Uncertainty-aware semi-supervised learning for neurosurgical navigation

Background/Objective: Accurate, real-time segmentation of anatomical structures during neurosurgical proced- ures can support intraoperative orientation. One of the most significant challenges in this domain is developing robust segmentation models with limited annotated data while maintaining clinical reliability. This work ad- dresses how semi-supervised learning can leverage both labeled and unlabeled data, while ensuring the dependability crucial for clinical applications where even small segmentation errors can have significant con- sequences. Methods: We present a novel uncertainty-aware semi-supervised framework for neurosurgical scene segmentation. Our approach introduces Semantic Spatial Uncertainty (SSU), a metric that quantifies prediction reliability by analyzing spatial consistency across multiple stochastic forward passes using Monte Carlo Dropout. The framework employs class-specific calibration with adaptive thresholds that continuously refine through iterative pseudo-labeling, effectively counteracting dataset imbalance. Results: Our method achieves significant improvements for clinically critical classes, with relative gains in Dice Similarity Coefficient of + 40% for tumors, + 15% for middle cerebral artery and + 14% for aneurysm. Unlike traditional uncertainty measures, SSU cap- tures uncertainty even for structures with high perimeter-to-area ratios, demonstrating strong correlation with segmentation quality (Pearson coefficient 0.85) without requiring ground truth. Our approach also outperforms intensive data augmentation (even at 200% synthetic samples) and maintains effectiveness across multiple ar- chitectures, demonstrating its architecture-agnostic advantages. Conclusion: By reframing annotation scarcity as an uncertainty quantification problem, our approach provides a practical solution for medical image segmen- tation in data-constrained environments. This segmentation framework offers potential applications beyond neurosurgery to other computer vision segmentation tasks with limited labeled data. Code is available at htt ps://github.com/nittifra/ua-ssl-neuro