  Convolutional networks (ConvNets) have achieved great successes in various
challenging vision tasks. However, the performance of ConvNets would degrade
when encountering the domain shift. The domain adaptation is more significant
while challenging in the field of biomedical image analysis, where
cross-modality data have largely different distributions. Given that annotating
the medical data is especially expensive, the supervised transfer learning
approaches are not quite optimal. In this paper, we propose an unsupervised
domain adaptation framework with adversarial learning for cross-modality
biomedical image segmentations. Specifically, our model is based on a dilated
fully convolutional network for pixel-wise prediction. Moreover, we build a
plug-and-play domain adaptation module (DAM) to map the target input to
features which are aligned with source domain feature space. A domain critic
module (DCM) is set up for discriminating the feature space of both domains. We
optimize the DAM and DCM via an adversarial loss without using any target
domain label. Our proposed method is validated by adapting a ConvNet trained
with MRI images to unpaired CT data for cardiac structures segmentations, and
achieved very promising results.
