Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model

Current deep learning based segmentation models generalize poorly to different domains due to the lack of sufficient labelled image data. An important example in radiology is generalizing from contrast enhanced CT to non-contrast CT. In real-world clinical applications, cross-domain image analysis tools are in high demand since medical images from different domains are generally used to achieve precise diagnoses. For example, contrast enhanced CT at different phases are used to enhance certain pathologies or internal organs. Many existing cross-domain image-to-image translation models show impressive results on large organ segmentation by successfully preserving large structures across domains. 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. 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.