Abstract
Choroid neovascularization (CNV) usually causes varying degrees of irreversible retinal degradation, central scotoma, metamorphopsia or permanent visual lose. If early prediction can be achieved, timely clinical treatment can be applied to prevent further deterioration. In this paper, a CNV growth prediction framework based on physiological structure revealed in noninvasive optical coherence tomography (OCT) images is proposed. The method consists of three steps: pre-processing, CNV growth modeling and prediction. For growth modeling, a new combination model is proposed. The hyperelastic biomechanical model and reaction-diffusion model with treatment factor are combined through mass effect. For parameter optimization, the genetic algorithm is applied. The proposed method was tested on a data set with 6 subjects, each with 12 longitudinal 3-D images. The experimental results showed that the average TPVF, FPVF and Dice coefficient of 80.0 ± 7.62%, 23.4 ± 8.36% and 78.9 ± 7.54% could be achieved, respectively.
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Spilsbury, K., Garrett, K.L., Shen, W.Y., Constable, I.J., Rakoczy, P.E.: Overexpression of Vascular Endothelial Growth Factor (VEGF) in the retinal pigment epithelium leads to the development of choroidal neovascularization. Am. J. Pathol. 157(1), 135–144 (2000)
Ahn, S.J., Woo, S.J., Kim, K.E., Park, K.H.: Association between choroidal morphology and anti-vascular endothelial growth factor treatment outcome in myopic choroidal neovascularization. Invest. Ophthalmol. Vis. Sci. 54(3), 2115–2122 (2013)
Mueller, A.J., Freeman, W.R., Schaller, U.C., Kampik, A., Folberg, R.: Complex microcirculation patterns detected by confocal indocyanine green angiography predict time to growth of small choroidal melanocytic tumors. Ophthalmology 109(12), 2207–2214 (2002)
Zhu, S., Chen, X., Shi, F., Xiang, D., Zhu, W., Chen, H.: 3D choroid neovascularization growth prediction based on reaction-diffusion model. In: Gimi, B., Krol, A. (eds.) Proceedings of SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, San Diego, California, United States, vol. 9788 (2016)
Zhu, S., Shi, F., Xiang, D., Zhu, W., Chen, X., Chen, H.: Choroid neovascularization growth prediction with treatment based on reaction-diffusion model in 3D OCT images. IEEE J. Biomed. Health Inf. PP(99), 2168–2194 (2017)
Clatz, O., Sermesant, M., Bondiau, P.-Y., Delingette, H., Warfield, S.K., Malandain, G., Ayache, N.: Realistic simulation of the 3D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. IEEE Trans. Med. Imaging 24(10), 1334–1346 (2005)
Chen, X., Summers, R.M., Yao, J.: Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model. IEEE Trans. Biomed. Eng. 60(1), 169–173 (2013)
Mohamed, A., Davatzikos, C.: Finite element modeling of brain tumor mass-effect from 3D medical images. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 400–408. Springer, Heidelberg (2005). doi:10.1007/11566465_50
Wong, K.C.L., Summers, R.M., Kebebew, E., Yao, J.: Tumor growth prediction with hyperelastic biomechanical model, physiological data fusion, and nonlinear optimization. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 25–32. Springer, Cham (2014). doi:10.1007/978-3-319-10470-6_4
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Fang, Q.: ISO2Mesh: a 3D surface and volumetric mesh generator for MATLAB/octave. http://iso2mesh.sourceforge.net/cgi-bin/index.cgi. Accessed 16 Jan 2017
Bathe, K.J.: Finite Element Procedures. Prentice Hall, Upper Saddle River (1996)
Hoge, C., Davatzikos, C., Biros, G.: An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effects. J. Math. Biol. 56(6), 793–825 (2008)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–407 (1998)
Martin, D.F., Klein, M., Haller, J.: Preclinical and phase 1A clinical evaluation of an anti-VEGF pegylated aptamer (EYE001) for the treatment of exudative age-related macular degeneration. Retina J. Retin. Vitr. Dis. 22, 143–152 (2002)
Holzapfel, G.A.: Nonlinear Solid Mechanics: A Continuum Approach for Engineering. Wiley, New York (2000)
Rangarajan, N., Kamalakkannan, S.B., Hasija, V., Shams, T., Jenny, C., et al.: Finite element model of ocular injury in abusive head trauma. J. Am. Assoc. Pediatr. Ophthalmol. Strabismus 13(4), 364–369 (2009)
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Zuo, C., Shi, F., Zhu, W., Chen, H., Chen, X. (2017). 3D Choroid Neovascularization Growth Prediction with Combined Hyperelastic Biomechanical Model and Reaction-Diffusion Model. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_16
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