  For medical image segmentation, imagine if a model was only trained using MR
images in source domain, how about its performance to directly segment CT
images in target domain? This setting, namely generalizable cross-modality
segmentation, owning its clinical potential, is much more challenging than
other related settings, e.g., domain adaptation. To achieve this goal, we in
this paper propose a novel dual-normalization model by leveraging the augmented
source-similar and source-dissimilar images during our generalizable
segmentation. To be specific, given a single source domain, aiming to simulate
the possible appearance change in unseen target domains, we first utilize a
nonlinear transformation to augment source-similar and source-dissimilar
images. Then, to sufficiently exploit these two types of augmentations, our
proposed dual-normalization based model employs a shared backbone yet
independent batch normalization layer for separate normalization. Afterward, we
put forward a style-based selection scheme to automatically choose the
appropriate path in the test stage. Extensive experiments on three publicly
available datasets, i.e., BraTS, Cross-Modality Cardiac, and Abdominal
Multi-Organ datasets, have demonstrated that our method outperforms other
state-of-the-art domain generalization methods. Code is available at
https://github.com/zzzqzhou/Dual-Normalization.
