  Medical image segmentation methods normally perform poorly when there is a
domain shift between training and testing data. Unsupervised Domain Adaptation
(UDA) addresses the domain shift problem by training the model using both
labeled data from the source domain and unlabeled data from the target domain.
Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the
source data during the adaptation, due to data privacy or data transmission
issues, which normally adapts the pre-trained deep model in the testing stage.
However, in real clinical scenarios of medical image segmentation, the trained
model is normally frozen in the testing stage. In this paper, we propose
Fourier Visual Prompting (FVP) for SFUDA of medical image segmentation.
Inspired by prompting learning in natural language processing, FVP steers the
frozen pre-trained model to perform well in the target domain by adding a
visual prompt to the input target data. In FVP, the visual prompt is
parameterized using only a small amount of low-frequency learnable parameters
in the input frequency space, and is learned by minimizing the segmentation
loss between the predicted segmentation of the prompted target image and
reliable pseudo segmentation label of the target image under the frozen model.
To our knowledge, FVP is the first work to apply visual prompts to SFUDA for
medical image segmentation. The proposed FVP is validated using three public
datasets, and experiments demonstrate that FVP yields better segmentation
results, compared with various existing methods.
