  Source-Free Unsupervised Domain Adaptation (SFUDA) has recently become a
focus in the medical image domain adaptation, as it only utilizes the source
model and does not require annotated target data. However, current SFUDA
approaches cannot tackle the complex segmentation task across different MRI
sequences, such as the vestibular schwannoma segmentation. To address this
problem, we proposed Reliable Source Approximation (RSA), which can generate
source-like and structure-preserved images from the target domain for updating
model parameters and adapting domain shifts. Specifically, RSA deploys a
conditional diffusion model to generate multiple source-like images under the
guidance of varying edges of one target image. An uncertainty estimation module
is then introduced to predict and refine reliable pseudo labels of generated
images, and the prediction consistency is developed to select the most reliable
generations. Subsequently, all reliable generated images and their pseudo
labels are utilized to update the model. Our RSA is validated on vestibular
schwannoma segmentation across multi-modality MRI. The experimental results
demonstrate that RSA consistently improves domain adaptation performance over
other state-of-the-art SFUDA methods. Code is available at
https://github.com/zenghy96/Reliable-Source-Approximation.
