Frequency-Aware Unsupervised Domain Adaptation for Semantic Segmentation of Laparoscopic Images

Semantic segmentation of laparoscopic images requires costly pixel-level annotations, which are often unavailable for real surgical data. This gives rise to an unsupervised domain adaptation scenario, where labeled synthetic images serve as the source domain and unlabeled real images as the target. We propose a frequency-aware unsupervised domain adaptation framework to mitigate the domain gap between simulated and real laparoscopic images. Specifically, we introduce a Radial Frequency Masking module that selectively masks frequency components of real images, and employ a Mean Teacher framework to enforce consistency between high- and low-frequency representations. In addition, we propose a module called Fourier Domain Adaptation-Blend, a style transfer strategy based on low-frequency blending, and apply entropy minimization to enhance prediction confidence on the target domain. Experiments are conducted on public datasets by jointly training on simulated and real laparoscopic images. Our method consistently outperforms representative baselines. These results demonstrate the effectiveness of frequency-aware adaptation in surgical image segmentation without relying on manual annotations from the target domain.