An Ultrasound Signal-Guided Two-Stage Weakly Supervised Network for Intraoperative Glioma Localization and...

Surgical resection is the standard treatment for glioma. While gross tumor regions can be identified, microscopic infiltration is often elusive without histopathology. Developing real-time techniques to approximate gold-standard boundaries intraoperatively is crucial for surgical accuracy and patient outcomes.

We propose a ultrasound signal-guided two-stage spatiotemporal feature-aware weak supervision network for glioma infiltration boundaries, utilizing nude mouse pathological annotations as reference standards. In Stage 1, a spatio-temporal feature extraction module generates pseudo-boundary masks through multi-constraint learning, effectively translating the ultrasound radio frequency signal into anatomically plausible boundary probability distributions. Building upon these masks as dynamic anatomical priors, Stage 2 establishes cross-task reinforcement between tumor classification and boundary refinement in an end-to-end architecture. This cross-task synergy enhances localization accuracy with limited labels, enabling annotation-efficient and real-time intraoperative localization.

Trained on 3400 intraoperative ultrasound frames (1400 tumor/2000 normal) with frame-level signal labels, the model was evaluated on a test set comprising 680 nude mouse frames (280 tumor/400 normal) using pathological annotations. For tumor/normal frame differentiation, the model achieved an accuracy of 0.985, AUC of 0.990, sensitivity of 1.000, and specificity of 0.975. Boundary recognition yielded a Dice coefficient of 0.814, intersection over union of 0.690, Hausdorff distance of 25.088, and average surface distance of 8.359 against histopathology.

Our method enabled accurate tumor localization with infiltration boundaries and tumor sizes closely matching the pathological gold standard, outperforming preoperative MRI. This approach offers a reliable solution for intraoperative ultrasound-assisted tumor localization, laying the foundation for clinical validation.