Normalization methods play a critical role in the performance and generalization of deep neural networks for semantic segmentation, particularly in medical imaging. While batch normalization (BN) is widely used, its limitations, such as reduced performance with small batch sizes, have led to alternative normalization methods, such as instance normalization (IN), layer normalization (LN), group normalization (GN), and filter response normalization (FRN). However, most segmentation networks apply a single normalization method uniformly, without optimizing layer- or task-specific configurations.