  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
