  Medical image translation (e.g. CT to MR) is a challenging task as it
requires I) faithful translation of domain-invariant features (e.g. shape
information of anatomical structures) and II) realistic synthesis of
target-domain features (e.g. tissue appearance in MR). In this work, we propose
Manifold Disentanglement Generative Adversarial Network (MDGAN), a novel image
translation framework that explicitly models these two types of features. It
employs a fully convolutional generator to model domain-invariant features, and
it uses style codes to separately model target-domain features as a manifold.
This design aims to explicitly disentangle domain-invariant features and
domain-specific features while gaining individual control of both. The image
translation process is formulated as a stylisation task, where the input is
"stylised" (translated) into diverse target-domain images based on style codes
sampled from the learnt manifold. We test MDGAN for multi-modal medical image
translation, where we create two domain-specific manifold clusters on the
manifold to translate segmentation maps into pseudo-CT and pseudo-MR images,
respectively. We show that by traversing a path across the MR manifold cluster,
the target output can be manipulated while still retaining the shape
information from the input.
