  Our contribution is a unified cross-modality feature disentagling approach
for multi-domain image translation and multiple organ segmentation. Using CT as
the labeled source domain, our approach learns to segment multi-modal
(T1-weighted and T2-weighted) MRI having no labeled data. Our approach uses a
variational auto-encoder (VAE) to disentangle the image content from style. The
VAE constrains the style feature encoding to match a universal prior (Gaussian)
that is assumed to span the styles of all the source and target modalities. The
extracted image style is converted into a latent style scaling code, which
modulates the generator to produce multi-modality images according to the
target domain code from the image content features. Finally, we introduce a
joint distribution matching discriminator that combines the translated images
with task-relevant segmentation probability maps to further constrain and
regularize image-to-image (I2I) translations. We performed extensive
comparisons to multiple state-of-the-art I2I translation and segmentation
methods. Our approach resulted in the lowest average multi-domain image
reconstruction error of 1.34$\pm$0.04. Our approach produced an average Dice
similarity coefficient (DSC) of 0.85 for T1w and 0.90 for T2w MRI for
multi-organ segmentation, which was highly comparable to a fully supervised MRI
multi-organ segmentation network (DSC of 0.86 for T1w and 0.90 for T2w MRI).
