Semi-Nonlinear Self-Adaptation Normalization Generator (SNSAN) to Improve Generalization of White Matter Hyperintensities Segmentation

Motivation: Self-adaptation normalization (SAN) method generalizes lesion segmentation to new sites by using a linear generator to convert inputs to a site-independent style. But it fails when the domain difference is mostly nonlinear. Goal(s): Develop a semi-nonlinear self-adaptation network(SNSAN) to generalize the White Matter Hyperintensity(WMH) segmentation model to an external site. Approach: To replace the linear generator, the method blends two SAN results with a pseudo correlation map, later use the gradient reversal method to guide the result to a site-unrelated style. Results: SNSAN normalizes the input data close to a Gaussian distribution and improves the generalization performance on the data from external site. Impact: We provide a simple and efficient semi-nonlinear normalization method to enhance the domain generalization, and its performance is better than SAN when the domain gap is affected by more nonlinear factors.