  Domain shift is a commonly encountered issue in medical imaging solutions,
primarily caused by variations in imaging devices and data sources. To mitigate
this problem, unsupervised domain adaptation techniques have been employed.
However, concerns regarding patient privacy and potential degradation of image
quality have led to an increased focus on source-free domain adaptation. In
this study, we address the issue of false labels in self-training based
source-free domain adaptive medical image segmentation methods. To correct
erroneous pseudo-labels, we propose a novel approach called the local-global
pseudo-label correction (LGDA) method for source-free domain adaptive medical
image segmentation. Our method consists of two components: An offline local
context-based pseudo-label correction method that utilizes local context
similarity in image space. And an online global pseudo-label correction method
based on class prototypes, which corrects erroneously predicted pseudo-labels
by considering the relative distance between pixel-wise feature vectors and
prototype vectors. We evaluate the performance of our method on three benchmark
fundus image datasets for optic disc and cup segmentation. Our method achieves
superior performance compared to the state-of-the-art approaches, even without
using of any source data.

Computer Vision and Pattern Recognition