Learning Frequency Adaptation for Cross-domain Medical Image Segmentation

In medical image segmentation, some recent methods improve domain adaptation performance through frequency domain adaptation and frequency mixup. However, these approaches have two limitations: (1) frequency domain adaptation ignores the adverse effects of high-frequency noise on model generalization, and (2) frequency mixup confuses semantic information. To address these issues, we propose a novel frequency adaptation approach for medical image segmentation including low-frequency component alignment (LFCA) and random amplitude cutmix (RAC). Since low-frequency contains major image information and high-frequency noise affects adaptation, we leverage discrete wavelet transform to decompose images into low and high-frequency components. LFCA aligns the domain distribution of low frequencies and high frequencies are passed to the decoder via skip connections. In addition, we design RAC to generate diverse augmented samples through amplitude cutmix while avoiding distortion of the original distributions. Experiments on benchmark datasets validate the efficacy of our proposed approach.