Source-Free Domain Adaptation for Medical Image Segmentation via Mutual Information Maximization and Prediction Bank

Medical image segmentation faces significant challenges due to domain shift between different clinical centers and data privacy restrictions. Current source-free domain adaptation methods for medical images suffer from three critical limitations, including unstable training caused by noisy pseudo-labels and poor handling of foreground-background imbalance where critical structures like optic cup occupy extremely small regions. Additionally, strict privacy regulations often prevent access to source domain data during adaptation. To address these limitations, this paper proposes a source-free domain adaptation approach based on mutual information optimization for fundus image segmentation. The method incorporates a teacher–student network to ensure training stability and a mutual information maximization algorithm to reduce pseudo-label noise naturally. Furthermore, a prediction bank is constructed to handle class imbalance by leveraging complete statistics. Experimental results on fundus segmentation datasets demonstrate better performance, achieving 91.74% average Dice coefficient on Drishti-GS and 87.80% on RIM-ONE-r datasets, outperforming current methods. This work provides a practical solution for cross-institutional medical image analysis while preserving data privacy, with significant potential for eye disease diagnosis and other medical applications requiring robust domain adaptation.