TriggerMatch: Conditional Semantic Acquisition and Maintenance for Semi-Supervised 3D Medical Image Segmentation

Conventional semi-supervised multi-class semantic segmentation (SSS) treats the role of unlabeled data as identical to that of labeled data: both are expected to increase the model's semantic understanding. We recast SSS as semantic acquisition and semantic maintenance, two responsibilities that are assumed precisely by learning from labeled data and learning from unlabeled data, respectively. Based on this perspective, we introduce TriggerMatch, which partially masks the training process of semantic acquisition, so that acquisition no longer duplicates what maintenance already preserves, while the maintenance process itself receives richer gradient diversity. Meanwhile, learning from labeled data is supervised by a correctness metric computed against ground truth, and this metric governs the acquisition and maintenance states of the corresponding semantic classes. Furthermore, we conduct extensive ablation and parameter studies to verify the effectiveness of our design, and benchmark TriggerMatch on three standard 3D medical image datasets: AMOS2022, FLARE2022, and Synapse, where it achieves state-of-the-art performance.