IEEE J Biomed Health Inform. 2017 Nov;21(6):1667-1674. doi: 10.1109/JBHI.2017.2702603. Epub 2017 May 16.
Choroid neovascularization (CNV) is caused by new blood vessels growing in the choroid and penetrating the bruch membrane. It is the major cause of vision disability in many retinal diseases. Though anti-vascular endothelial growth factor injection has proved to be effective for treating CNV, treatment planning is essential to ensure the efficacy while reducing the risk. For this purpose, we propose a CNV growth model based on longitudinal optical coherence tomography (OCT) images. The reaction-diffusion model is applied to simulate the growth and shrinkage of CNV volumes, and is solved by using the finite-element method. A fitted curve of the CNV growth/shrinkage rate is obtained by optimizing the growth parameters. Then, the trained parameters are applied to the predicted image to get the simulated image, which is compared with the validated image to evaluate the accuracy of prediction. The proposed method was tested on a dataset with seven patients in which each patient has 12 longitudinal OCT images. The resulted mean dice coefficient is 76.40% ± 8.20%. The experimental results show a promising step towards the image-guided patient-specific treatment.
脉络膜新生血管（CNV）是由脉络膜中新血管生长并穿透布鲁赫膜引起的。它是许多视网膜疾病导致视力障碍的主要原因。尽管抗血管内皮生长因子注射已被证明对治疗 CNV 有效，但为了确保疗效并降低风险，治疗计划至关重要。为此，我们提出了一种基于纵向光学相干断层扫描（OCT）图像的 CNV 生长模型。应用反应扩散模型来模拟 CNV 体积的生长和收缩，并使用有限元法求解。通过优化生长参数，得到 CNV 生长/收缩率的拟合曲线。然后，将训练好的参数应用于预测图像，得到模拟图像，并将其与验证图像进行比较，以评估预测的准确性。该方法在一个包含 7 名患者的数据集上进行了测试，每个患者有 12 个纵向 OCT 图像。得到的平均骰子系数为 76.40%±8.20%。实验结果表明，该方法朝着图像引导的个体化治疗迈出了有希望的一步。
