A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation

The limited data annotations have made semi-supervised learning (SSL) increasingly popular in medical image analysis. However, the use of pseudo labels in SSL degrades the performance of decoders that heavily rely on high-accuracy annotations. This issue is particularly pronounced in class-imbalanced multi-organ segmentation tasks, where small organs may be under-segmented or even ignored. In this paper, we propose a semantic knowledge complementarity based decoupling framework for accurate multi-organ segmentation in class-imbalanced CT images. The framework decouples the data flow based on the responsibilities of the encoder and decoder during model training to make the model effectively learn semantic features, while mitigating the negative impact of unlabeled data on the semantic segmentation task. Then, we design a semantic knowledge complementarity module that adopt labeled data to guide the generation of pseudo labels and enriches the semantic features of labeled data with unlabeled data, which improves the quality of generated pseudo labels and the robustness of the overall model. Furthermore, we also design an auxiliary balanced segmentation head based training strategy to further enhance the segmentation performance of small organs. Extensive experiments on the Synapse and AMOS datasets show that our method significantly outperforms existing state-of-the-art methods.