PrixMatch: Semi-supervised Network for Multi-modal Medical Image Segmentation with Cross-modal Data Augmentation and Adaptive Prior Knowledge Thresholding

Semi-supervised medical image segmentation has made significant strides, yet most existing methods are confined to single-modality data, limiting both the volume of data and the generalizability of the models. Multi-modal data can provide richer information, expand the dataset and enhance model robustness. However, integrating multi-modal learning into semi-supervised medical image segmentation presents challenges, primarily in how to deal with the scarcity of labels and alignment across different modalities simultaneously. In this paper, we propose PrixMatch, a multi-modal semi-supervised model with a teacher-student strategy for medical image segmentation. Initially, we propose a cross-modal data augmentation strategy, which randomly exchanges image blocks of the same location between different modalities, to guide the student model to learn cross-modal consistency without the need for additional network modules. Secondly, we design a cross-modal adaptive pseudo-label threshold setting strategy, which can align the prior anatomical knowledge of different modalities, and combine the modal-aligned prior knowledge and model learning state to filter the pseudo-labels at the pixel-level, flexibly alleviating the confirmation bias that occurs during semi-supervised training. Experiments demonstrate that PrixMatch achieves a Dice Similarity Coefficient (DSC) of 87.2% on the BTCV (CT) and CHAOS (MR) multi-modal datasets with only 10% labeling ratio, bringing nearly 5.5% improvement over the latest state-of-the-art method.