  Deep learning models have obtained state-of-the-art results for medical image
analysis. However, when these models are tested on an unseen domain there is a
significant performance degradation. In this work, we present an unsupervised
Cross-Modality Adversarial Domain Adaptation (C-MADA) framework for medical
image segmentation. C-MADA implements an image- and feature-level adaptation
method in a sequential manner. First, images from the source domain are
translated to the target domain through an un-paired image-to-image adversarial
translation with cycle-consistency loss. Then, a U-Net network is trained with
the mapped source domain images and target domain images in an adversarial
manner to learn domain-invariant feature representations. Furthermore, to
improve the networks segmentation performance, information about the shape,
texture, and con-tour of the predicted segmentation is included during the
adversarial train-ing. C-MADA is tested on the task of brain MRI segmentation,
obtaining competitive results.
