FPL+：基于过滤伪标签的无监督跨模态自适应方法用于三维医学图像分割
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
AI总结：
本文提出FPL+方法，通过跨域数据增强和域特定批归一化生成高质量伪标签，并结合不确定性估计与双域一致性加权，在三维医学图像分割的无监督跨模态自适应中超越十种现有方法。
摘要：
使医学图像分割模型适应新域对于提高其跨域迁移能力至关重要，而由于标注过程昂贵，无监督域自适应（UDA）具有吸引力，因为自适应过程只需要未标注图像。现有的UDA方法主要基于图像或特征对齐并结合对抗训练进行正则化，它们受限于目标域中监督信息不足。本文提出了一种增强的基于过滤伪标签（FPL+）的UDA方法，用于三维医学图像分割。该方法首先利用跨域数据增强，将源域中的已标注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训练伪标签生成器，采用域特定的批归一化层来处理域偏移，同时学习域不变的结构特征，从而为目标域图像生成高质量的伪标签。然后，我们将已标注的源域图像和带有伪标签的目标域图像结合起来训练最终的分割器，其中提出了基于不确定性估计的图像级加权和基于双域一致性的像素级加权，以减轻噪声伪标签的不利影响。在三个用于前庭神经鞘瘤、脑肿瘤和全心分割的公开多模态数据集上的实验表明，我们的方法超越了十种最先进的UDA方法，并且在某些情况下甚至在目标域中取得了比全监督学习更好的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
