Both limited annotation and domain shift are prevalent challenges in medical
image segmentation. Traditional semi-supervised segmentation and unsupervised
domain adaptation methods address one of these issues separately. However, the
coexistence of limited annotation and domain shift is quite common, which
motivates us to introduce a novel and challenging scenario: Mixed Domain
Semi-supervised medical image Segmentation (MiDSS). In this scenario, we handle
data from multiple medical centers, with limited annotations available for a
single domain and a large amount of unlabeled data from multiple domains. We
found that the key to solving the problem lies in how to generate reliable
pseudo labels for the unlabeled data in the presence of domain shift with
labeled data. To tackle this issue, we employ Unified Copy-Paste (UCP) between
images to construct intermediate domains, facilitating the knowledge transfer
from the domain of labeled data to the domains of unlabeled data. To fully
utilize the information within the intermediate domain, we propose a symmetric
Guidance training strategy (SymGD), which additionally offers direct guidance
to unlabeled data by merging pseudo labels from intermediate samples.
Subsequently, we introduce a Training Process aware Random Amplitude MixUp
(TP-RAM) to progressively incorporate style-transition components into
intermediate samples. Compared with existing state-of-the-art approaches, our
method achieves a notable 13.57% improvement in Dice score on Prostate dataset,
as demonstrated on three public datasets. Our code is available at
this https URL .

医学图像分割中存在有限标注和领域转移的问题，传统的半监督分割和无监督领域自适应方法只解决了其中一个问题，本文提出了一种新的具有挑战性的情景：混合领域半监督医学图像分割（MiDSS），并通过引入统一的复制 - 粘贴（UCP）和对称引导训练策略（SymGD）解决问题，进一步使用训练过程感知的随机幅值混合（TP-RAM）方法以获得更好的分割结果。