Semi-URF: Progressive Uncertainty-Aware Region Filtering and Fusion for Semi-Supervised Medical Image Segmentation

Semi-supervised learning for medical image segmentation is often hindered by a conservative approach to unlabeled data, where information-rich yet uncertain regions-such as complex structures and ambiguous boundaries-are typically discarded to ensure pseudo-label quality. This "avoidance" strategy limits the model's ability to learn from the most challenging areas, which are critical for clinical accuracy. To address these limitations, we propose Semi-URF, an Uncertainty-Aware Region Filtering and Fusion framework. Instead of treating uncertainty as an obstacle to be avoided, Semi-URF progressively exploits it as a supervisory signal to enhance learning from unlabeled data. Our approach establishes a synergistic closed loop with three key innovations. First, an Uncertainty Distribution Adaptive Thresholding (UDAT) mechanism adaptively separates reliable from unreliable regions by tracking the model's evolving uncertainty distribution, maximizing the use of informative pixels from unlabeled data. Second, our Bidirectional Uncertainty-Consistent Exchange (BUCE) method facilitates learning from unreliable regions by exchanging patches between labeled and unlabeled data, subject to a semantic consistency constraint. Finally, to enhance the model's foundational capabilities for this task, a Frequency-Enhanced Feature Fusion (FEFF) module uses fast wavelet transforms and cross-attention to sharpen the perception of high-frequency boundary details. Comprehensive experiments demonstrate that Semi-URF outperforms state-of-the-art methods, especially in situations where annotated data are severely limited. By transforming uncertainty from a problem into a solution, Semi-URF significantly reduces reliance on costly expert annotations. This approach facilitates the development of more efficient and cost-effective AI models for clinical applications. The code is available at https://github.com/senseiyang/Semi-URF.