  Current deep learning based segmentation models often generalize poorly
between domains due to insufficient training data. In real-world clinical
applications, cross-domain image analysis tools are in high demand since
medical images from different domains are often needed to achieve a precise
diagnosis. An important example in radiology is generalizing from non-contrast
CT to contrast enhanced CTs. Contrast enhanced CT scans at different phases are
used to enhance certain pathologies or organs. Many existing cross-domain
image-to-image translation models have been shown to improve cross-domain
segmentation of large organs. However, such models lack the ability to preserve
fine structures during the translation process, which is significant for many
clinical applications, such as segmenting small calcified plaques in the aorta
and pelvic arteries. In order to preserve fine structures during medical image
translation, we propose a patch-based model using shared latent variables from
a Gaussian mixture model. We compare our image translation framework to several
state-of-the-art methods on cross-domain image translation and show our model
does a better job preserving fine structures. The superior performance of our
model is verified by performing two tasks with the translated images -
detection and segmentation of aortic plaques and pancreas segmentation. We
expect the utility of our framework will extend to other problems beyond
segmentation due to the improved quality of the generated images and enhanced
ability to preserve small structures.
