  Generalization capabilities of learning-based medical image segmentation
across domains are currently limited by the performance degradation caused by
the domain shift, particularly for ultrasound (US) imaging. The quality of US
images heavily relies on carefully tuned acoustic parameters, which vary across
sonographers, machines, and settings. To improve the generalizability on US
images across domains, we propose MI-SegNet, a novel mutual information (MI)
based framework to explicitly disentangle the anatomical and domain feature
representations; therefore, robust domain-independent segmentation can be
expected. Two encoders are employed to extract the relevant features for the
disentanglement. The segmentation only uses the anatomical feature map for its
prediction. In order to force the encoders to learn meaningful feature
representations a cross-reconstruction method is used during training.
Transformations, specific to either domain or anatomy are applied to guide the
encoders in their respective feature extraction task. Additionally, any MI
present in both feature maps is punished to further promote separate feature
spaces. We validate the generalizability of the proposed domain-independent
segmentation approach on several datasets with varying parameters and machines.
Furthermore, we demonstrate the effectiveness of the proposed MI-SegNet serving
as a pre-trained model by comparing it with state-of-the-art networks.
