Domain Adaptation (DA) is important for deep learning-based medical image
segmentation models to deal with testing images from a new target domain. As
the source-domain data are usually unavailable when a trained model is deployed
at a new center, Source-Free Domain Adaptation (SFDA) is appealing for data and
annotation-efficient adaptation to the target domain. However, existing SFDA
methods have a limited performance due to lack of sufficient supervision with
source-domain images unavailable and target-domain images unlabeled. We propose
a novel Uncertainty-aware Pseudo Label guided (UPL) SFDA method for medical
image segmentation. Specifically, we propose Target Domain Growing (TDG) to
enhance the diversity of predictions in the target domain by duplicating the
pre-trained model's prediction head multiple times with perturbations. The
different predictions in these duplicated heads are used to obtain pseudo
labels for unlabeled target-domain images and their uncertainty to identify
reliable pseudo labels. We also propose a Twice Forward pass Supervision (TFS)
strategy that uses reliable pseudo labels obtained in one forward pass to
supervise predictions in the next forward pass. The adaptation is further
regularized by a mean prediction-based entropy minimization term that
encourages confident and consistent results in different prediction heads.
UPL-SFDA was validated with a multi-site heart MRI segmentation dataset, a
cross-modality fetal brain segmentation dataset, and a 3D fetal tissue
segmentation dataset. It improved the average Dice by 5.54, 5.01 and 6.89
percentage points for the three tasks compared with the baseline, respectively,
and outperformed several state-of-the-art SFDA methods.

我们提出了一种新颖的基于不确定性伪标签引导的源域自由领域适应方法，通过目标领域增长和两次前向传递监督来提高医学图像分割的多样性和准确性，获得可靠的伪标签并进一步优化自适应性。在多个数据集上的实验证明，与基准方法相比，我们的方法在三个任务中平均提高了 5.54％，5.01％和 6.89％的 Dice 系数。