  Generative models have been widely proposed in image recognition to generate
more images where the distribution is similar to that of the real ones. It
often introduces a discriminator network to differentiate the real data from
the generated ones. Such models utilise a discriminator network tasked with
differentiating style transferred data from data contained in the target
dataset. However in doing so the network focuses on discrepancies in the
intensity distribution and may overlook structural differences between the
datasets. In this paper we formulate a new image-to-image translation problem
to ensure that the structure of the generated images is similar to that in the
target dataset. We propose a simple, yet powerful Structure-Unbiased
Adversarial (SUA) network which accounts for both intensity and structural
differences between the training and test sets when performing image
segmentation. It consists of a spatial transformation block followed by an
intensity distribution rendering module. The spatial transformation block is
proposed to reduce the structure gap between the two images, and also produce
an inverse deformation field to warp the final segmented image back. The
intensity distribution rendering module then renders the deformed structure to
an image with the target intensity distribution. Experimental results show that
the proposed SUA method has the capability to transfer both intensity
distribution and structural content between multiple datasets.
