Adapting a medical image segmentation model to a new domain is important for
improving its cross-domain transferability, and due to the expensive annotation
process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled
images are needed for the adaptation. Existing UDA methods are mainly based on
image or feature alignment with adversarial training for regularization, and
they are limited by insufficient supervision in the target domain. In this
paper, we propose an enhanced Filtered Pseudo Label (FPL+)-based UDA method for
3D medical image segmentation. It first uses cross-domain data augmentation to
translate labeled images in the source domain to a dual-domain training set
consisting of a pseudo source-domain set and a pseudo target-domain set. To
leverage the dual-domain augmented images to train a pseudo label generator,
domain-specific batch normalization layers are used to deal with the domain
shift while learning the domain-invariant structure features, generating
high-quality pseudo labels for target-domain images. We then combine labeled
source-domain images and target-domain images with pseudo labels to train a
final segmentor, where image-level weighting based on uncertainty estimation
and pixel-level weighting based on dual-domain consensus are proposed to
mitigate the adverse effect of noisy pseudo labels. Experiments on three public
multi-modal datasets for Vestibular Schwannoma, brain tumor and whole heart
segmentation show that our method surpassed ten state-of-the-art UDA methods,
and it even achieved better results than fully supervised learning in the
target domain in some cases.

提出了一种基于增强型筛选伪标签的无监督域自适应 (Unsupervised Domain Adaptation, UDA) 方法，用于三维医学图像分割，通过交叉域数据增强生成高质量伪标签，结合标注的源域图像和目标域伪标签进行模型训练，在多个公共多模态数据集上表现优于十种先进的 UDA 方法，并在某些情况下甚至超过了全监督学习的效果。