  There are considerable interests in automatic stroke lesion segmentation on
magnetic resonance (MR) images in the medical imaging field, as stroke is an
important cerebrovascular disease. Although deep learning-based models have
been proposed for this task, generalizing these models to unseen sites is
difficult due to not only the large inter-site discrepancy among different
scanners, imaging protocols, and populations, but also the variations in stroke
lesion shape, size, and location. To tackle this issue, we introduce a
self-adaptive normalization network, termed SAN-Net, to achieve adaptive
generalization on unseen sites for stroke lesion segmentation. Motivated by
traditional z-score normalization and dynamic network, we devise a masked
adaptive instance normalization (MAIN) to minimize inter-site discrepancies,
which standardizes input MR images from different sites into a site-unrelated
style by dynamically learning affine parameters from the input; \ie, MAIN can
affinely transform the intensity values. Then, we leverage a gradient reversal
layer to force the U-net encoder to learn site-invariant representation with a
site classifier, which further improves the model generalization in conjunction
with MAIN. Finally, inspired by the ``pseudosymmetry'' of the human brain, we
introduce a simple yet effective data augmentation technique, termed
symmetry-inspired data augmentation (SIDA), that can be embedded within SAN-Net
to double the sample size while halving memory consumption. Experimental
results on the benchmark Anatomical Tracings of Lesions After Stroke (ATLAS)
v1.2 dataset, which includes MR images from 9 different sites, demonstrate that
under the ``leave-one-site-out'' setting, the proposed SAN-Net outperforms
recently published methods in terms of quantitative metrics and qualitative
comparisons.
