论文标题
使用对抗网络的单眼视网膜深度估计以及关节视盘和杯赛分割
Monocular Retinal Depth Estimation and Joint Optic Disc and Cup Segmentation using Adversarial Networks
论文作者
论文摘要
评估青光眼的重要参数之一是视神经头（ONH）评估，该评估通常涉及深度估计以及随后的视盘和杯边界提取。通常从光学相干断层扫描（OCT）（OCT）等成像方式明确获得深度，并且在估算单个RGB图像的深度非常具有挑战性。为此，我们提出了一种使用对抗网络的新方法来预测单个图像的深度图。提出的深度估计技术是使用Inspire-Stereo数据集的单个视网膜图像进行训练和评估的。在五倍交叉验证的情况下，我们获得了非常高的平均相关系数为0.92。然后，我们将深度估计过程用作关节视盘和杯子分段的替代任务。
One of the important parameters for the assessment of glaucoma is optic nerve head (ONH) evaluation, which usually involves depth estimation and subsequent optic disc and cup boundary extraction. Depth is usually obtained explicitly from imaging modalities like optical coherence tomography (OCT) and is very challenging to estimate depth from a single RGB image. To this end, we propose a novel method using adversarial network to predict depth map from a single image. The proposed depth estimation technique is trained and evaluated using individual retinal images from INSPIRE-stereo dataset. We obtain a very high average correlation coefficient of 0.92 upon five fold cross validation outperforming the state of the art. We then use the depth estimation process as a proxy task for joint optic disc and cup segmentation.
