Constructing and Exploring Intermediate Domains in Mixed Domain
  Semi-supervised Medical Image Segmentation
Qinghe Ma Jian Zhang Lei Qi Qian Yu Yinghuan Shi Yang Gao
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
https://github.com/MQinghe/MiDSS .