  Deep learning models often encounter challenges in making accurate inferences
when there are domain shifts between the source and target data. This issue is
particularly pronounced in clinical settings due to the scarcity of annotated
data resulting from the professional and private nature of medical data.
Despite the existence of decent solutions, many of them are hindered in
clinical settings due to limitations in data collection and computational
complexity. To tackle domain shifts in data-scarce medical scenarios, we
propose a Random frequency filtering enabled Single-source Domain
Generalization algorithm (RaffeSDG), which promises robust out-of-domain
inference with segmentation models trained on a single-source domain. A
filter-based data augmentation strategy is first proposed to promote domain
variability within a single-source domain by introducing variations in
frequency space and blending homologous samples. Then Gaussian filter-based
structural saliency is also leveraged to learn robust representations across
augmented samples, further facilitating the training of generalizable
segmentation models. To validate the effectiveness of RaffeSDG, we conducted
extensive experiments involving out-of-domain inference on segmentation tasks
for three human tissues imaged by four diverse modalities. Through thorough
investigations and comparisons, compelling evidence was observed in these
experiments, demonstrating the potential and generalizability of RaffeSDG. The
code is available at
https://github.com/liamheng/Non-IID_Medical_Image_Segmentation.
