  Multi-domain data are widely leveraged in vision applications taking
advantage of complementary information from different modalities, e.g., brain
tumor segmentation from multi-parametric magnetic resonance imaging (MRI).
However, due to possible data corruption and different imaging protocols, the
availability of images for each domain could vary amongst multiple data sources
in practice, which makes it challenging to build a universal model with a
varied set of input data. To tackle this problem, we propose a general approach
to complete the random missing domain(s) data in real applications.
Specifically, we develop a novel multi-domain image completion method that
utilizes a generative adversarial network (GAN) with a representational
disentanglement scheme to extract shared skeleton encoding and separate flesh
encoding across multiple domains. We further illustrate that the learned
representation in multi-domain image completion could be leveraged for
high-level tasks, e.g., segmentation, by introducing a unified framework
consisting of image completion and segmentation with a shared content encoder.
The experiments demonstrate consistent performance improvement on three
datasets for brain tumor segmentation, prostate segmentation, and facial
expression image completion respectively.
