  Since stroke is the main cause of various cerebrovascular diseases, deep
learning-based stroke lesion segmentation on magnetic resonance (MR) images has
attracted considerable attention. However, the existing methods often neglect
the domain shift among MR images collected from different sites, which has
limited performance improvement. To address this problem, we intend to change
style information without affecting high-level semantics via adaptively
changing the low-frequency amplitude components of the Fourier transform so as
to enhance model robustness to varying domains. Thus, we propose a novel
FAN-Net, a U-Net--based segmentation network incorporated with a Fourier-based
adaptive normalization (FAN) and a domain classifier with a gradient reversal
layer. The FAN module is tailored for learning adaptive affine parameters for
the amplitude components of different domains, which can dynamically normalize
the style information of source images. Then, the domain classifier provides
domain-agnostic knowledge to endow FAN with strong domain generalizability. The
experimental results on the ATLAS dataset, which consists of MR images from 9
sites, show the superior performance of the proposed FAN-Net compared with
baseline methods.
