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.

中文翻译：

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FPL+： 用于 3D 医学图像分割的基于过滤伪标签的无监督跨模态适应


将医学图像分割模型适应新域对于提高其跨域可转移性非常重要，并且由于昂贵的注释过程，无监督域适应 （UDA） 在只需要未标记图像进行适应的情况下很有吸引力。现有的 UDA 方法主要基于图像或特征对齐和对抗性训练进行正则化，并且受到目标域监督不足的限制。在本文中，我们提出了一种增强的基于过滤伪标签 （FPL+） 的 UDA 方法，用于 3D 医学图像分割。它首先使用跨域数据增强将源域中的标记图像转换为由伪源-域集和伪目标-域集组成的双域训练集。为了利用双域增强图像来训练伪标签生成器，使用特定于域的批量归一化层来处理域偏移，同时学习域不变结构特征，为目标域图像生成高质量的伪标签。然后，我们将标记的源域图像和带有伪标签的目标域图像结合起来，以训练最终的分割器，其中提出了基于不确定性估计的图像级加权和基于双域共识的像素级加权，以减轻噪声伪标签的不利影响。在前庭神经鞘瘤、脑肿瘤和全心脏分割的三个公共多模态数据集上的实验表明，我们的方法超过了十种最先进的 UDA 方法，在某些情况下，它甚至取得了比目标域的完全监督学习更好的结果。