  Deep learning-based segmentation methods have been widely employed for
automatic glaucoma diagnosis and prognosis. In practice, fundus images obtained
by different fundus cameras vary significantly in terms of illumination and
intensity. Although recent unsupervised domain adaptation (UDA) methods enhance
the models' generalization ability on the unlabeled target fundus datasets,
they always require sufficient labeled data from the source domain, bringing
auxiliary data acquisition and annotation costs. To further facilitate the data
efficiency of the cross-domain segmentation methods on the fundus images, we
explore UDA optic disc and cup segmentation problems using few labeled source
data in this work. We first design a Searching-based Multi-style Invariant
Mechanism to diversify the source data style as well as increase the data
amount. Next, a prototype consistency mechanism on the foreground objects is
proposed to facilitate the feature alignment for each kind of tissue under
different image styles. Moreover, a cross-style self-supervised learning stage
is further designed to improve the segmentation performance on the target
images. Our method has outperformed several state-of-the-art UDA segmentation
methods under the UDA fundus segmentation with few labeled source data.
