Unsupervised domain adaptation (UDA) has increasingly gained interests for its capacity to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. However, typical UDA methods require concurrent access to both the source and target domain data, which largely limits its application in medical scenarios where source data is often unavailable due to privacy concern. To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation. Specifically, in the prototype-anchored feature alignment stage, we first utilize the weights of the pre-trained pixel-wise classifier as source prototypes, which preserve the information of source features. Then, we introduce the bi-directional transport to align the target features with class prototypes by minimizing its expected cost. On top of that, a contrastive learning stage is further devised to utilize those pixels with unreliable predictions for a more compact target feature distribution. Extensive experiments on a cross-modality medical segmentation task demonstrate the superiority of our method in large domain discrepancy settings compared with the state-of-the-art SFDA approaches and even some UDA methods. Code is available at https://github.com/CSCYQJ/MICCAI23-ProtoContra-SFDA.

翻译：无监督域自适应（UDA）因其能将标注源域学到的知识迁移至未标注目标域而日益受到关注。然而，典型的UDA方法需同时访问源域和目标域数据，这在医疗场景中因隐私问题常导致源数据不可用而受到极大限制。为解决源数据缺失问题，我们提出一种新颖的两阶段无源域自适应（SFDA）框架用于医学图像分割，在域自适应过程中仅需利用预训练的源分割模型与未标注的目标域数据。具体而言，在原型锚定特征对齐阶段，我们首先将预训练逐像素分类器的权重作为保留源特征信息的源原型，随后引入双向传输机制通过最小化期望代价来对齐目标特征与类别原型。在此基础上，进一步设计对比学习阶段，利用预测不可靠的像素实现更紧凑的目标特征分布。跨模态医学分割任务的大量实验表明，在较大域差异场景下，本方法相较现有最优SFDA方法甚至部分UDA方法均展现出优越性。代码开源于https://github.com/CSCYQJ/MICCAI23-ProtoContra-SFDA。