论文标题
有条件的gan预测青光眼进展的黄斑光学相干断层扫描
Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography
论文作者
论文摘要
青光眼进展的估计是一项具有挑战性的任务，因为除了测量变异性和定义进展方面缺乏标准化之外，个体之间的疾病进展速度也有所不同。结构性测试，例如视网膜神经纤维层的厚度测量或具有光学相干断层扫描（OCT）的黄斑，能够检测到青光眼眼睛的解剖学变化。在任何功能损坏之前，都可以观察到这种变化。在这项工作中，我们使用条件GAN体系结构建立了一种生成深度学习模型，以预测青光眼的发展。从三个或两个先前的测量中预测患者的OCT扫描。预测的图像显示与地面真相图像相似。此外，我们的结果表明，仅从两次先前的访问中获得的OCT扫描实际上可能足以预测六个月后患者的下一个OCT扫描。
The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient's OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.
