  In medical applications, the same anatomical structures may be observed in
multiple modalities despite the different image characteristics. Currently,
most deep models for multimodal segmentation rely on paired registered images.
However, multimodal paired registered images are difficult to obtain in many
cases. Therefore, developing a model that can segment the target objects from
different modalities with unpaired images is significant for many clinical
applications. In this work, we propose a novel two-stream translation and
segmentation unified attentional generative adversarial network (UAGAN), which
can perform any-to-any image modality translation and segment the target
objects simultaneously in the case where two or more modalities are available.
The translation stream is used to capture modality-invariant features of the
target anatomical structures. In addition, to focus on segmentation-related
features, we add attentional blocks to extract valuable features from the
translation stream. Experiments on three-modality brain tumor segmentation
indicate that UAGAN outperforms the existing methods in most cases.
