Self-Calibrating Dual-Stream Network for Semi-Supervised 3D Medical Image Segmentation

Three-dimensional medical image segmentation plays an essential role in clinical diagnosis and treatment, yet its progress is often limited by the heavy reliance on expert annotations. Semi-supervised learning offers a way to ease this burden by making better use of unlabeled data, but most existing approaches struggle with the complexity of volumetric data, the instability of pseudo-labels, and the challenge of combining different types of information. To address these issues, we introduce a Self-Calibrating Dual-Stream Semi-Supervised Segmentation framework. The model incorporates a structural branch built on a 3D convolutional architecture to capture fine anatomical details, alongside a contextual branch based on a lightweight transformer to gather broader spatial cues. An uncertainty-aware refinement module is employed to improve pseudo-label reliability, and a feature integration mechanism adaptively merges information from both branches to maintain a balance between local accuracy and global consistency. Experiments on multiple public datasets show that the proposed method achieves strong segmentation performance under limited supervision and provides more precise boundary localization. Component analyses further verify the importance of each module, and expert feedback highlights its potential value in clinical practice.