Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

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