Glaucoma is one of the leading causes of irreversible blindness. Segmentation of optic disc (OD) and optic cup (OC) on fundus images is a crucial step in glaucoma screening. Although many deep learning models have been constructed for this task, it remains challenging to train an OD/OC segmentation model that could be deployed successfully to different healthcare centers. The difficulties mainly comes from the domain shift issue, i.e., the fundus images collected at these centers usually vary greatly in the tone, contrast, and brightness. To address this issue, in this paper, we propose a novel unsupervised domain adaptation (UDA) method called Reconstruction-driven Dynamic Refinement Network (RDR-Net), where we employ a due-path segmentation backbone for simultaneous edge detection and region prediction and design three modules to alleviate the domain gap. The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way. It also uses a style-consistency constraint to force the network to retain more domain-invariant information. The low-level feature refinement (LFR) module employs input-specific dynamic convolutions to suppress the domain-variant information in the obtained low-level features. The prediction-map alignment (PMA) module elaborates the entropy-driven adversarial learning to encourage the network to generate source-like boundaries and regions. We evaluated our RDR-Net against state-of-the-art solutions on four public fundus image datasets. Our results indicate that RDR-Net is superior to competing models in both segmentation performance and generalization ability

翻译：青光眼是导致不可逆失明的主要原因之一。在眼底图像上分割视盘和视杯是青光眼筛查中的关键步骤。尽管已有许多深度学习模型被用于此任务，但训练一个能够成功部署到不同医疗中心的视盘/视杯分割模型仍具挑战性。主要困难源于域偏移问题，即这些医疗中心采集的眼底图像在色调、对比度和亮度上通常差异较大。为解决这一问题，本文提出一种新颖的无监督域自适应方法——重建驱动动态精细化网络（RDR-Net）。我们采用双路径分割骨干网络同时进行边缘检测和区域预测，并设计了三个模块来缓解域差距。重建对齐模块使用变分自编码器重建输入图像，从而以自监督方式增强网络的图像表示能力，同时通过风格一致性约束强制网络保留更多域不变信息。低层特征精细化模块采用输入特定的动态卷积抑制所获低层特征中的域变异信息。预测图对齐模块通过熵驱动的对抗学习，促使网络生成类似源域的边界和区域。我们在四个公开眼底图像数据集上评估了RDR-Net与现有最优方法的性能。结果表明，RDR-Net在分割性能和泛化能力上均优于竞争模型。