffusion model in 3-D OCT images. IEEE J.
Biomed. Health Inform. 21(6), 1667–1674 (2017)
11. X. Guo, Three-dimensional moment invariants under rigid transformation. Lect. Notes Comput.
Sci. 719, 518–522 (1993)
12. F. Shi, X. Chen, H. Zhao, W. Zhu, D. Xiang, E. Gao, M. Sonka, H. Chen, Automated 3D retinal layer segmentation of macular optical coherence tomography images with serous
pigment epithelial detachments. IEEE Trans. Med. Imaging 34(2), 441–452 (2015)
13. X. Chen, M. Niemeijer, L. Zhang, K. Lee, M.D. Abràmoff, M. Sonka, Three-dimensional
segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graphsearch-graph-cut. IEEE Trans. Med. Imaging 31(8), 1521–1531 (2012)
14. Q. Fang, ISO2Mesh: a 3D surface and volumetric mesh generator for MATLAB/octave
[Online]. Available: http://iso2mesh.sourceforge.net/cgi-bin/index.cgi?Home (2010)
15. Q. Fang, D.A. Boas, Tetrahedral mesh generation from volumetric binary and grayscale images,
in Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro (IEEE Press, 2009), pp. 1142–1145
16. A.E. Islam, N. Goel, S. Mahapatra, M.A. Alam, Reaction-diffusion model. Springer Series
Adv. Microelectron. 139, 181–207 (2016)
17. X. Chen, R.M. Summers, J. Yao, Kidney tumor growth prediction by coupling reactiondiffusion and biomechanical model. IEEE Trans. Biomed. Eng. 60(1), 169–173 (2013)