  Automatic and accurate segmentation for retinal and choroidal layers of
Optical Coherence Tomography (OCT) is crucial for detection of various ocular
diseases. However, because of the variations in different equipments, OCT data
obtained from different manufacturers might encounter appearance discrepancy,
which could lead to performance fluctuation to a deep neural network. In this
paper, we propose an uncertainty-guided domain alignment method to aim at
alleviating this problem to transfer discriminative knowledge across distinct
domains. We disign a novel uncertainty-guided cross-entropy loss for boosting
the performance over areas with high uncertainty. An uncertainty-guided
curriculum transfer strategy is developed for the self-training (ST), which
regards uncertainty as efficient and effective guidance to optimize the
learning process in target domain. Adversarial learning with feature
recalibration module (FRM) is applied to transfer informative knowledge from
the domain feature spaces adaptively. The experiments on two OCT datasets show
that the proposed methods can obtain significant segmentation improvements
compared with the baseline models.
