  Magnetic resonance imaging (MRI) provides varying tissue contrast images of
internal organs based on a strong magnetic field. Despite the non-invasive
advantage of MRI in frequent imaging, the low contrast MR images in the target
area make tissue segmentation a challenging problem. This paper demonstrates
the potential benefits of image-to-image translation techniques to generate
synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle
generative adversarial network (CycleGAN) with an attention mechanism to
increase the contrast within underlying tissues. The attention block, as well
as training on HTC images, guides our model to converge on certain tissues. To
increase the resolution of HTC images, we employ multi-stage architecture to
focus on one particular tissue as a foreground and filter out the irrelevant
background in each stage. This multi-stage structure also alleviates the common
artifacts of the synthetic images by decreasing the gap between source and
target domains. We show the application of our method for synthesizing HTC
images on brain MR scans, including glioma tumor. We also employ HTC MR images
in both the end-to-end and two-stage segmentation structure to confirm the
effectiveness of these images. The experiments over three competitive
segmentation baselines on BraTS 2018 dataset indicate that incorporating the
synthetic HTC images in the multi-modal segmentation framework improves the
average Dice scores 0.8%, 0.6%, and 0.5% on the whole tumor, tumor core, and
enhancing tumor, respectively, while eliminating one real MRI sequence from the
segmentation procedure.
