3D choroid neovascularization growth prediction based on reaction-diffusion model

Shuxia Zhu; Xinjian Chen; Fei Shi; Dehui Xiang; Weifang Zhu; Haoyu Chen

doi:10.1117/12.2216188

29 March 2016 3D choroid neovascularization growth prediction based on reaction-diffusion model

Shuxia Zhu, Xinjian Chen, Fei Shi, Dehui Xiang, Weifang Zhu, Haoyu Chen

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 978807 (2016) https://doi.org/10.1117/12.2216188
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Choroid neovascularization (CNV) is a kind of pathology from the choroid and CNV-related disease is one important cause of vision loss. It is desirable to predict the CNV growth rate so that appropriate treatment can be planned. In this paper, we seek to find a method to predict the growth of CNV based on 3D longitudinal Optical Coherence Tomography (OCT) images. A reaction-diffusion model is proposed for prediction. The method consists of four phases: pre-processing, meshing, CNV growth modeling and prediction. We not only apply the reaction-diffusion model to the disease region, but also take the surrounding tissues into consideration including outer retinal layer, inner retinal layer and choroid layer. The diffusion in these tissues is considered as isotropic. The finite-element-method (FEM) is used to solve the partial differential equations (PDE) in the diffusion model. The curve of CNV growth with treatment are fitted and then we can predict the CNV status in a future time point. The preliminary results demonstrated that our proposed method is accurate and the validity and feasibility of our model is obvious.

Citation Download Citation

Shuxia Zhu, Xinjian Chen, Fei Shi, Dehui Xiang, Weifang Zhu, and Haoyu Chen "3D choroid neovascularization growth prediction based on reaction-diffusion model", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 978807 (29 March 2016); https://doi.org/10.1117/12.2216188

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