  Source-free test-time adaptation for medical image segmentation aims to
enhance the adaptability of segmentation models to diverse and previously
unseen test sets of the target domain, which contributes to the
generalizability and robustness of medical image segmentation models without
access to the source domain. Ensuring consistency between target edges and
paired inputs is crucial for test-time adaptation. To improve the performance
of test-time domain adaptation, we propose a multi task consistency guided
source-free test-time domain adaptation medical image segmentation method which
ensures the consistency of the local boundary predictions and the global
prototype representation. Specifically, we introduce a local boundary
consistency constraint method that explores the relationship between tissue
region segmentation and tissue boundary localization tasks. Additionally, we
propose a global feature consistency constraint toto enhance the intra-class
compactness. We conduct extensive experiments on the segmentation of benchmark
fundus images. Compared to prediction directly by the source domain model, the
segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and
Drishti GS datasets, respectively. Additionally, the results of experiments
demonstrate that our proposed method outperforms existing competitive domain
adaptation segmentation algorithms.

Computer Vision and Pattern Recognition