  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.
