  Despite the widespread use of deep learning methods for semantic segmentation
of images that are acquired from a single source, clinicians often use
multi-domain data for a detailed analysis. For instance, CT and MRI have
advantages over each other in terms of imaging quality, artifacts, and output
characteristics that lead to differential diagnosis. The capacity of current
segmentation techniques is only allow to work for an individual domain due to
their differences. However, the models that are capable of working on all
modalities are essentially needed for a complete solution. Furthermore,
robustness is drastically affected by the number of samples in the training
step, especially for deep learning models. Hence, there is a necessity that all
available data regardless of data domain should be used for reliable methods.
For this purpose, this manuscript aims to implement a novel model that can
learn robust representations from cross-domain data by encapsulating distinct
and shared patterns from different modalities. Precisely, covariate shift
property is retained with structural modification and adversarial loss where
sparse and rich representations are obtained. Hence, a single parameter set is
used to perform cross-domain segmentation task. The superiority of the proposed
method is that no information related to modalities are provided in either
training or inference phase. The tests on CT and MRI liver data acquired in
routine clinical workflows show that the proposed model outperforms all other
baseline with a large margin. Experiments are also conducted on Covid-19
dataset that it consists of CT data where significant intra-class visual
differences are observed. Similarly, the proposed method achieves the best
performance.
