PURPOSE The cup-to-disc ratio (CDR), a clinical metric of the relative size of the optic cup to the optic disc, is a key indicator of glaucoma, a chronic eye disease leading to loss of vision. CDR can be measured from fundus images through the segmentation of optic disc and optic cup . Deep convolutional networks have been proposed to achieve biomedical image segmentation with less time and more accuracy, but requires large amounts of annotated training data on a target domain, which is often unavailable. Unsupervised domain adaptation framework alleviates this problem through leveraging off-the-shelf labeled data from its relevant source domains, which is realized by learning domain invariant features and improving the generalization capabilities of the segmentation model. METHODS In this paper, we propose a WGAN domain adaptation framework for detecting optic disc-and-cup boundary in fundus images. Specifically, we build a novel adversarial domain adaptation framework that is guided by Wasserstein distance, therefore with better stability and convergence than typical adversarial methods. We finally evaluate our approach on publicly available datasets. RESULTS Our experiments show that the proposed approach improves Intersection-over-Union score for optic disc-and-cup segmentation, Dice score and reduces the root-mean-square error of cup-to-disc ratio, when we compare it with direct transfer learning and other state-of-the-art adversarial domain adaptation methods. CONCLUSION With this work, we demonstrate that WGAN guided domain adaptation obtains a state-of-the-art performance for the joint optic disc-and-cup segmentation in fundus images.

中文翻译：

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WGAN域自适应用于眼底图像中联合视盘和杯的分割。

目的杯碟比（CDR）是视杯相对于视盘的相对大小的临床指标，是青光眼（一种导致视力丧失的慢性眼病）的关键指标。可以通过视盘和视杯的分割从眼底图像中测量CDR。已经提出了深度卷积网络以更少的时间和更高的精度实现生物医学图像分割，但是需要在目标域上使用大量带注释的训练数据，而这通常是不可用的。无监督域自适应框架通过利用来自其相关源域的现成标记数据来缓解此问题，这是通过学习域不变特征并提高分割模型的泛化能力来实现的。方法在本文中，我们提出了一种WGAN域自适应框架，用于检测眼底图像中的视盘和杯边缘。具体来说，我们建立了一个由Wasserstein距离指导的新颖的对抗域适应框架，因此比典型的对抗方法具有更好的稳定性和收敛性。最后，我们在公开可用的数据集上评估我们的方法。结果我们的实验表明，与直接转移进行比较时，所提出的方法提高了视盘和杯分割的联合相交分数，Dice分数并降低了杯碟比的均方根误差学习和其他最新的对抗域适应方法。结论这项工作