A WEb-Accessible comprehensiVE (WEAVE) Platform for Automatic Vestibular Schwannoma Segmentation and Longitudinal Volumetric Tracking

Background Vestibular schwannomas (VS) require long-term tracking for treatment decisions and outcome assessment. This study aims to develop a WEb-Accessible comprehensiVE (WEAVE) platform, that combines AI-driven segmentation with a user-friendly interface to enable longitudinal volume tracking for disease assessment, planning, and monitoring. Methods WEAVE was built using nnU-Net as the backbone for auto-segmentation, with three models trained and validated on combinations of various image modalities. Auto-segmentation performance was evaluated with multiple metrics including absolute and relative volume differences (AVD/RVD), Dice score, mean surface-to-surface distance (MSSD), and 95th percentile Hausdorff distance (HD95). The platform features a central database with DICOM-RT import/export capabilities, and its interface is built using Rust and WebAssembly. Results Three models demonstrated comparable performance without significant differences with mean Dice scores, AVD and RVD ranged from (0.89–0.90), (0.11-0.13cc) and (10.70–13.44%), respectively. MSSD and HD95 values were consistently low (0.14-0.19mm) and (0.74-0.88mm) respectively. Average inference time was ∼60 seconds per case. The platform successfully enabled longitudinal tumor volume tracking and provided flexible visualization options including single & multiple image views and graphical representation of volume changes over time. Conclusions WEAVE is a comprehensive platform that combines automated segmentation with longitudinal tracking to support VS management. The AI models achieved Dice scores comparable to interobserver variability in manual contouring, indicating clinical adequacy. The tracking capability provides consistency in treatment planning and monitoring and opens the possibility to advance AI-driven segmentation and streamline workflows for other intracranial pathologies.