  Degeneration of articular cartilage (AC) is actively studied in knee
osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation
of AC tissues from MRI data is an essential step in quantification of their
damage. Deep learning (DL) based methods have shown potential in this realm and
are the current state-of-the-art, however, their robustness to heterogeneity of
MRI acquisition settings remains an open problem. In this study, we
investigated two modern regularization techniques -- mixup and adversarial
unsupervised domain adaptation (UDA) -- to improve the robustness of DL-based
knee cartilage segmentation to new MRI acquisition settings. Our validation
setup included two datasets produced by different MRI scanners and using
distinct data acquisition protocols. We assessed the robustness of automatic
segmentation by comparing mixup and UDA approaches to a strong baseline method
at different OA severity stages and, additionally, in relation to anatomical
locations. Our results showed that for moderate changes in knee MRI data
acquisition settings both approaches may provide notable improvements in the
robustness, which are consistent for all stages of the disease and affect the
clinically important areas of the knee joint. However, mixup may be considered
as a recommended approach, since it is more computationally efficient and does
not require additional data from the target acquisition setup.
