  We propose an unsupervised domain adaptation (UDA) approach for white matter
hyperintensity (WMH) segmentation, which uses Self-Training with Uncertainty
DEpendent Label refinement (STRUDEL). Self-training has recently been
introduced as a highly effective method for UDA, which is based on
self-generated pseudo labels. However, pseudo labels can be very noisy and
therefore deteriorate model performance. We propose to predict the uncertainty
of pseudo labels and integrate it in the training process with an
uncertainty-guided loss function to highlight labels with high certainty.
STRUDEL is further improved by incorporating the segmentation output of an
existing method in the pseudo label generation that showed high robustness for
WMH segmentation. In our experiments, we evaluate STRUDEL with a standard U-Net
and a modified network with a higher receptive field. Our results on WMH
segmentation across datasets demonstrate the significant improvement of STRUDEL
with respect to standard self-training.
