  Semi-supervised medical image segmentation (SSMIS) has emerged as a promising
solution to tackle the challenges of time-consuming manual labeling in the
medical field. However, in practical scenarios, there are often domain
variations within the datasets, leading to derivative scenarios like
semi-supervised medical domain generalization (Semi-MDG) and unsupervised
medical domain adaptation (UMDA). In this paper, we aim to develop a generic
framework that masters all three tasks. We notice a critical shared challenge
across three scenarios: the explicit semantic knowledge for segmentation
performance and rich domain knowledge for generalizability exclusively exist in
the labeled set and unlabeled set respectively. Such discrepancy hinders
existing methods from effectively comprehending both types of knowledge under
semi-supervised settings. To tackle this challenge, we develop a Semantic &
Domain Knowledge Messenger (S&D Messenger) which facilitates direct knowledge
delivery between the labeled and unlabeled set, and thus allowing the model to
comprehend both of them in each individual learning flow. Equipped with our S&D
Messenger, a naive pseudo-labeling method can achieve huge improvement on six
benchmark datasets for SSMIS (+7.5%), UMDA (+5.6%), and Semi-MDG tasks
(+1.14%), compared with state-of-the-art methods designed for specific tasks.
