UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation

Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In turn, the already available annotated datasets, such as well-known BraTS, are difficult to generalize to multicenter unannotated datasets due to the variations in imaging machines and parameters. To address this challenge, a novel unsupervised domain adaptation framework for glioma segmentation (UDA-GS) is proposed. UDA-GS uses GliomaMix to mix labeled tumors with unlabeled images and aligns the features of the same tumor, allowing the network to adapt to different backgrounds across different centers. Additionally, the framework leverages tumor information generated by GliomaMix as prior knowledge for self-supervised regression tasks to enhance feature encoding for tumors in different domains. Using Mean-Teacher as the basic framework, UDA-GS also incorporates weighted consistency regularization and mask combining strategy to achieve efficient unsupervised domain adaptation. Quantitative and qualitative evaluations were conducted on 1179 cases across 27 centers, without requiring any local annotations. The results demonstrate that UDA-GS outperforms the second-best method in terms of Dice coefficient segmentation metrics by 18.2 %, 6.9 %, and 4.6 % for the whole tumor, tumor core, and enhanced tumor, respectively, on the internal testing set. Additionally, the evaluations reveal that doctors express greater satisfaction with the segmentation outcomes achieved by UDA-GS in comparison to other methods including the segment anything model (SAM).