Glaucoma is chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays an significant role in clinical screening and early diagnosis of glaucoma. In general, obtaining CDR is subjected to measuring on manually or automatically segmented optic disc and cup. Despite great efforts have been devoted, obtaining CDR values automatically with high accuracy and robustness is still a great challenge due to the heavy overlap between optic cup and neuroretinal rim regions. In this paper, a direct CDR estimation method is proposed based on the well-designed semi-supervised learning scheme, in which CDR estimation is formulated as a general regression problem while optic disc/cup segmentation is cancelled. The method directly regresses CDR value based on the feature representation of optic nerve head via deep learning technique while bypassing intermediate segmentation. The scheme is a two-stage cascaded approach comprised of two phases: unsupervised feature representation of fundus image witha convolutional neural networks (MFPPNet) and CDR value regression by random forest regressor. The proposed scheme is validated on the challenging glaucoma dataset Direct-CSU and public ORIGA, and the experimental results demonstrate that our method can achieve a lower average CDR error of 0.0563 and a higher correlation of around 0.726 with measurement before manual segmentation of optic disc/cup by human experts. Our estimated CDR values are also tested for glaucoma screening, which achieves the areas under curve of 0.905 on dataset of 421 fundus images. The experiments show that the proposed method is capable of state-of-the-art CDR estimation and satisfactory glaucoma screening with calculated CDR value.

中文翻译：

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通过半监督学习对青光眼进行筛查的直接杯碟比估计。

青光眼是一种慢性眼病，会导致不可逆的视力丧失。杯碟比（CDR）是青光眼筛查的最重要指标，在青光眼的临床筛查和早期诊断中起着重要作用。通常，获得CDR的过程需要在手动或自动分段的光盘和杯上进行测量。尽管付出了巨大的努力，但是由于视杯和神经视网膜边缘区域之间的大量重叠，因此以高精度和鲁棒性自动获得CDR值仍然是一个巨大的挑战。本文提出了一种基于精心设计的半监督学习方案的直接CDR估计方法，该方法将CDR估计公式化为一般的回归问题，同时取消了视盘/杯的分割。该方法通过深度学习技术基于视神经头的特征表示直接回归CDR值，而绕过了中间分割。该方案是一个分为两个阶段的两阶段级联方法：使用卷积神经网络（MFPPNet）进行眼底图像的无监督特征表示，以及通过随机森林回归器进行CDR值回归。该方案在具有挑战性的青光眼数据集Direct-CSU和公共ORIGA上得到了验证，实验结果表明，我们的方法与手动分割视盘/由人类专家杯。我们还估算了我们估计的CDR值以进行青光眼筛查，在421个眼底图像的数据集上，曲线下的面积达到0.905。