  Semantic segmentation is an essential step for electron microscopy (EM) image
analysis. Although supervised models have achieved significant progress, the
need for labor intensive pixel-wise annotation is a major limitation. To
complicate matters further, supervised learning models may not generalize well
on a novel dataset due to domain shift. In this study, we introduce an
adversarial-prediction guided multi-task network to learn the adaptation of a
well-trained model for use on a novel unlabeled target domain. Since no label
is available on target domain, we learn an encoding representation not only for
the supervised segmentation on source domain but also for unsupervised
reconstruction of the target data. To improve the discriminative ability with
geometrical cues, we further guide the representation learning by multi-level
adversarial learning in semantic prediction space. Comparisons and ablation
study on public benchmark demonstrated state-of-the-art performance and
effectiveness of our approach.
